students-performance-in-exams-prtogal-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "69cd34d2",
   "metadata": {},
   "source": [
    "# Import important libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3d62fa47",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, roc_curve, accuracy_score, f1_score, roc_auc_score, classification_report\n",
    "from astropy.table import Table\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6996333e",
   "metadata": {},
   "source": [
    "# Downloading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b961a241",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>teacher</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>R</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>services</td>\n",
       "      <td>health</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>services</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</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>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>A</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>services</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>5</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>teacher</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30 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",
       "5      GP   M   16       U     LE3       T     4     3  services     other   \n",
       "6      GP   M   16       U     LE3       T     2     2     other     other   \n",
       "7      GP   F   17       U     GT3       A     4     4     other   teacher   \n",
       "8      GP   M   15       U     LE3       A     3     2  services     other   \n",
       "9      GP   M   15       U     GT3       T     3     4     other     other   \n",
       "10     GP   F   15       U     GT3       T     4     4   teacher    health   \n",
       "11     GP   F   15       U     GT3       T     2     1  services     other   \n",
       "12     GP   M   15       U     LE3       T     4     4    health  services   \n",
       "13     GP   M   15       U     GT3       T     4     3   teacher     other   \n",
       "14     GP   M   15       U     GT3       A     2     2     other     other   \n",
       "15     GP   F   16       U     GT3       T     4     4    health     other   \n",
       "16     GP   F   16       U     GT3       T     4     4  services  services   \n",
       "17     GP   F   16       U     GT3       T     3     3     other     other   \n",
       "18     GP   M   17       U     GT3       T     3     2  services  services   \n",
       "19     GP   M   16       U     LE3       T     4     3    health     other   \n",
       "20     GP   M   15       U     GT3       T     4     3   teacher     other   \n",
       "21     GP   M   15       U     GT3       T     4     4    health    health   \n",
       "22     GP   M   16       U     LE3       T     4     2   teacher     other   \n",
       "23     GP   M   16       U     LE3       T     2     2     other     other   \n",
       "24     GP   F   15       R     GT3       T     2     4  services    health   \n",
       "25     GP   F   16       U     GT3       T     2     2  services  services   \n",
       "26     GP   M   15       U     GT3       T     2     2     other     other   \n",
       "27     GP   M   15       U     GT3       T     4     2    health  services   \n",
       "28     GP   M   16       U     LE3       A     3     4  services     other   \n",
       "29     GP   M   16       U     GT3       T     4     4   teacher   teacher   \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",
       "5   ...      5        4      2     1     2      5        6  12  12  13  \n",
       "6   ...      4        4      4     1     1      3        0  13  12  13  \n",
       "7   ...      4        1      4     1     1      1        2  10  13  13  \n",
       "8   ...      4        2      2     1     1      1        0  15  16  17  \n",
       "9   ...      5        5      1     1     1      5        0  12  12  13  \n",
       "10  ...      3        3      3     1     2      2        2  14  14  14  \n",
       "11  ...      5        2      2     1     1      4        0  10  12  13  \n",
       "12  ...      4        3      3     1     3      5        0  12  13  12  \n",
       "13  ...      5        4      3     1     2      3        0  12  12  13  \n",
       "14  ...      4        5      2     1     1      3        0  14  14  15  \n",
       "15  ...      4        4      4     1     2      2        6  17  17  17  \n",
       "16  ...      3        2      3     1     2      2       10  13  13  14  \n",
       "17  ...      5        3      2     1     1      4        2  13  14  14  \n",
       "18  ...      5        5      5     2     4      5        2   8   8   7  \n",
       "19  ...      3        1      3     1     3      5        6  12  12  12  \n",
       "20  ...      4        4      1     1     1      1        0  12  13  14  \n",
       "21  ...      5        4      2     1     1      5        0  11  12  12  \n",
       "22  ...      4        5      1     1     3      5        0  12  13  14  \n",
       "23  ...      5        4      4     2     4      5        2  10  10  10  \n",
       "24  ...      4        3      2     1     1      5        2  10  11  10  \n",
       "25  ...      1        2      2     1     3      5        6  10  11  12  \n",
       "26  ...      4        2      2     1     2      5        8  11  12  12  \n",
       "27  ...      2        2      4     2     4      1        0  11  11  11  \n",
       "28  ...      5        3      3     1     1      5        2  12  12  13  \n",
       "29  ...      4        4      5     5     5      5        4  12  11  12  \n",
       "\n",
       "[30 rows x 33 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std = pd.read_csv(\"student-por.csv.xls\")\n",
    "std.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "20f7eb14",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 649 entries, 0 to 648\n",
      "Data columns (total 33 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   school      649 non-null    object\n",
      " 1   sex         649 non-null    object\n",
      " 2   age         649 non-null    int64 \n",
      " 3   address     649 non-null    object\n",
      " 4   famsize     649 non-null    object\n",
      " 5   Pstatus     649 non-null    object\n",
      " 6   Medu        649 non-null    int64 \n",
      " 7   Fedu        649 non-null    int64 \n",
      " 8   Mjob        649 non-null    object\n",
      " 9   Fjob        649 non-null    object\n",
      " 10  reason      649 non-null    object\n",
      " 11  guardian    649 non-null    object\n",
      " 12  traveltime  649 non-null    int64 \n",
      " 13  studytime   649 non-null    int64 \n",
      " 14  failures    649 non-null    int64 \n",
      " 15  schoolsup   649 non-null    object\n",
      " 16  famsup      649 non-null    object\n",
      " 17  paid        649 non-null    object\n",
      " 18  activities  649 non-null    object\n",
      " 19  nursery     649 non-null    object\n",
      " 20  higher      649 non-null    object\n",
      " 21  internet    649 non-null    object\n",
      " 22  romantic    649 non-null    object\n",
      " 23  famrel      649 non-null    int64 \n",
      " 24  freetime    649 non-null    int64 \n",
      " 25  goout       649 non-null    int64 \n",
      " 26  Dalc        649 non-null    int64 \n",
      " 27  Walc        649 non-null    int64 \n",
      " 28  health      649 non-null    int64 \n",
      " 29  absences    649 non-null    int64 \n",
      " 30  G1          649 non-null    int64 \n",
      " 31  G2          649 non-null    int64 \n",
      " 32  G3          649 non-null    int64 \n",
      "dtypes: int64(16), object(17)\n",
      "memory usage: 167.4+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'age'}>,\n",
       "        <AxesSubplot:title={'center':'Medu'}>,\n",
       "        <AxesSubplot:title={'center':'Fedu'}>,\n",
       "        <AxesSubplot:title={'center':'traveltime'}>],\n",
       "       [<AxesSubplot:title={'center':'studytime'}>,\n",
       "        <AxesSubplot:title={'center':'failures'}>,\n",
       "        <AxesSubplot:title={'center':'famrel'}>,\n",
       "        <AxesSubplot:title={'center':'freetime'}>],\n",
       "       [<AxesSubplot:title={'center':'goout'}>,\n",
       "        <AxesSubplot:title={'center':'Dalc'}>,\n",
       "        <AxesSubplot:title={'center':'Walc'}>,\n",
       "        <AxesSubplot:title={'center':'health'}>],\n",
       "       [<AxesSubplot:title={'center':'absences'}>,\n",
       "        <AxesSubplot:title={'center':'G1'}>,\n",
       "        <AxesSubplot:title={'center':'G2'}>,\n",
       "        <AxesSubplot:title={'center':'G3'}>]], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u1w58OrHfBcBNeef/K8EJ/Wtue2AX4G/AToltxwGvES7q28D6eK4Vcf11wHfj74mEV9FFwKuEeWiPAA4BniNkuG8kjt0LmAa8EG2ZAexYS33zNe5kmz/E//52/P9fA+YCq+L/vSCx7XDAgD5xuQ04uci1KLTtRcBs4C1gPrAHcE/U71ngmMT+hxBKgKtj3vjnrOXhUjWugt5fjuveBE4D9iU46RXAjxLbT4n6Xh41fYVQaPo+Yb7kZcBVwDbJfJ2GrtXUuAwtbwF+CtwBrCHc+4OBXxPu9YXAmbnrRif3adTL4nE7gI9GfR9InN+AfwSej/n0O8AHgAfj9ZwBbJnY/jBgXrxu/wf8XTXzVvKTVqPtYmDXxPJQQiYrGUkfAs4A9jWz/sBBwF+Afwd+aWb9zOzDJRxnNOFiH0+4yDtFezCzpQSHc0xily8B081sPuEmejCea4cip9iFkEGGAOcD18RjjAM+DpwvaWTc9kzCA+ET0ZY3CXWY5VKxvl1hZp8E/gScYWb9gMeBE4AdCA/er0o6okqnO55QgutPmJj9HsJNOhA4DviJpD3jtj8HTo15Yi/CzZ0GqWucpJt67w/sBnwBuIJQmPk0sCdwjKRP5G37IkHTJcD3CKXgscAHeS//1pJUNC6g5bvAFwkFi/4Ep/o7gsZDCJOsnyXpoHiIzu7TA+L3DtEvPFjEjEkEH/AR4F+Aq4HJhP+7FyFfI2kf4FrgVIJv+i9gpqStKhKhCGk5/D8Du0kaIWlL4FhgZpnHWE8ohYyWtIWZtZvZC92w5WjgNjO738zeAb4FbEisv57goHONSMcBN5Zx/LXAEjNbC0wnvG5daWarzewp4Cng7+K2pwL/amaLoy0XAEdLKrctpRr6loWZtZnZfDPbYGZPALcSbohqcJ2ZPWVm64DtgHYz+4WZrTOzxwglsaPjtmsJeWI7M3szrk+DmmucpES9v2Nmb5vZ3YSS661m9qqZvUxweHsntn3FzH4YNTbgK8A/mdkbZraaUJA6NvU/tim11Pi3ZjbbzDYQ3t53NrNvm9m7FtoQruG9/1+N+/R7ZrYq+oAngbvN7EUzWwncyXvX5ivAf5nZw2a23syuB94hPCiqTioOP2aqM4C7gGeAGfGPl8LV8RgLgLMIYr8qabqkwd0wZzDhtTdn2xrCa1qO3xIcyEhCtMBKM3ukjOO/nrOZUCUB4RWZRFq/+HsY8D+SVkhaQdBmPTCojPNVqi8Je0tG0v6SZkl6TdJKwtvPgHKPU4RFid9zgf1zGkWdJhPepABaCdU6f5X0R0kfrZINm1APjZOUqHd+PiuW72BTjW8FtgXmJDT+PVB6XXAVqILG5ZD8/8OAwXl57BuE+/BqqnOflnpthgHn5NmyK8FvFaPbeSu1oRXM7A5CnVm5+12d+H0LcIuk7QivOt8DFhTYbQ0hA+fYJfF7CTAqtyBpW8KrU+4cb0uaQXAqe7Bp6b6kFu2kzV2wCDjJzGaXuH1n5+yWvnHf7mSYW4AfAQdHza6gNIff2bXZaFLi96+B7c3swEIHM7M/A4dL2oLgLGawabVA1aiDxkm6q3dRkxK/LyfUK+8Z3wbqRiUal3uqxO9FwEIz263QhpLOoch9KmlYle1aBFxkZheVukMleSuzPW0lfUjSJ2Nd1tuEp+J6wpNyuKSk7fOAYyVtIWk8773+A/w3cJikCfG18dts/r9vIDS8fA64KZG+DBga96sGVwEX5TKNpJ0lHV6lY6dNf+CN6Hz2I9SJlsI84ABJ75e0PXBeF9vfBuwu6fh4PbeQtK+kUZK2lDRZ0vaxCm0VIU/0RLqrd5fEao1rgMslDQSQNCRRh93TeQRYJelcSdtI6i1pL0n7xvWd3aevEaqER25+2G5xDXBafKOTpL6SDpXUv0rH34TMOnxC/f3FhPCjpYTGpm8Av4rrX5eUq7/9FqEV/E3gQkLpCID4inh6TFsSt1mcPFF8km8AHjOz9sSqPxDq4JdKqkao3JWEOsq7Ja0GHiI0pjUC/wh8O9p9PqFk3SVmdg/wS0L0yByCQ+9s+9XAZwj1qa8Qrv33CPkBQgNvu6RVhGqOL5X9TxqDbuldBucS3pYfilreSwh57PGY2Xrgs4QG64UEH/MzYPu4SdH71Mz+Rowqi1UwFdW1m9mjhHr8HxF80wJC4TMduhveU40PoXX6VeDJRNoFhHC7efFzSI1s+QMxlLCTbXYFZhHq9J4CvhbTdyREljwfv99XT10T9k4ihDUuAKYVWC/gB3H9E8A+NbKroI5520wEVibywfn11tM17pmfUrTKygfoTWjnuq1b+9fZ+AOAfQo4/FRiqzuxY19CDGz/LrZryd2whFfu54DRwCW5m50Qv/u9jGSMFwivnlsSQtBG521zCCFiQISogIdrZFtBHfO2mdjdTO0a9xyNs6JVVj7A2YTaim5dt7pW6ZjZ/YSOIHVD0vWE19mzLFQnFMXMllgMA4zbPkOI4z2cEN5J/D4iNYNLZ2O3dTN7lxAymt9ecDhwgwUeAnaQ1JK2YZ3o2Gi4xj2ARtFK0lBCn4yfdfcYWa3DP0PSE5KulfS+NE9kZiea2fZmdl05+0kaToilfRgYZGZL4vGWENob6s0QNg1FW8zmmbiUbVIlT8d8PirpcUl3JjpeZQnXuIfRhVb15gpCJ64NXWxXlCw6/J8SGmDHEhpZL62rNQWQ1I8QPniWma2qtz1FUIG0/DDTUrZJjS50fIwwTsiHgR8C/1sru8rANe5BZPm+lnQY8KqZVTSGTyqDp5VlgDR8p512Wjh8+PDUzrFmzRr69u3+kKK1ImnnnDlzlls5gyLlETskXWBmBwEMGDDAcho3oh7VplJ9oXON0yTL16+aebgQrnGFGmegEWL4uHHjLE1mzZqV6vGrRdJOKhggKexOH8LYKSOALZMaN6Ie1aZSfa0LjdMky9evmnm40Mc1rkzjuk5iLulWQqRAKuTGcM+foCQrEwqniZmtk5Trtt47ua47k3M4m9OZxo1G1iffzhqNqle9o3SOM7PUIxYageHDh3PSSScxduxYxo8fD4CkHSXdI+n5+L2xAVvSeQrTwj1brIekmd1hZrub2Qdq9DeaDtfYaSSy2GjbtFx++eXMmzePRx99NJc0DbjPwpgf98Xl3JDPxxKGwZ1EGD64oUuYjuOkjzv8bFMsvv9wwpj975jZQkIvzv1qb57jOI1EXevwnfeQxNe//nW+/e1vc+qpp+aSN4nvzw10RYjjfiixe8HYbiWmhhs0aBBtbW3hoNuEdo18cuuzQkdHR+ZscpxGxh1+Rpg9ezbPPfcco0eP5sADD4RNxzLPp6TYbgvDqF4NMH78eJs4cSIAP7z5t1w6f/NL3z55Ytl2p0lbWxs5mx3HqRyv0skIgweH+Q4GDhzIkUceCdAXWJbrhh+/X42b13T6PcfpikWLFvEP//APnHjiiey5555ceeWVQOWBB0518RJ+BlizZg0bNmzY+Pvuu++GMP7/TOBEwjDRJxJm5yKm3yLpMsLMOLsRxvh2nLrQp08fLr30UlatWsW4ceMYN24cwNa8F3hwsaRpcfncvMCDwcC9kna3MHSxkxJews8Ay5YtY8KECUydOpX99tuPQw89FMLkHhcDB0p6njD94sWwcYz/GcDThKnpTvcbxaknLS0t7LPPPgD079+fUaNGQRhB1AMPMoSX8DPAyJEjefzxxzeps/7mN7+Jmb0OfKrQPhamRCt5WjTHqRXt7e3MnTsXoAMYWUngARQPPkiTrgIGCgU9QG0CHyoJZnCH7zhO1XjrrbdobW3liiuuoLW1tbNRHUseVK5Y8EGadBUwUKinOtQm8KGSYAav0nEcpyqsXbuW888/n8mTJ3PUUUflkj3wIEN0WcKXtCthku9dCOMwX21mV0rakTBX6XCgHTjGzN6M+5wHTCVMMH2mmd2VivWO42QCM2Pq1KkMGzaMs88+O7nKAw8oPvYO1Hb8nVKqdNYB55jZY3Em9TmS7iFMtFv11vesCOP0LBYtWsQJJ5zA0qVL6dWrF8RJarzgUh1mz57NjTfeyMiRIxk7dmwueXuCo58haSrwEvB5CIEHknKBB+vwwIOa0KXDjw0uuUaX1ZKS0/pNjJtdD7QB55JofQcWSsq1vj9YbeMdp1RyYYP77LMPq1evZrvtthsYCydT8LDBipkwYQJmtkn9sqSVHniQLcpqtO1sWr9qdfsv1voN5beA546VP5RAVrvr+1AC6dHS0kJLSxiYtX///hD6OXjBxWkqSnb4+dN/SYUa2cOmBdJK7vZfrPUbym8Bn5IYDz85lEDWhhDI4UMJ1Ib29naAbUmx4JImaRQMqhVm6IWWbFOSw5e0BcHZ32xmv4nJyyS1xJvEW9+dhqCjo4PW1laARWkWXNIkjYJBtcIMvdCSbUqJ0hHwc+AZM7ssscpb352GYu3atbS2tjJ58mQee+yxFTHZCy7doFhwxXWTsjkPrBMoJQ7/Y8DxwCclzYufQ/Bu/04DkQsbHDVqVLGwQdi84HKspK0kjcALLk4PoJQonQco/HoL3vruNAi5sMExY8bkwgZHJwouHjboNAU+tILTFOTCBnNIetrM7oiLXnBxmgIfWsFxHKdJcIfvOI7TJHiVjuM4TYEP2+IlfMdxnKbBHb7jOE6T4A7fcRynSXCH7ziO0yR4o22JFGvwaZbGHqdx8LzqFMNL+I7jOE2Cl/Adx3EySBoD1HkJ33Ecp0lwh+84jtMkuMN3HMdpErwO33GchsSjkcrHS/iO4zhNgpfwnYrwUpbjNA7u8GtMZyP2+XygjuOkSWoOX9Ik4EqgN/AzM7s4rXM1I65v+lSicXfefJrxbcnzcW1JpQ5fUm/gx8DBwGjgOEmj0zhXM+L6po9rnD6uce1Jq4S/H7DAzF4EkDQdOJwwIbRTOT1C3/b2dkaMGMHatWvp06fPZiXcc8asY8q028su4VappNwjNAZ4+6UnWP67S+Hi5fU2JZ+aa7z4pyex08Fnss3wsVU7Zsf8e+l4/G52+dIlBde3tbWx+McnMvT066t2zu6i5MTOVTuodDQwycxOjsvHA/ub2RmJbU4BTomLHwKerboh7zEASCu3bwmMAeZU4VhJO4eZ2c6FNipF35heTONq6jEG2AKw+HkbeB14rYR9u9IuzetWVF+oisbVYBegH7AgkbYXsIFNneJewMvAm0WO0x8YATxRRduKUVIehrppPAZoB1Z3sV05eW+nuH3OrnHAk8A7cbna+pescT5plfBVIG2TJ4uZXQ1cndL5NzVGetTMxqd07OHAQuAjZrauwmOVameX+kJxjauph6R24GQzu1fS9sAnCHWy883sy13sO5xOtEvzupVARRpXxQDpY8AdBCe4XtIuwEPAkLy0JcDHzeyVIseZCNxUCy3LvGY11zjm19PN7N4utiv5f0iaQrgHJsRlA440swVxeSJV1L+S+yKtOPzFwK6J5aFAwcxYbSTtI2mupNWSfiXpl8DguO4rkhZIekPSTEmDE/v9vaQ/S1oZv/8+sa5d0qcTyxdIuiku3h+/V0jqkPTRGvzNuunbGWa20sxmAl8ATpS0l6RD4/VYJWmRpAuK7S9pR0m/kPSKpDeBD9TK9gJkQeM/E96exsblA4BZhLeoZNoLwEGSnon5/kVJpxY7qKRdJf1G0muSXpf0o9T+QefUS+Oxkp6I9/ovJW0NIOkwSfMkrQD2kPR3uR0kTZP0QtT3aUlHFjqwpJw/eDz6gy8k1p0j6VVJSyR1WhhKi7Qc/p+B3SSNkLQlcCwwM6VzbSSe63+A64AdgVuBI+O6TwL/ARwDtAB/BabHdTsCtwM/ILyeXQbcLmmnEk57QPzewcz6mdmD1fo/nVAXfUvFzB4h3MwfB9YAJwA7AIcCX5V0RJFdbwS2BfYEBgLL0ra1E+qusZm9CzzMe3nsAOBPQEde2v3Aq8BhwHbAl4HLJe2Tf8zYUHobIf8PJ7wtTE/tT3ROvTQ+BphEqGb5O2BK1Opa4FSCD3gNmClpq7jPC4T8vD1wIXCTpJb8A5tZ7rp8OPqDX8blXeK+Q4CpwI8lvS+NP9cZqTj8+Hp+BnAX8Awww8yeSuNceXyEUE31AzNba2a/AR4B5gKTgWvN7DEzewc4D/horFY4FHjezG40s3VmdivwF+CzNbA5SUmvrVXQtxZVaa8AO5pZm5nNN7MNZvYE4SH8ifyN481zMHCamb1pZmsJD966UMc8nM8fec+5f5zg8Gfkpf3RzG43sxcs8Efg7rgun/0Ib7xfN7M1Zva2mT1QRXtLzlt11PgHZvaKmb0B/I7wtvQV4L/M7GEzW08oHL5D8CmY2a/iPhuiE3+eoGWprAW+Hf3SHYSH9oe6aX+379/U4vDjn7ojreMXYTDwsm3aEr2I8HQeBzyWSzSzDkmvE564gwklniR/jetqRqyrLHXbbutbznkqYAjwhqT9gYsJDYtbAlsBvyqw/a7AG2a2seGxRnYWpU55OJ/7gdNjaXBnM3te0reA52PaXsD9kg4G/g3YnVCQ2xaYX+B4uwJ/rbS9qRjlXrM6abw08ftvhPt/R0I15P9LrNuS96qDTwDOJrwVQWhMH1DGOV/P0/xv8RhlU8l90dPG0lkCDJGUbAzK1RG+AgzLJUrqS3h1ezl/XeT9cR2EaoltE+t2SfyufphTgyNpX4LDfwC4hfCavquZbQ9cReHGukXAjpJ2qJWdDcKDhKqAU4DZAGa2ipBnT4nfrwC/Br4PDDKzHQhOtJjO75fkvew3ZRFwkZntkPhsa2a3ShoGXEN4G9kp6vskhfXNND3N4T8IrAfOkNRH0uG899p1C/BlSWNjvdy/Aw+bWTvh5thd0hfjfl8gdAS5Le47DzhW0haSxgNHJ875GiFMbmTK/y3zSNpO0mGEOuGbzGw+ISTtDTN7W9J+wBcL7WtmS4A7gZ9Iel/U+oBC2zYTZvYW8CihdPmnxKoHYtr9vPfm9BqwLpb2P1PkkI8QCkYXS+oraWuFaKBm5xrgNEn7K9A3Bhz0B/oSCnavAcQG1706OdYyMuoPepTDj41cRxEaRVYQ6uVWEhpifgD8gVASWkKIADk27vc6ocHrHEIM+b8Ah5lZLtb1W3H7NwkNNrckzvk34CJgtqQVkj7SHdsl9Y7RLLd1vXX3kTRJ0rMK0UrTqnTY30laTSgl/Suh7j0XhfCPwLfj+vMJ9c/FOJ4QlbKMEIlym6SvVcnGhiJG0syS9AywG6ERO1nX/qeYdlT8vYQQePAm4aFasPEz1k9/Fvgg8BKhcf0Lhbbtwr52SfNjVMujBdZL0g9iPnuiUANyvZG0K3AiQY/rCQW8HxE0XABMASYQCpIrgKeAVYRY/tmdHPoC4ProD46pwL7qa2xmPfZDiMaZT3A+/YHngNH1tquIrWcTHiS3pXiO3oT2jJGEUuHjWdMjXrN94u9MX7N66wBMTDO/dGFfOzCgk/WHEN7YRGj4fLjemrrG1rNK+ACSPiFpl1hH+RlCSeb3ZraaEAlQ04bYUpA0lBAp9LOUT7WxK7uFt6FcV/bMYGZLzOyx+Duz1yxteoAOhwM3WOAhYIdCYYz1pBk17nEOnxDq9DihKucc4GgzWxLDL/cmxDVnjSsI1UgbUj7PEEK1S47FZDiDZ/ya1YwudPiopMcl3SlpzxqaZcDdkuYoDH2QT0/Kaz1G4x7XUm8FumFL6keouz/LQoRDZoiNnK+a2RyFLtipnq5AWiajjLJ8zWpJFzo8RhhLpUPSIcD/Eur7a8HHzOwVSQOBeyT9xczuT6zvKXmtR2ncE0v4myBpC8LFvNlCR6ys8THgcwpjfEwHPqn3hm2oNlkYLqBLGuCa1YSudDCzVWbWEX/fAWwhqZzY8G5jcdweM3uV0Ls9vxNSj8hrPU3jLkfLjC3ZNxBizzcAV5vZlQrDEfyS0BGhHTjGYqcZSecRImXWA2ea2V2dnWPAgAE2fPjwTu2oFmvWrKFv32zOLJW0bc6cOcutjFHwusI1TldfqJ3GWdUXNtN4LSFcdH9C79ZyeqYWxDWuUOPutmQDlwDTYvo04Hvx92hCHfpWhLEqXgB6d3aOcePGWa2YNWtWzc5VLknbgEetii3+rnG6+loNNc6qvmabafxqvP/nA+PNNa4KlWjcZR2+hQ4xS+Lv1TEueAihhXhi3Ox6oA04N6ZPtzBezUJJCwivIrUYVKwiOptvtidNM9eMU+nVGtcYgJesfsNbNzzF8lDe3NdlaVxWo21eS/ag+DDAQhTMwLjZEMKY3TkKthwrManBoEGDaGtrK8eUbtPR0VH0XOeMKT68SC3s68w2x3GcSinZ4ee3ZEtFh5Eoe1KD8ePH28SJE0s1pSLa2toodq4pnZXwJxfep5p0ZpvjOE6llBSlU6Qle1kuyD9+vxrTG6J13nEcp9no0uErFOV/DjxjZsnxyWcSxqEgfv82kX6spK0kjSDErD5SPZMdx3Gc7lBKlc7HCINazZc0L6Z9gzDG+QxJUwmDMH0ewMyekjSDMMnyOsL8keurbbjjlMOiRYv4p3/6J95++2169eoFYeAxqhle3MwsWrSIE044gRdffJF+/fpxyimhY6jrmy1KidJ5gOLjPn+qyD4XEUaQdJxM0KdPH7761a9yyimnsHr1arbbbruBkkYTRkS8z8wujqOHTgPOjeuOJUy3OBi4V9LuXngpTJ8+fbj00ktZtWoV48aNY9y4cQBbE/R0fTNCj+9p6zgALS0t7L777gD0798f4C3eCy++Pm52PXBE/L0xvNjMFhKGy62441BPpaWlhX32CaPz9u/fn1GjRkEYkdX1zRA9biwdx+mK9vZ2CDOYpRZeXCzEtxpht1kO3+3o6GD69Ok89NBDEOZtHVmJvlCfEO4saFwsD1Vimzt8p6no6OigtbUVYFGa4cXFQnyrEd6b5fDdO++8k//8z//kpz/9Ka2trZ2N/lrywF/1COHOgsbF8tB1k/p22zav0nGahnXr1tHa2srkyZMhzGAEHl5cNdauXcv555/P5MmTOeqoo3LJrm+GcIfvNAVmxiWXXMKoUaM4++yzk6s8vLgKmBlTp05l2LBhrm+G6RFVOj5uidMVs2fP5p577mHp0qWMHTsWYHQc39zDi6vA7NmzufHGGxk5cmROX4DtcX0zRY9w+PXEHzaNwYQJE5g1a9bGuk9JT1sY3xw8vLhiJkyYgJltUvctaaWZvY7rmxm8SsdxHKdJcIfvOI7TJHiVTgbopFv6BcBXgNfipt/IVUN4t/Seh1cPOmnjDj8DdNItHeByM/t+cnvvlu44TnfwKp0M0Em39GJ4t3THccrGS/g1prNpFK+b1Jf29nbmzp0LoVs6wBmSTgAeBc6JIw1mtts/ZKNbeiGyapfj1Ap3+BnirbfeorW1lSuuuCLXLf2nwHcIXc6/A1wKnESGu/1DNrqlFyKrdjlOrXCHnxFs/TrOP/+7m3RLN7NlufWSrgFui4veLd1x6kijNrC7w88AZsbrd17Jvnts2i1dUktupEHgSODJ+HsmcIukywiNtt4t3dlIozojJ33c4WeAd15+mjVPzWLuW5t1S79E0lhCdU07cCp4t3THcbqHO/wMsPXQPRl27m38PDHsaeyWfnyxfbxbuuM0Dp0Fa9TyzcvDMh3HcZoEd/iO4zhNglfpOI7TkA29jWhzvfESvuM4TpPgJXzHcZqCrDSc1hMv4TuO4zQJXsJ3SsLrSx2n8cmcw/fXLsdxnHTwKh3HcZwmIXMlfMdxsk+xN/HrJvWtsSVOOXgJ33Ecp0lwh+84jtMkuMN3HMdpErwO36kpHoXlOPUjtRK+pEmSnpW0QNK0tM7TrLi+6eMap49rXFtScfiSegM/Bg4GRgPHSRpd7fNcd911TJgwodqHzTy10reZcY3TxzWuPWlV6ewHLDCzFwEkTQcOJ8zQ5FROQ+g7ffp0Lr/8cp588kn69u3LiBEjWL39OPrtfQjvvDSfFf93K+8ufYFeW/dj6FevLXqccnv5VilkMPMaF9L3xBNPxOz9rHrkN6x58j7WrXqNXttsR/+9DwEyV2XW0BqvfnQmq+fMZP1bq+i15TZsu8fHWbfuIPr0yW5Nucys+geVjgYmmdnJcfl4YH8zOyOxzSnAKXHxQ8Cz3TjVTsCAMvcdACzvxrlqQdK2YWa2c6GNStE3pldD4+4wAOgN7AK8BKwENgDbxLT2+HtrwltmCzC/RnZ1qS9kXuNS9B0IrAb+BmwF7A4sBt6sgW3NovGWhClG18dtPxC3W1YD20rSeDPMrOof4PPAzxLLxwM/rOB404AXCBn4aeDImD4FmA38kCD0X4BPJfabArwY91sITAYejetOAp4h3AB3ReFy+xlwGvB8XP9j4sMxrv9K3Ddnzz4xfTDwa+C1eL4zE/vsBzwKrCJkiMsK/M9H66FvCtd/LrAGaC1h208D7TWyqyR9s65xOfom9vlBLexvVo0Jhc97gZ9kSeP8T1qNtouBXRPLQ4FXKjjeC8DHCRN7XwjcJKklrtuf4NQHAP8G/EbSjpL6EjL5wWbWH/h7YB6ApCOAbwBHATsDfwJuzTvnYcC+wIeBY4CD4r6fBy4ATgC2Az4HvC6pF/A74HFgCPAp4CxJB8XjXQlcaWbbEUoCMyrQo9r6Vpu+hFLlb+ttSAVkWeOy9JUkwv3zVJpGdYOG11jSFyWtIpS4Pwz8Vw1s6z4pPYH6EJzwCMJrz+PAnlU8/jxCXd8UQgZJlr4fIZQU+gIrgFZgm+TTEbgTmJpI60V49R0Wlw2YkFg/A5gWf98FfK2ATfsDL+WlnQf8Iv6+n/CwGlDpkzttfatwfV4Elual/V+8Hm8BByTSs1rCz6zG5egb110Y7d/KNU5N492A7wC7ZEnj/E8qJXwzWwecQXCOzwAzzKzbpQtJJ0iaJ2mFpBXAXoQSPcDLFlWI/BUYbGZrgC8QqmaWSLpd0h7A1cAw4MrE8d4ARCiZ51ia+P03oF/8vSvhjSOfYcDg3DHjcb8BDIrrpxLqUf8i6c+SDitwjKu70gKqr28K/A4YIGlj65WZ/b2Z7QC8Tv06/JWkL2Re45L1lXQG4W30UDN7pwa2NZ3Gcd3zhDeon9TAtpI13ox6P0lLeJoNA94BJgC9Y9o84GQKl/AfBo7PO8Y2wKXAn+LyXcDkTs5pwAcTy9cB303s+7UC+3wUeL6E/9MLOBp4G+hbb31TumY7UKT+k/AaPzGxXLMSfk/5lKovoZ1qMTCy3jY32qecPJxI/xLweL1t7+zTCEMr9CU44NcAJH2ZUMLPMRA4U9IWsX59FHCHpEGSPhfr8t8BOgit6QBXAedJ2jMec/u4byn8DPhnSeMU+KCkYYSqpFWSzpW0jaTekvaStG88x5ck7WxmGwivhSTs6VGY2QpCNcJPJB0tqZ+kXpLGEq4ncXlrYIuwqK0lbVk3oxuIEvWdDPw7cKDFsEendErU+GRJA+Pv0YQq3PvqZHJp1PuJU+LT9iJCtcty4DLgj7xXwp8N/IgQpfMc8Jm4T0vcbiXBwbYBoxPHPJ4QCrgKWARcm1hXtIQfl08jhIZ1AE8Ce8f0wYTG36WE6J6HgE/HdTcBr8Z9ngKOqLeuNbhukwkPwr8RHtgPE8LrtgQmRp2Tn7Z629xIny70XQisjfkt97mq3jY32qcLjX9BiLhbQwjT/E9g63rb3NknlTj8eiJpV+AGQqzsBuBqM7syb5uJhNb3hTHpN2b27RrZ104I51wPrDOz8XnrRYjoOYSQyaaY2WO1sK1UXOP0cY3Tpyk1rvcTJ4UncgvvxcX3J5T6R+dtMxG4rU72tdN5pM4hhCgiAR8BHq63pq6xa+wa9wyNG6EOvyzMbInFp5yZrSa0/g/pfK9McThwgwUeAnZI9DnIBK5x+rjG6dOMGvc4h59E0nBgb0K9Wz4flfS4pDtzjbc1woC7Jc2JXcbzGUJoU8ixmAxnQtc4fVzj9GkWjbM7yk+FSOpHGObgLDNblbf6MUInqw5JhwD/S+g4UQs+ZmavxNb9eyT9xczuT6xXgX0y2dDiGqePa5w+zaRxJhptBwwYYMOHDwdgzZo19O2bzYmQa2nbnDlzlpvZzpKeJcT8LqnkeFnTuN42zJkzZ7WFYS5wjSvn3XffZeHChaxbtw6AAQMGsHjx4tXAcOCXwCeABwnRaW8CSDqP0CFxPWHcqbu6Ok8za1yIsvNxPRoj8j/jxo2zHLNmzbKsUkvbCENAfAR4xHqgxvW2gRCuK9e4Orzyyis2Z84cMzNbtWqV7bbbbkaIMLmEMPjgI4RBEL9noZA5mjjcA2FohReIHSs7+zSzxoUoNx/32CqdHsBewDXAl+ttSA/lHWABIZzNNa6QlpYWWlpCe2H//v0ZNWoUzz///DrgLIIzPx54mdAf5lxCg+N0C8M9LJS0gDCi7IO1t76hKSsfZ87hz395JVPKnPCih/Kk5cXd9gRyk5OcM2bdZte5xtf3pZ6oLxTXuFb6tre3M3fuXAix6yPNbFRuXa5nKqFx8aHEbkUbHJUYD3/QoEG0tbUB0NHRsfF3rZn/8koABm0DP7z5vQE1xwzZvtamlJWPM+fwHcdpXDo6OmhtbeWKK66gtbV1QyebltzgaGZXEwcMGz9+vE2cOBGAtrY2cr9rzZTEQ/XS+e+50fbJ9bGnVHp0WKbjOLVj7dq1tLa2MnnyZI466qhc8rJcbHj8fjWmZ3ks/B6LO3zHcSrGzJg6dSqjRo3i7LPPTq6aCZwYf5/IexOKzASOlbSVpBGEUMdHamZwk+JVOo7TJBSb3B0qr9+fPXs2N954I2PGjGHs2LG55O2Bi4EZkqYS5ob9PICZPSVpBmGK0HXA6WbWI0ePzRLu8B3HqZgJEybkxnfZiKSVZvY6YbrPzTCziwgj4To1wqt0HMdxmgQv4TuZoViVQ5OF4zpOangJ33Ecp0nwEr7jOE6VyPpbqpfwHcdxmgR3+I7jOE2CO3zHcZwmwR2+4zhOk+AO33Ecp0lwh+84jtMkdBmWKWlX4AZgF2ADcLWZXSlpR8LUZcOBduAYq2DqsmbmpJNO4rbbbmPgwIE8+eSTueTeku7B9a0KhTRu9Dzc2dg4jlOIUkr464Bz4iQGHwFOlzSaMF3ZfWa2G3BfXCauOxbYE5gE/ERS7zSM7ylMmTKF3//+9/nJLTSwvsOn3V7wUy+KaOx52GkquizhW5gQd0n8vVrSM4SZaQ4HJsbNrsenLus2BxxwAO3t7fnJOxB0hSbXtxqdWYpo7HnYqQlpjlRaDmX1tJU0HNgbeBgYFB8GmNmScqcuKzZt2aBtwiwyhajXdGY50pxSbenSpaxZsyZ5/D6V6Av1nRqu2DXM0dl1LpVy/0MBjSvKw9BzNK73veXUhpIdvqR+wK+Bs8xslVRohrKwaYG0zaYuKzZt2Q9v/u0mU4Ylqff0YWlOqdbe3k7fvn1LOX5DTA1XbF7iHPlTw3WHcvODa1ycet9bTm0oKTdI2oLg7G82s9/E5GWSWmLJyKcuqz7rXN/U8TwcyfoYME516LLRVqEo/3PgGTO7LLHKpy5LlxW4vmnjedhpKkop4X8MOB6YL2leTPsGPnVZ1TjuuONoa2tj+fLlDB06lAsvvBBCQ/mBrm91yNcYGIDnYafJKCVK5wEK12lCjacu66mvnbfeeutmaSeffPJ6M/Op4apEvsaSlvv0e06z4T1tHcdxmgR3+I7jOE2Cz3jlOI5ThJ42fIU7fMdxnDpSy7ZJr9JxHMdpEtzhO47jNAnu8B3HcZoEr8N3KqKnNWo5m9JT+740K+7wnYbFnZHjlIdX6TiO4zQJ7vAdx3GahB5RpeOv9o7jOF3TIxx+MfxB4PQEvGHcqRY92uE7jpMOWShMzX95ZcFZv3pKgS4NjVNz+JImAVcCvYGfmdnFaZ2rGXF908c1Tp+saNwsb1GpOHxJvYEfAwcSpov7s6SZZvZ0GuerFlmZWb4r6qFvI90Q1SgZVapxTy99VoNG9RONTFol/P2ABWb2IoCk6cDhhBmE6k53nNfwabdzzph1m93EdbqBM61vD6HmGjfSQ7VKeD6uMWk5/CHAosTyYmD/5AaSTgFOiYsdkp6NvwcAy1OyqyLOLGCbvpfa6YZ1sq5LfSHbGhfSMm3yrlVn+kJKGqeYXzbDNU6fBtB4E9Jy+IWmRLRNFsyuBq7ebEfpUTMbn5JdFZEh27rUF7KtcRZs6ALXOH1c4xqTVserxcCuieWhwCspnasZcX3TxzVOH9e4xqTl8P8M7CZphKQtgWOBmSmdqxlxfdPHNU4f17jGpFKlY2brJJ0B3EUIt7rWzJ4qcffNXt0yRCZsq1BfyMb/yIINRXGN08c1rj0y26zKzHEcx+mB+OBpjuM4TYI7fMdxnCYhUw5f0iRJz0paIGlanW3ZVdIsSc9IekrS12L6jpLukfR8/H5fPe0sl3poLOlaSa9KejKR1tA6doZrnC6ub/fJjMNPdLM+GBgNHCdpdB1NWgecY2ajgI8Ap0d7pgH3mdluwH1xuSGoo8bXAZPy0hpWx85wjdPF9a2MzDh8Et2szexdINfNui6Y2RIzeyz+Xg08Q+gZeDhwfdzseuCIuhjYPeqisZndD7yRl9zIOnaGa5wurm8FZMnhF+pmPaROtmyCpOHA3sDDwCAzWwLhoQAMrKNp5ZIljRtZx85wjdPF9a2ALDn8krpZ1xpJ/YBfA2eZ2ap621MhmdS4h+Eap4vrWwFZcviZ62YtaQuCs7/ZzH4Tk5dJaonrW4BX62VfN8iSxo2sY2e4xuni+lZAlhx+prpZSxLwc+AZM7sssWomcGL8fSLw21rbVgFZ0riRdewM1zhdXN9KMLPMfIBDgOeAF4B/rbMtEwivik8A8+LnEGAnQov88/F7x3rrlnWNgVuBJcBaQgltaqPr6Bq7vo2orw+t4DiO0yRkqUrHcRzHSRF3+I7jOE2CO3zHcZwmwR2+4zhOk+AO33Ecp0lwh+84jtMkuMN3HMdpEv4/Cw3W+j38CLYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "std.info()\n",
    "std.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dc52c7a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17    179\n",
       "16    177\n",
       "18    140\n",
       "15    112\n",
       "19     32\n",
       "20      6\n",
       "21      2\n",
       "22      1\n",
       "Name: age, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std[\"age\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b7c4e77a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    186\n",
       "4    175\n",
       "1    143\n",
       "3    139\n",
       "0      6\n",
       "Name: Medu, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std[\"Medu\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f5feeaf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    209\n",
       "1    174\n",
       "3    131\n",
       "4    128\n",
       "0      7\n",
       "Name: Fedu, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std[\"Fedu\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e10cd4a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GP    423\n",
       "MS    226\n",
       "Name: school, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std[\"school\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "527eafc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "other       258\n",
       "services    136\n",
       "at_home     135\n",
       "teacher      72\n",
       "health       48\n",
       "Name: Mjob, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std[\"Mjob\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "45566a0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    649.000000\n",
       "mean      11.399076\n",
       "std        2.745265\n",
       "min        0.000000\n",
       "25%       10.000000\n",
       "50%       11.000000\n",
       "75%       13.000000\n",
       "max       19.000000\n",
       "Name: G1, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std[\"G1\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "14224e7a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    649.000000\n",
       "mean      11.570108\n",
       "std        2.913639\n",
       "min        0.000000\n",
       "25%       10.000000\n",
       "50%       11.000000\n",
       "75%       13.000000\n",
       "max       19.000000\n",
       "Name: G2, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std[\"G2\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "54cb8f08",
   "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>Fjob</th>\n",
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       "      <th>famrel</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <th>645</th>\n",
       "      <td>MS</td>\n",
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       "      <td>U</td>\n",
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       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>teacher</td>\n",
       "      <td>services</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>1</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>MS</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>services</td>\n",
       "      <td>services</td>\n",
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>R</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>services</td>\n",
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       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <td>5</td>\n",
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       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>649 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",
       "644     MS   F   19       R     GT3       T     2     3  services     other   \n",
       "645     MS   F   18       U     LE3       T     3     1   teacher  services   \n",
       "646     MS   F   18       U     GT3       T     1     1     other     other   \n",
       "647     MS   M   17       U     LE3       T     3     1  services  services   \n",
       "648     MS   M   18       R     LE3       T     3     2  services     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",
       "644  ...      5        4      2     1     2      5        4  10  11  10  \n",
       "645  ...      4        3      4     1     1      1        4  15  15  16  \n",
       "646  ...      1        1      1     1     1      5        6  11  12   9  \n",
       "647  ...      2        4      5     3     4      2        6  10  10  10  \n",
       "648  ...      4        4      1     3     4      5        4  10  11  11  \n",
       "\n",
       "[649 rows x 33 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8e58fb95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     649\n",
       "unique      2\n",
       "top         F\n",
       "freq      383\n",
       "Name: sex, dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std[\"sex\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0d0162cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mapping strings to numeric values:\n",
    "def numerical_data():\n",
    "    std['school'] = std['school'].map({'GP': 0, 'MS': 1})\n",
    "    std['sex'] = std['sex'].map({'M': 0, 'F': 1})\n",
    "    std['address'] = std['address'].map({'U': 0, 'R': 1})\n",
    "    std['famsize'] = std['famsize'].map({'LE3': 0, 'GT3': 1})\n",
    "    std['Pstatus'] = std['Pstatus'].map({'T': 0, 'A': 1})\n",
    "    std['Mjob'] = std['Mjob'].map({'teacher': 0, 'health': 1, 'services': 2, 'at_home': 3, 'other': 4})\n",
    "    std['Fjob'] = std['Fjob'].map({'teacher': 0, 'health': 1, 'services': 2, 'at_home': 3, 'other': 4})\n",
    "    std['reason'] =std['reason'].map({'home': 0, 'reputation': 1, 'course': 2, 'other': 3})\n",
    "    std['guardian'] = std['guardian'].map({'mother': 0, 'father': 1, 'other': 2})\n",
    "    std['schoolsup'] = std['schoolsup'].map({'no': 0, 'yes': 1})\n",
    "    std['famsup'] =std['famsup'].map({'no': 0, 'yes': 1})\n",
    "    std['paid'] = std['paid'].map({'no': 0, 'yes': 1})\n",
    "    std['activities'] = std['activities'].map({'no': 0, 'yes': 1})\n",
    "    std['nursery'] = std['nursery'].map({'no': 0, 'yes': 1})\n",
    "    std['higher'] = std['higher'].map({'no': 0, 'yes': 1})\n",
    "    std['internet'] = std['internet'].map({'no': 0, 'yes': 1})\n",
    "    std['romantic'] = std['romantic'].map({'no': 0, 'yes' : 1})\n",
    "   # std['passed'] = std['passed'].map({'no': 0, 'yes': 1})\n",
    "    # reorder dataframe columns :\n",
    "   # col = std['passed']\n",
    "   # del std['passed']\n",
    "  #  std['passed'] = col\n",
    "\n",
    "    \n",
    "# feature scaling will allow the algorithm to converge faster, large data will have same scal\n",
    "def feature_scaling(df):\n",
    "    for i in std:\n",
    "            col = std[i]\n",
    "        # let's choose columns that have large values\n",
    "    if(np.max(col)>6):\n",
    "                Max = max(col)\n",
    "                Min = min(col)\n",
    "                mean = np.mean(col)\n",
    "                col  = (col-mean)/(Max)\n",
    "                std[i] = col\n",
    "            \n",
    "    elif(np.max(col)<6):\n",
    "                col = (col-np.min(col))\n",
    "                col /= np.max(col)\n",
    "                std[i] = col\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1eb2cb50",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <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>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",
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       "      <td>1</td>\n",
       "      <td>18</td>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>649 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  ...  \\\n",
       "0         0    1   18        0        1        1     4     4     3     0  ...   \n",
       "1         0    1   17        0        1        0     1     1     3     4  ...   \n",
       "2         0    1   15        0        0        0     1     1     3     4  ...   \n",
       "3         0    1   15        0        1        0     4     2     1     2  ...   \n",
       "4         0    1   16        0        1        0     3     3     4     4  ...   \n",
       "..      ...  ...  ...      ...      ...      ...   ...   ...   ...   ...  ...   \n",
       "644       1    1   19        1        1        0     2     3     2     4  ...   \n",
       "645       1    1   18        0        0        0     3     1     0     2  ...   \n",
       "646       1    1   18        0        1        0     1     1     4     4  ...   \n",
       "647       1    0   17        0        0        0     3     1     2     2  ...   \n",
       "648       1    0   18        1        0        0     3     2     2     4  ...   \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",
       "644       5         4      2     1     2       5         4  10  11  10  \n",
       "645       4         3      4     1     1       1         4  15  15  16  \n",
       "646       1         1      1     1     1       5         6  11  12   9  \n",
       "647       2         4      5     3     4       2         6  10  10  10  \n",
       "648       4         4      1     3     4       5         4  10  11  11  \n",
       "\n",
       "[649 rows x 33 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All values in numerical after calling numerical_data() function\n",
    "numerical_data()\n",
    "std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c8fc3fb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uXfLIJUmSBszZWCVJ0jhZzOxjs60FjkhyLXBE+5yqugo4D/g68EngZVV17/YGKkmSNAKcjVUTzYpRSRovi6oUqqqpqjq6fXxrVT2jqg5of9/Wsd7pVfXoqnpcVX1iqYOWJEkaNGdjlQArRiVprCx+BGdJkqTJ9EaWeDZW6G5GVmdjnckZSGcaVHl0VIyeDvxBu/gYYFX7eB0wBZxKR8UocH2S6YrRL/Q9UElS16wUkiRJWkC/ZmOF7mZkdTbWmZyBdKYBlscbGVLFKFgZ2Gmcy6KXCvBxLg+p36wUkiQtKMm7gekvxQe3y14HvAT4Trvaa6rq4+1rpwEnAfcCr6iqCwcetLS0nI1VE23YFaNgZWCncS6LE9d8bNHbnLV617EtD6nftmegaUnS5DiLZkyI2f62qg5tf6YrhBxHQmPH2Viln1SMrgfOAZ7eWTEKYMWoJC0/VgpJkhZUVZ8DbltwxYYD7GqSOBurJoIVo5I0nuw+JknaHi9P8pvAl4FTqup2+jCORC/mG5Og1wF7F7IUsY/ymAijGtsw4qqqKZrBdKmqW4FnzLPe6TQD8krjbC1wXpKTgBuA46CpGE0yXTG6BStGJWkkWSkkSerV/wX+gmaMiL8A3gC8mD6MI9GL+cYkOOWQLT0N2LuQpRjQd5THiBjV2EY1LmmcWTEqSePD7mOSpJ5U1S1VdW9V/Rj4O+7rIuY4EpIkSdIyYKWQtIAkOye5NMlXklyV5M/a5XsmuSjJte3vPTq2OS3JdUmuSfKs4UUv9c/0wKKtXwW+1j52HAlJkiRpGbD7mLSwe4CnV9XmJDsCn0/yCeC5wMVVtTbJGmANcOqsmZf2AT6d5LH2o9dyluT9wCpgryQbgNcCq5IcStM1bD3wO+A4EpIkSdJyYaWQtICqKmBz+3TH9qdoZlha1S5fR9O3/lQ6Zl4Crk8yPfPSFwYXtbS0qur5cyx+1zbWdxwJSZIkacRZKSR1IckOwGXAY4C3VdUlSVZU1UaAqtqY5KHt6ks+89KozvozDONaFr3OhjWu5TEpVs4aDPuUQ7bMO0B2p/Vrj+pXSJIkSZogVgpJXWi7vhya5MHAh5McvI3Vl3zmJWfXuc+4lkU3FQFzOWv1rmNZHpIkSZL6z4GmpUWoqjtouomtBm6ZHmi3/b2pXc2ZlyRJkiRJI89KIWkBSR7SthAiyS7AM4Fv0MywdEK72gnA+e1jZ16SJEmSJI28BSuFnI5bYm/gs0m+CnwJuKiqPgqsBY5Ici1wRPucqroKmJ556ZM485IkSZIkaQR1M6aQ03FrolXVV4EnzLH8VuAZ82zjzEuSJEnSGGonofkycFNVHZ1kT+BcYCWwHnheVd3ernsacBJwL/CKqrpwKEFL81iwpVA15puOe127fB1wbPv4J9NxV9X1wPR03JIkSZIkLXevBK7ueL6GpsHEAcDF7XNmNZhYDby9rVCSRkZXYwol2SHJFTQD6V5UVZcAM6bjBjqn476xY/N5p+OWJEmSJGm5SLIfcBTwzo7FNpjQstXVlPT9mI47ycnAyQArVqxgampqzp2t2AVOOWRLN2HOMN/+lrvNmzeP7d+2WJaFJEmSpAF7I/BqYPeOZTMaTCTpbDDxxY715m0w0c33Y78bz+T3wZl6LY+uKoWmVdUdSabomI67PegXPR13VZ0JnAlw2GGH1apVq+Z8z7ecfT5vuHJRYQKw/gVz72+5m5qaYr6ymjSTVBZX3vQ9TlzzsUVts37tUX2KRpImT5Kdgc8BO9FcP32gql7rOBKSNDmSHA1sqqrLkqzqZpM5lm3VYAK6+37sd+OZJun7YDd6LY9uZh9zOm5JkjTppife+FngUGB1kqfgOBKaIM5KLPFU4DlJ1gPnAE9P8l7aBhMAvTSYkIapmzGFnI5bkiRNNCfekAArRzXhquq0qtqvqlbSHNufqaoXYoMJLWMLtj1zOm5JkqSfTEF8GfAY4G1VdUkSx5EYAseRmGlQ5VFVBcxXObqqXb4OmAJOpaNyFLg+yXTl6Bf6Hqw0WGuB85KcBNwAHAdNg4kk0w0mtmCDCY2gxXdIlCRJmkD9mHij3a/jSCyS40jMNMjy6EflaLcT0FgZeJ9xLoteKsCHUR5VNUVTAWqDCS1rVgpJkiQtwlJOvCEtN/2oHO12AhorA+8zzmWx2MlVAM5avevYlofUb92MKSRJkjTRnHhDmqmq7qBpJfGTylFwkF1JWm5sKSRJGnkre7hrKC2xvYF1bdeZ+wHnVdVHk3wBx5HQhEjyEOBHbWu56crR13Nf5ehatq4cfV+SM4B9sHJUkkaOlUKSJEkLcOINCbByVJLGjpVCkiRJkhZk5agkjR/HFJIkSZIkSZpAVgpJkhaU5N1JNiX5WseyPZNclOTa9vceHa+dluS6JNckedZwopYkSZK0LVYKSZK6cRbNDDOd1gAXV9UBwMXtc5IcCBwPHNRu8/Z2/AlJkiRJI8RKIUnSgqrqc8BtsxYfA6xrH68Dju1Yfk5V3VNV1wPXAYcPIk5JkiRJ3XOgaUlSr1ZU1UaAqtqY5KHt8n2BL3ast6FdtpUkJwMnA6xYsYKpqak53+iUQ7YsUciwYpel3d+0+WLfltlxdBtbL++1vTZv3jyU913IqMYlSZK0HFgpJC0gyf7Ae4CHAT8GzqyqNyXZEzgXWAmsB55XVbe325wGnATcC7yiqi4cQujSsGSOZTXXilV1JnAmwGGHHVarVq2ac4cnrvnYUsXGKYds4Q1XLv3pb/0LVi16m9l/V7ex9fJe22tqaor5/j/DNKpxSZIkLQd2H5MWtgU4paoeDzwFeFk7ZorjqWjS3ZJkb4D296Z2+QZg/4719gNuHnBskiRJkhZgSyGNhJU9tAI4a/WufYhka233mOkuMncluZqmK8wxwKp2tXXAFHAqHeOpANcnmR5P5QsDCVganAuAE4C17e/zO5a/L8kZwD7AAcClQ4lQkiRJ0rysFJIWIclK4AnAJQxwPJVexkAZ1zE2xnX8kF7HuBlUeSR5P00l6F5JNgCvpakMOi/JScANwHEAVXVVkvOAr9O0tHtZVd3b9yAlSZIkLYqVQlKXkuwGfBB4VVXdmcw1bEqz6hzLtms8lbecff6ix0AZxpgjgzCu44f0OmbOWat3HUh5VNXz53npGfOsfzpwev8ikiRJkrS9FhxTKMn+ST6b5OokVyV5Zbt8zyQXJbm2/b1HxzanJbkuyTVJntXPP0AahCQ70lQInV1VH2oXO56KJEmSJGnZ6magaQfZ1URL0yToXcDVVXVGx0vT46nA1uOpHJ9kpySPxPFUJEmSJEkjaMFKoaraWFWXt4/vAjoH2V3XrrYOOLZ9/JNBdqvqemB6kF1puXoq8CLg6UmuaH+OpBlP5Ygk1wJHtM+pqquA6fFUPonjqUiSJEmSRtCiBilZykF2+znALjjI7nLTy/94UGVRVZ9n7nGCwPFUJGkiJNkfeA/wMODHwJlV9aYkewLnAiuB9cDzqur2dpvTgJOAe4FXVNWFQwhdkiRpXl1XCi31ILv9HGAXHGR3uellkN1BDbArSRL3dae/PMnuwGVJLgJOpOlOvzbJGpru9KfO6k6/D/DpJI+15aiWMytHJWn8dDOmkIPsSpKkiWZ3eglwrFFJGjsLNsHpYpDdtWw9yO77kpxBc2fMQXYlSdLYWMru9O3+FuxSb3f6mca1O32vBtilfiMwfbzflaSzcnRVu9o6YAo4lY7KUeD6JNOVo1/oe7CSpK500y9repDdK5Nc0S57DU1l0HlJTgJuAI6DZpDdJNOD7G7BQXYlSdKYWOru9NBdl3q70880rt3pV/bQnR7grNW7Dbw8hjHWqJWB9xnnshjlsUalcbTg1YWD7EqSJG27O337Rdju9JoIwxprdFwrA3sxzmXhWKPSYC3+lpMkSdKEsTu91Bhm5eiVN31v0RUG69ce1evbSdJE6GqgaUmSpAk33Z3+6UmuaH+OpKkMOiLJtcAR7XOq6ipgujv9J7E7vcZAF5WjsHXl6PFJdkrySKwclaSRY0shSZKkBdidXgIca1SSxo6VQpIkSZIWZOWoJI0fu49JkiRJkrSAJPsn+WySq5NcleSV7fI9k1yU5Nr29x4d25yW5Lok1yR51vCil+ZmpZAkSZIkSQvbApxSVY8HngK8LMmBwBrg4qo6ALi4fU772vHAQcBq4O1JdhhK5NI8rBSSJEmSJGkBVbWxqi5vH98FXA3sCxwDrGtXWwcc2z4+Bjinqu6pquuB64DDBxq0tADHFJIkSZIkaRGSrASeAFwCrKiqjdBUHCV5aLvavsAXOzbb0C6ba38nAycDrFixgqmpqa3WWbELnHLIlkXHOte+xsHmzZvH9m/rRa/lYaWQJEmSJEldSrIb8EHgVVV1ZzLf+OtzDsxec61YVWcCZwIcdthhtWrVqq3WecvZ5/OGKxf/FX79C7be1ziYmppirnKaVL2Wh93HJEmSJEnqQpIdaSqEzq6qD7WLb0myd/v63sCmdvkGYP+OzfcDbh5UrFI3rBSSJEmSJGkBaZoEvQu4uqrO6HjpAuCE9vEJwPkdy49PslOSRwIHAJcOKl6pG3YfkyRtlyTrgbuAe4EtVXVYkj2Bc4GVwHrgeVV1+7BilCRJWgJPBV4EXJnkinbZa4C1wHlJTgJuAI4DqKqrkpwHfJ1m5rKXVdW9A49a2gYrhSRJS+FpVfXdjufTU7OuTbKmfX7qcEKTJEnaflX1eeYeJwjgGfNsczpwet+CkraT3cekLiR5d5JNSb7WsWzPJBclubb9vUfHa6cluS7JNUmeNZyopaGab2pWSZIkSSPClkJSd84C3gq8p2PZnC0hkhwIHA8cBOwDfDrJY20qqjFWwKeSFPD/2tkz5puadYZupl+F3qZfnU+v07kupJcpQGfH0W1sw5h+dVSnfR3VuCRJkpaDBSuFkrwbOBrYVFUHt8vmHSsiyWnASTRjS7yiqi7sS+TSAFXV55KsnLX4GGBV+3gdMEXTPeYY4Jyquge4Psl1wOHAFwYSrDR4T62qm9uKn4uSfKPbDbuZfhXgxDUfW4o4gabSpZfpXBfSy3Svs/+ubmMbxtSyozrt66jGJUmStBx0c1V8FraQkOYyX0uIfYEvdqy3oV22lW5bSfTSsmFc75yPa6uAXluujEJ5VNXN7e9NST5MUwl6S5K929zonJpVkiRJ0ohYsFLIFhLSos01+FzNtWK3rSTecvb5i27ZMIyWBIMwrq0Cem0Jc9bqXYdaHkl2Be5XVXe1j38Z+HPum5p1LTOnZpWWLVtPa9KZA5I0fnodaHpGCwmgs4XEjR3rzdtCQhoDt7QtIJjVEmIDsH/HevsBNw84NmlQVgCfT/IV4FLgY1X1SZrKoCOSXAsc0T6XlruzgNWzlk23nj4AuLh9zqzW06uBtyfZYXChSn1xFuaAJI2VpR5UoesWEv3sNgN2nVluevkfj0BZzNcS4gLgfUnOoOlGeQDNl2Vp7FTVN4GfnWP5rcwzNau0XNl6WpPOHJCk8dNrpdB8Y0V03UKin91mwK4zy00vXWcG2W0myftpLnj2SrIBeC1NZdB5SU4CbgCOA6iqq5KcB3wd2AK8zHG1JGlsDWR8OW+SzTQCN4b6YpmOL+cYi0MwrjkAy/ZmsbRs9VopZAsJTZSqev48L83ZEqKqTgdO719EkqQRt6Tjy3mTbCZvks007PHl5uEYi300rjkAo3+zWBo3C44p1LaQ+ALwuCQb2lYRc44VUVVXAdMtJD6JLSQkSdJ4c3w5TTpzQJKWsQUrharq+VW1d1XtWFX7VdW7qurWqnpGVR3Q/r6tY/3Tq+rRVfW4qvpEf8OXJEkaqunW07B16+njk+yU5JHYelrjyxyQpGVsqQealiRJGkuOL6dJZw5I0vixUkiSpGVmZQ/jLaxfe1QfIpksji+nSWcOSNL4WbD7mCRJkiRJksaPlUKSJEmSJEkTyEohSZIkSZKkCeSYQpIkSZIkSSOgl7EjAc5avWtP29lSSJIkSZIkaQJZKSRJkiRJkjSBrBSSJEmSJEmaQFYKSZIkSZIkTSArhSRJkiRJkiaQlUKSJEmSJEkTyEohSZIkSZKkCWSlkCRJkiRJ0gSyUkiSJEmSJGkCWSkkSZIkSZI0ge7frx0nWQ28CdgBeGdVre3Xe0mjyBzQpDMHxsPKNR8D4JRDtnBi+3gh69ce1c+QlhXzQJPOHJDMA422vrQUSrID8Dbg2cCBwPOTHNiP95JGkTmgSWcOSOaBZA5I5oFGX79aCh0OXFdV3wRIcg5wDPD1Pr2fNGrMAU06c2CCreyyRdFsY9jCyDzQpDMHJPNAI65flUL7Ajd2PN8APLlzhSQnAye3TzcnuWaefe0FfHexAeT1i91i2eipPMbR016/zbJ4xCBjmcOCOQD9zQNzYDKMcB4sdQ4smVf06RhaipzrV2yw/fH1M7ZpPcY4qjkAS5sHXg/N5LmgwzbOBeOUA+D1UCdzoMMIXw+B54J+Mg869Hou6FelUOZYVjOeVJ0JnLngjpIvV9VhSxXYcmd53GfEy2LBHADzoBeWxUwjXB5LmgNLaYTLzNh6MKpxtZYsD0b87xw4y2OmES4Pr4f6xLKYacTLw3NBn1geM/VaHv2afWwDsH/H8/2Am/v0XtIoMgc06cwByTyQzAHJPNCI61el0JeAA5I8MskDgOOBC/r0XtIoMgc06cwByTyQzAHJPNCI60v3sarakuTlwIU00+69u6qu6nF3A+1WsAxYHvcZ2bJY4hyAEf5bh8CymGkky6MPObCURrLMWsa2eKMal9dD/WV5zDSS5eH1UF9ZFjONbHl4Lugry2OmnsojVVt1Z5QkSZIkSdKY61f3MUmSJEmSJI0wK4UkSZIkSZIm0MhUCiVZneSaJNclWTPH60ny5vb1ryZ54jDiHIQuymJVku8luaL9+dNhxDkISd6dZFOSr83z+tgcF+bATObBfSYpD5bafGWX5Pfa4+uqJP97VGJLcmiSL7bH9JeTHD6EuPZP8tkkV7fl88p2+Z5JLkpybft7jxGK7f8k+UZ7/H84yYMHHdtS8Vwwk+eC+0zKucAcmMkcuM+k5ACYB7OZB/fpSx5U1dB/aAbc+k/gUcADgK8AB85a50jgE0CApwCXDDvuIZbFKuCjw451QOXxi8ATga/N8/pYHBfmQE/lYR5M4LGxFGUHPA34NLBT+/yhIxTbp4Bnd/xfp4YQ197AE9vHuwP/ARwI/G9gTbt8DfD6EYrtl4H7t8tfP4zYlujv81yw+PLwXDBGx4Y50FN5mANjdmyYBz2Vh3mwHcfGqLQUOhy4rqq+WVU/BM4Bjpm1zjHAe6rxReDBSfYedKAD0E1ZTIyq+hxw2zZWGZfjwhyYyTzoMEF5sOTmKbvfBdZW1T3tOpsGHhjzxlbAA9vHDwJuHmhQQFVtrKrL28d3AVcD+9IcZ+va1dYBx45KbFX1qara0q72RWC/Qce2RDwXzOS5oMOEnAvMgZnMgQ4TkgNgHsxmHnToRx6MSqXQvsCNHc83tMsWu8446Pbv/PkkX0nyiSQHDSa0kTQux4U5MJN5sDiTdGwshccC/yPJJUn+OcmThh1Qh1cB/yfJjcDfAKcNM5gkK4EnAJcAK6pqIzSVM8BDhxja7Ng6vZjmDtly5LlgJs8FizMOx4Y5MJM5sDjjcmyYBzOZB4uz6GPj/n0Np3uZY1n1sM446ObvvBx4RFVtTnIk8BHggH4HNqLG5bgwB2YyDxZnko6NpXB/YA+aJrVPAs5L8qhq29wO2e8Cv19VH0zyPOBdwDOHEUiS3YAPAq+qqjuTuQ6z4ZgdW8fy/wVsAc4eVmzbyXPBTJ4LFmccjg1zYCZzYHHG5dgwD2YyDxZn0cfGqLQU2gDs3/F8P7ZuMt/NOuNgwb+zqu6sqs3t448DOybZa3AhjpRxOS7MgZnMg8WZpGNjKWwAPtQ2q70U+DEwKsfOCcCH2sf/SNNkeuCS7EhT6XJ2VU3Hc8t08+P291C63c0TG0lOAI4GXjAiFXy98Fwwk+eCxRmHY8McmMkcWJxxOTbMg5nMg8VZ9LExKpVCXwIOSPLIJA8AjgcumLXOBcBvtqNpPwX43nQz9jGzYFkkeVjaW7ZpZqa5H3DrwCMdDeNyXJgDM5kHizNJx8ZS+AjwdIAkj6UZtPC7wwyow83AL7WPnw5cO+gA2rx6F3B1VZ3R8dIFNJVWtL/PH5XYkqwGTgWeU1XfH3RcS8hzwUyeCxZnHI4Nc2Amc2BxxuXYMA9mMg8WZ9HHxkh0H6uqLUleDlxIM7r4u6vqqiQvbV9/B/BxmpG0rwO+D/zWsOLtpy7L4teA302yBfgv4PhlfFd0m5K8n2Y0+b2SbABeC+wI43VcmAMzmQczTUoe9MM8Zfdu4N1ppvL8IXDCMI6deWJ7CfCmJPcHfgCcPOi4gKcCLwKuTHJFu+w1wFqarnYnATcAx41QbG8GdgIuaq8Jv1hVLx1CfNvFc8FMngtmmoRzgTkwkzkw0yTkAJgHs5kHM/UjDzKmZSVJkiRJkqRtGJXuY5IkSZIkSRogK4XGQJJK8phhxyENWpKV7fE/El1hpX5I8o4kf9Llup4PNFaSPDzJ5iQ7dLHu65K8dxBxSZJG0+zvB2mmqD9hoe0mmV+kBizJemAfYJ+q+m7H8iuAnwUeWVXrhxKcNGBtPqwA7u1Y/NiqGtfZE6QZujwnLLuxcaRebOOcsNtwIpIkLXdV9exhxzDqbCk0HNcDz59+kuQQYJfhhSMN1a9U1W4dP1YIadJ4TpDu4zlBE8FWztL2M4+WhpVCw/EPwG92PD8BeM/0kyQ7JfmbJDckuaXtOrBLx+t/lGRjkpuTvLhzx0mmkvx2x/MTk3y+j3+LtKSSPCjJu9pj/KYkfzndbSDJDm1ufDfJN4GjZm27PskzO57blUDLwULnhLOS/GXH85ckuS7JbUkuSLLPrP0dmeSbbZ78nySe67VszdENYJ/2uL+tzYOXzNpk5yTnJrkryeVJfnYIYUtzaq9TTk3yVeDuJL+Q5N+S3JHkK0lWdaz7W0mubo/lbyb5nY7X9kry0Xa725L8y/RnfZLHt98H7khyVZLndGx3VpK3JflYu99Lkjx6gEWgCZLkiUn+vT3W/rH9bP7Lub6fdnZ/T3JUu92dSW5M8rqO9abPCScluQH4TBffD37y/TjJo5N8Jsmt7fpnJ3lwx7rrk/xhkq8m+V4b8879K6XR4IXicHwReGD7ob0D8OtA5xfX1wOPBQ4FHgPsC/wpQJLVwB8CRwAHAM9EGi/rgC00x/4TgF8Gpis6XwIc3S4/jGb6SWm5W+ic8BNJng78NfA8YG/gW8A5s1b7VZr8eCJwDPBipPHxfmADTbfLXwP+KskzOl4/BvhHYE/gfcBHkuw48Cil+T2f5kvro4Dzgb+kOV7/EPhgkoe0622iueZ5IM2U0n+b5Inta6fQ5MFDaLpcvgao9lj/J+BTwEOB3wPOTvK4We//Z8AeNFNWn96fP1OTLMkDgA8DZ9Ec3++nuT7pxt00N8seTJMrv5vk2Fnr/BLweOBZLO77QWiuo/Zpt98feN2sdZ4HrAYeCfwMcGKXcS9bVgoNz/Sd4SOAbwA3tctDc2D/flXdVlV3AX8FHN++/jzg76vqa1V1N1sfxNJy85H2btYdST4BPBt4VVXdXVWbgL9l5vH/xqq6sapuo/lQl8bBfOeE2V4AvLuqLq+qe4DTgJ9PsrJjnde3548bgDfS0TVNWgY6zwkf6Xwhyf7ALwCnVtUPquoK4J3AizpWu6yqPlBVPwLOAHYGnjKY0KWuvLmqbgReCHy8qj5eVT+uqouALwNHAlTVx6rqP6vxzzQVPf+j3cePaG4MPKKqflRV/1JVRXOs7wasraofVtVngI8y8zzwoaq6tKq2AGfT3ISWltpTaMYvfnN7jH4IuLSbDatqqqqubPPiqzQVSr80a7XXtd8V/otFfD+oquuq6qKquqeqvkNznpi97zdX1c3tvv6JCcgR++ANzz8An6OpgXxPx/KHAD8FXJZkelmA6Vk39gEu61j/W/0NU+q7Y6vq0wBJDqep8d/YcfzfD7ixfbxPx2Pw+Nf4mO+cMNs+wOXTT6pqc5JbaVqUrm8Xz86R2d3LpFH2k3MCNF0FOl7bB5i+YTbtWzR3hqf95Pivqh8nmW5VJI2K6WP0EcBxSX6l47Udgc8CJHk28Fqa3gP3o/l+cGW73v+huTH8qfZ66cyqWkt7nVRVP+7Y57dozhHTvt3x+Ps0lUjSUtsHuKmtrJx243wrd0ryZGAtcDDwAGAnmhagnTr31fX3gyQPBd5MU8G6O01u3T5rtdk5MvbnEFsKDUlVfYtmcNEjgQ91vPRd4L+Ag6rqwe3Pgzpm3thI08xt2sNn7fpumpPGtIctbeRSX90I3APs1XH8P7CqDmpf9/jXWNrGOWG2m2m+SACQZFfgp5nZsmh2jjhQr8bFzcCeSXbvWPZw5jn+2zFW9sMc0GiZ/pJ8I/APHdc7D66qXatqbZKdgA8CfwOsqKoHAx+nuVFMVd1VVadU1aOAXwH+oO1GeTOw/6yx5GbniDQIG4F903GXl/s+n2dcryeZfb3+PuACYP+qehDwDtpjv0NnZdNC3w86/XW77c9U1QNpWuzN3vfEsVJouE4Cnt52A5v2Y+DvaPoNPxQgyb5JntW+fh5wYpIDk/wUzR2ETlcAz03yU+1gXSf19S+QllBVbaRpHv2GJA9Mcr92QLjpZp3nAa9Isl+SPYA1s3ZxBXB8kh2TOOaQlpu5zgmzvQ/4rSSHtl8a/gq4pKrWd6zzR0n2aLvavBI4t28RSwPUdrn5N+Cvk+yc5Gdo8ubsjtV+Lslz0wxM/SqaGw1fHHiw0sLeC/xKkme1A+XunGRVkv24r3XEd4AtbauhX57eMMnRSR7TfuG+E7i3/bmE5gv3q9troVU0lUazx56T+u0LNMfky5PcP8kxwOHta18BDmqvZXZm6+FQdqdpFfqDthfBbyzwXgt9P5i9783AHUn2Bf5oUX/VmLJSaIjafsJfnuOlU2kGfvtikjuBTwOPa7f5BM0YEZ9p1/nMrG3/FvghcAvNgL1nIy0vv0lzMfR1muacH6DpNw9NhemFNCeTy9m6RcWfAI9ut/szmi/Q0rKwjXNC5zoX0xznH6S5M/Zo7htza9r5NN2MrwA+BrxryYOVhuf5wEqaFhEfBl7bjsUy7Xyawdpvpxlr6Lnt+ELSSGkrOY+hGST6OzQth/4IuF/bRfIVNF92b6f5UnxBx+YH0Hw/2Ezz5fvt7TgsPwSeQzM+43eBtwO/WVXfGMgfJbXaY/G5NBX3d9C0yPkocE9V/Qfw5zTH8LXA7Jmy/yfw50nuopls6bwF3m6h7wed/oxmIo7v0VwjbWvdiZGZ3fwkSdIoSfIe4Lqq+vNhxyINWpJH0XxpuH950SpJy1aSS4B3VNXfDzsWzWRLIUmSRlTbBeZxNOMNSZPoYGC9FUKStLwk+aUkD2u7j51AM737J4cdl7bm7GOSJI2ub9N0BfvgsAORBi3JHwCvBn5v2LFIkhbtcTRdv3YD/hP4tXb8UI0Yu49JkiRJkiRNILuPSZIkSZIkTaCR6D6211571cqVK+d87e6772bXXXcdbEAjzPK4z7bK4rLLLvtuVT1kwCFtF/OgO5bFTOOUB+ZA9yyP+4xTDsD8eeD/fCbLY6b5ymOccgCW5//dmAfDc8Fksjxm6vVcMBKVQitXruTLX557Ft6pqSlWrVo12IBGmOVxn22VRZJvDTaa7WcedMeymGmc8sAc6J7lcZ9xygGYPw/8n89kecw0X3mMUw7A8vy/G/NgeC6YTJbHTL2eC+w+JkmSJEmSNIGsFJIkSZIkSZpAVgpJkiRJkiRNoJEYU0jdu/Km73Himo8tapv1a4/qUzTS4PWSA2AeSMvByh5y+6zVDjCp8dFLDoB5oO51c4ydcsiWra61vI4aDV4Hqx9sKSQtIMn+ST6b5OokVyV5Zbt8zyQXJbm2/b1HxzanJbkuyTVJnjW86CVJkiRJmpuVQtLCtgCnVNXjgacAL0tyILAGuLiqDgAubp/TvnY8cBCwGnh7kh2GErkkSZIkSfOwUkhaQFVtrKrL28d3AVcD+wLHAOva1dYBx7aPjwHOqap7qup64Drg8IEGLUmSJEnSAhxTSFqEJCuBJwCXACuqaiM0FUdJHtquti/wxY7NNrTL5trfycDJACtWrGBqamrO9928efO8r02aFbs0fd0Xa1zLz2NDkiRJUq+sFJK6lGQ34IPAq6rqziTzrjrHspprxao6EzgT4LDDDqtVq1bNucOpqSnme23SvOXs83nDlYv/6Fr/glVLH8wI8NiQJEmS1CsrhTQSRn3GmSQ70lQInV1VH2oX35Jk77aV0N7Apnb5BmD/js33A24eWLCSJEmSJHXBMYWkBaRpEvQu4OqqOqPjpQuAE9rHJwDndyw/PslOSR4JHABcOqh4JUmSJEnqhi2FpIU9FXgRcGWSK9plrwHWAuclOQm4ATgOoKquSnIe8HWamcteVlX3DjxqSZIkSZK2wUohaQFV9XnmHicI4BnzbHM6cHrfgpIkSZI0FEl2AL4M3FRVRyfZEzgXWAmsB55XVbe3654GnATcC7yiqi4cStDSPOw+JkmSJElS914JXN3xfA1wcVUdAFzcPifJgcDxwEHAauDtbYWSNDKsFJIkSZIkqQtJ9gOOAt7ZsfgYYF37eB1wbMfyc6rqnqq6HrgOOHxAoUpdsfuYJEmSJEndeSPwamD3jmUrqmojQDsz8UPb5fsCX+xYb0O7bCtJTgZOBlixYgVTU1NbrbNiFzjlkC2LDniufY2DzZs3j+3f1otey8NKIUmSJEmSFpDkaGBTVV2WZFU3m8yxrOZasarOBM4EOOyww2rVqq13/5azz+cNVy7+K/z6F2y9r3EwNTXFXOU0qXotDyuFJEmSJEla2FOB5yQ5EtgZeGCS9wK3JNm7bSW0N7CpXX8DsH/H9vsBNw80YmkBjikkSZIkSdICquq0qtqvqlbSDCD9map6IXABcEK72gnA+e3jC4Djk+yU5JHAAcClAw5b2iYrhSRJkhaQZOcklyb5SpKrkvxZu3zPJBclubb9vUfHNqcluS7JNUmeNbzoJUl9thY4Ism1wBHtc6rqKuA84OvAJ4GXVdW9Q4tSmsOC3ceSvBuY7jt5cLvsdcBLgO+0q72mqj7evnYacBJwL/CKqrqwD3FL6pOVaz626G3Wrz2qD5FI0ki5B3h6VW1OsiPw+SSfAJ5LMw3x2iRraKYhPnXWNMT7AJ9O8li/DEjSeKiqKWCqfXwr8Ix51jsdOH1ggUmL1E1LobOA1XMs/9uqOrT9ma4Q6rwAWg28PckOSxWsJEnSMFRjc/t0x/ancBpiSZK0jC3YUqiqPpdkZZf7+8kFEHB9kukLoC/0HqIkSdLwtTe6LgMeA7ytqi5JMpBpiJ12d6ZxLY9eppqG8S0PSVL/bc/sYy9P8pvAl4FTqup2lvgCCDzJzbZil8VfMCyH8uvlIshjQxqcJPsD7wEeBvwYOLOq3pRkT+BcYCWwHnheez6wO7HGTtv169AkDwY+nOTgbay+pNMQO+3uTONaHif20IUb4KzVu45leUiS+q/XSqH/C/wFzcXNXwBvAF7MEl8Awfie9Hv1lrPP5w1XLu7ftv4Fq/oTzBLq5SLICyBpoLbQ3AC4PMnuwGVJLgJOxPFUNGGq6o4kUzRd5Z2GWJIkLVs9zT5WVbdU1b1V9WPg77ivj7wXQJI0hqpqY1Vd3j6+C7iapiWo46loIiR5SNtCiCS7AM8EvoHTEEuSpGWsp5ZC03fE2qe/CnytfXwB8L4kZ9DcGfYCSJLGTDvO3BOAS4DtGk/FrsS9GdfyGPGuxHsD69pxhe4HnFdVH03yBeC8JCcBNwDHQTMNcZLpaYi34DTEkiRpBHUzJf37gVXAXkk2AK8FViU5lKZr2Hrgd2D5XwD1MhU3OB23pMmRZDfgg8CrqurOZK5ew82qcyzbqjuxXYl7M67lMcpdiavqqzSVobOXOw2xJElatrqZfez5cyx+1zbW9wJIksZQkh1pKoTOrqoPtYsdT0WSJElaprZn9jGNuV5aTo1rq6kk7waOBjZV1cHtstcBLwG+0672mqr6ePuasy5prKRpEvQu4OqqOqPjpenxVNay9XgqdieWJEmSRpiVQlJ3zgLeSjMld6e/raq/6VzgrEsaU08FXgRcmeSKdtlraCqDHE9FkiRJWoasFJK6UFWfawfX7cZPZl0Crk8yPevSF/oVn9RvVfV55h4nCBxPRZImQpL9aW6QPQz4MXBmVb0pyZ7AucBKmvFGn1dVt7fb2HpakkaYlULS9nl5kt8Evgyc0l4AdTXrEozmzEu9zP4zyFmQVuwy+jEO0rjOQiVJGklbaK53Lk+yO3BZkouAE4GLq2ptkjXAGuBUW09L0uizUkjq3f8F/oJmRqW/AN4AvJguZ12C0Zx5qZfZf9a/YNXSBzKPt5x9Pm+4cvEfXYOMcZDGdRYqSdLoqaqNwMb28V1Jrqa58XUMzWzFAOuAKeBUbD0tSSPPSiGpR1V1y/TjJH8HfLR96qxLkiRprLXd6p8AXAKsaCuMaGejfGi7Wletp0ex5fRSGbWYu2ltPVer7FH6G+YyauUsLSdWCkk9mp6Gu336q8DX2sfOuiRJksZWkt2ADwKvqqo7mwkq5151jmVbtZ4exZbTS2XUYu6mRfgph2zZqlX2qLe4HlQ5J9kZ+BywE8136Q9U1WsdV0vLmZVCUheSvJ+mWfReSTYArwVWJTmU5uJmPfA74KxLkiRpfCXZkaZC6Oyq+lC7+Jbpm2VJ9gY2tcttPa1xcw/w9Kra3ObC55N8AngujqulZcpKIakLVfX8ORa/axvrO+uSJEkaK2maBL0LuLqqzuh46QLgBGBt+/v8juW2ntbYqKoCNrdPd2x/CsfV0jJmpZAkSZKkbjwVeBFwZZIr2mWvoakMOi/JScANwHFg62mNpyQ7AJcBjwHeVlWXJNmucbXa/S44tpaz8M7kWFIz9VoeVgpJkiRJWlBVfZ65xwkCeMY829h6WmOlrdg8NMmDgQ8nOXgbqy/prMTOwjvTqI3ZNWy9lsf9lj4USZIkSZLGV1XdQdNNbDXtuFrQTEaD42ppGbFSSJIkSZKkBSR5SNtCiCS7AM8EvsF942rB1uNqHZ9kpySPxHG1NILsPiZJ81jZxbStc1m/9qgljkSSJEkjYG9gXTuu0P2A86rqo0m+gONqaZmyUkiSRkCvFVBnrd51iSORJEnSXKrqq8AT5lh+K46rpWXK7mOSJEmSJEkTyEohSZIkSZKkCWSlkCRJkiRJ0gSyUkiSJGkBSfZP8tkkVye5Kskr2+V7JrkoybXt7z06tjktyXVJrknyrOFFL0mSNLcFK4WSvDvJpiRf61jmBZAkSZokW4BTqurxwFOAlyU5EFgDXFxVBwAXt89pXzseOAhYDby9na1GkiRpZHTTUugsmouZTl4ASZKkiVFVG6vq8vbxXcDVwL7AMcC6drV1wLHt42OAc6rqnqq6HrgOOHygQUuSJC1gwSnpq+pzSVbOWnwMsKp9vA6YAk6l4wIIuD7J9AXQF5YoXkmSpKFqr4ueAFwCrKiqjdBUHCV5aLvavsAXOzbb0C6ba38nAycDrFixgqmpqa3W2bx585zLJ9W4lscph2zpabtxLQ9JUv8tWCk0j4FcAMFgT3K9nogHeRJescvi4+w1vl7KY5Dv5QWQJGnQkuwGfBB4VVXdmWTeVedYVnOtWFVnAmcCHHbYYbVq1aqt1pmammKu5ZNqXMvjxDUf62m7s1bvOpblIUnqv14rheazpBdAMNiTfq8n4vUvWLW0gWzDW84+nzdcubh/W6/x9VIeg3wvL4AkTaorb/reoj831689qqf3WtnLuaDH9xp1SXakqRA6u6o+1C6+Jcne7U2yvYFN7fINwP4dm+8H3Dy4aKXxM8jPPkmaFL3OPnZLe+GDF0CSJGncpWkS9C7g6qo6o+OlC4AT2scnAOd3LD8+yU5JHgkcAFw6qHglSZK60WulkBdAmijOwidJE++pwIuApye5ov05ElgLHJHkWuCI9jlVdRVwHvB14JPAy6rq3uGELkmSNLcF+yEleT/NoNJ7JdkAvJbmgue8JCcBNwDHQXMBlGT6AmgLXgBpfJwFvBV4T8ey6Vn41iZZ0z4/ddYsfPsAn07yWHNBkpavqvo8c3eTB3jGPNucDpzet6AmWC/diMCuROqvubrbnnLIlgWPVY9LScPUzexjz5/nJS+ANDGchU+SJEmSNG6WeqBpaZI4C19r1Gfgg95iHOSMhE5DLEmSJGnQrBSSlt7EzcI36jPwQW8xDnJGQqchliRJGm1J9qcZTuJhwI+BM6vqTUn2BM4FVgLrgedV1e3tNqcBJwH3Aq+oqguHELo0r14HmpbkLHySJEnSJNkCnFJVjweeArysHU90eqzRA4CL2+fMGmt0NfD2JDsMJXJpHlYKSb1zFj5JkiRpQlTVxqq6vH18F3A1zTARx9CMMUr7+9j28U/GGq2q64HpsUalkWH3MakLzsInSZIkaVo7Cc0TgEsY0FijgxxbczlwbM2Zei0PK4WkLjgLnyRJkiSAJLsBHwReVVV3JnMNKdqsOseynscaHeTYmsvBIMddXQ56LQ+7j0mSFpTk3Uk2Jflax7I9k1yU5Nr29x4dr52W5Lok1yR51nCiliRJWlpJdqSpEDq7qj7ULnasUS1bVgpJkrpxFs0AiZ0cVFGSJE2MNE2C3gVcXVVndLzkWKNatqwUkiQtqKo+B9w2a7GDKkqSpEnyVOBFwNOTXNH+HEkz1ugRSa4FjmifU1VXAdNjjX4SxxrVCHJMIUlSrwYyqCI4kOBsvQw02Wv5DXJAy17ey2NDkjQoVfV55h4nCBxrVMuUlUKSpKW2pIMqggMJztbLQJO9DjJ54pqPLXqbQb7XWat39diQBiTJu4GjgU1VdXC7bE/gXGAlsB54XlXd3r52GnAScC/wiqq6cAhhS5K2we5jkqReOaiiJE2Ws3B8OUkaK1YKSZJ65aCKkjRBHF9OksaP3cckSQtK8n5gFbBXkg3Aa2kGUTwvyUnADcBx0AyqmGR6UMUtOKiiJI2zgY0vN8jx1HoxV2zdxDzsGGebK+ZRH7vN8eWk3lkpJElaUFU9f56XHFRRkjSXJR9fbpDjqfVirnHRTjlky4IxDzvG2eaKeZAx9sKxB6XeWSkkSZIkqVe3JNm7bSXk+HKSBubKm7636Ekq1q89qk/RLF+OKSRJkiSpV44vJ0nLmC2FJEmSJC3I8eUkafxsV6VQkvXAXcC9wJaqOizJnsC5wEpgPfC8qrp9+8KUJEkariTvBo4GNlXVwe2yea97kpwGnERznfSKqrpwCGFLS8bx5SRp/CxF97GnVdWhVXVY+3wNcHFVHQBc3D6XJEla7s4CVs9aNud1T5IDgeOBg9pt3p5kh8GFKkmStLB+jCl0DLCufbwOOLYP7yFJkjRQVfU54LZZi+e77jkGOKeq7qmq64HrgMMHEackSVK3tndMoQI+laSA/9dOJ7miqjYCtLMQPHR7g5RGmd0opf5zdgmNsPmue/YFvtix3oZ22VaSnAycDLBixQqmpqa2Wmfz5s1zLp9UK3Zpps1erFEvw17+JvD4kCT1bnsrhZ5aVTe3F0AXJflGtxt2cwEEgz3J9XoiHuRJuJeLoF7jG+TFVi/vNWIXQE+rqu92PJ/uTrA2yZr2+anDCU2SNASZY1nNtWJ7U+1MgMMOO6xWrVq11TpTU1PMtXxSveXs83nDlYu/jF3/glVLH8wSWmzl97SzVu/q8SFJ6sl2VQpV1c3t701JPkzTLPqWJHu3d8v2BjbNs+2CF0Aw2IugXk/Eg7zA6OUiqNf4eimPQb7XiF8AHUMzOwc03QmmsFJIksbRfNc9G4D9O9bbD7h54NFJ0jKzssfvBYPipAMaNz2PKZRk1yS7Tz8Gfhn4GnABcEK72gnA+dsbpDTiprtRXta2gINZ3QkAu1FK0nia77rnAuD4JDsleSRwAHDpEOKTJC2ts3DSAY2R7WkptAL4cJLp/byvqj6Z5EvAeUlOAm4Ajtv+MKWRNvHdKEe9CyX0FuMgu5Q6joQ0+pK8n6YV6F5JNgCvBdYyx3VPVV2V5Dzg68AW4GVVde9QApckLZmq+lySlbMWz9dL4CeTDgDXJ5medOALAwlW6kLPlUJV9U3gZ+dYfivwjO0JSlpO7EY5+l0oobcYB9ml1HEkpNFXVc+f56U5r3uq6nTg9P5FJEkaEds96YA0LNs70LQ00dquk/erqrs6ulH+Ofd1J1iL3SglSZKkSdT1pAPd9CAY15kXezXISZiWg157EFgpJG0fu1FKkiRJk227Jx3opgfBuM682KtBTsK0HPTau8RKIWk72I1SkiRJmnjz9RK4AHhfkjOAfXDSAY0gK4UkSerQy1S4AOvXHrXEkUiSpFHjpAMaN1YKSZIkSZLUBScd0Li537ADkCRJkiRJ0uBZKSRJkiRJkjSBrBSSJEmSJEmaQFYKSZIkSZIkTSArhSRJkiRJkiaQlUKSJEmSJEkTyCnpJUmSpHmsXPOxnrZbv/aoJY5EkqSlZ6WQJEnSiLvypu9xYg+VE1ZMSJKkbbFSSJIkSZIkaQT02kL1rNW79rSdYwpJkiRJkiRNICuFJEmSJEmSJpDdxyRJkvQTDqwsSdLksKWQJEmSJEnSBLJSSJIkSZIkaQL1rftYktXAm4AdgHdW1dpe9uMUrFqulioHoLc8MAc0bEuZA9JyZR5o0pkDknmg0daXlkJJdgDeBjwbOBB4fpID+/Fe0igyBzTpzAHJPJDMAck80OjrV/exw4HrquqbVfVD4BzgmD69lzSKzAFNOnNAMg8kc0AyDzTiUlVLv9Pk14DVVfXb7fMXAU+uqpd3rHMycHL79HHANfPsbi/gu0se5PJledxnW2XxiKp6yCCD6dRNDrTLzYPFsyxmGsk8MAf6zvK4z0jmACx5Hvg/n8nymGm+8hinHIDl+X835sHwXDCZLI+ZejoX9GtMocyxbEbtU1WdCZy54I6SL1fVYUsV2HJnedxnxMtiwRwA86AXlsVMI1we5kAfWR73GfGyWLI8GPG/c+Asj5lGuDwm/lxgzIMx4jF7LugTy2OmXsujX93HNgD7dzzfD7i5T+8ljSJzQJPOHJDMA8kckMwDjbh+VQp9CTggySOTPAA4HrigT+8ljSJzQJPOHJDMA8kckMwDjbi+dB+rqi1JXg5cSDPt3rur6qoed7dgU9IJY3ncZ2TLYolzAEb4bx0Cy2KmkSwPc6DvLI/7jGxZeD3UV5bHTCNZHp4LAGMelJGN2XNBX1keM/VUHn0ZaFqSJEmSJEmjrV/dxyRJkiRJkjTCrBSSJEmSJEmaQCNdKZRkdZJrklyXZM2w4xmWJPsn+WySq5NcleSVw45pFCTZIcm/J/nosGPpF3PgPubB1syByWIObG0ScgDMg07mwdYmIQ+WYw4keXeSTUm+NuxYurUc8yvJzkkuTfKVNuY/G3ZM/bIc86BflmN+9ctS5O3IjimUZAfgP4AjaKbx+xLw/Kr6+lADG4IkewN7V9XlSXYHLgOOncSy6JTkD4DDgAdW1dHDjmepmQMzmQdbMwcmizmwtXHPATAPZjMPtjbuebBccyDJLwKbgfdU1cHDjqcbyzG/kgTYtao2J9kR+Dzwyqr64pBDW1LLNQ/6ZTnmV78sRd6Ockuhw4HrquqbVfVD4BzgmCHHNBRVtbGqLm8f3wVcDew73KiGK8l+wFHAO4cdSx+ZAx3Mg5nMgcljDsw0ITkA5sEM5sFME5IHyzIHqupzwG3DjmMxlmN+VWNz+3TH9mc0Wz1sn2WZB/2yHPOrX5Yib0e5Umhf4MaO5xsY8Q+lQUiyEngCcMmQQxm2NwKvBn485Dj6yRyYh3kAmAMTzRwAJiMHwDyYl3kATEYemANDsJzyq+1CeQWwCbioqkY+5h6YB1pQr3k7ypVCmWPZONb6di3JbsAHgVdV1Z3DjmdYkhwNbKqqy4YdS5+ZA3MwD8yBgUcxYsyBicoBMA/mZB5MVB6YAwO23PKrqu6tqkOB/YDDk4xjdyLzQNu0PXk7ypVCG4D9O57vB9w8pFiGru0j+0Hg7Kr60LDjGbKnAs9Jsp6m6eTTk7x3uCH1hTkwi3nwE+bAhDIHfmJScgDMg62YBz8xKXlgDgzQcs6vqroDmAJWDzeSvjAPNK/tzdtRHmj6/jSDaT0DuIlmMK3fqKqrhhrYELQDqK0DbquqVw05nJGSZBXwh2M6sKI50ME8mJs5MDnMgbmNcw6AeTCbeTC3cc6D5ZwDbVeOjy6XgXCXY34leQjwo6q6I8kuwKeA11fVWM3Gt5zzoF+WW371y1Lk7ci2FKqqLcDLgQtpBks6b4IP+qcCL6K5A3RF+3PksINSf5kDWzEPJow5sBVzYAKZB1sxDybMcs2BJO8HvgA8LsmGJCcNO6YuLMf82hv4bJKv0lSUXDRuFUKwfPOgX5ZpfvXLduftyLYUkiRJkiRJUv+MbEshSZIkSZIk9Y+VQstMkpVJqu1XOt86m5M8apBxSUspyfokz+xx23ck+ZOljknqVZLHJfn3JHclecU21nt4+/m9Q/t8KslvDy5SaXDaa5nH9GG/PZ8/pH7p9jwgqTdJPpHkhHleW/D786SzYHrUzvTw21X16SHHMQW8t6reOb2sqnYbXkSadEleBzymql44gPc6kSYPf2F6WVW9tN/vKy3Sq4GpqnrCtlaqqhsAP78lafx0dR6Q1JuqevawY1jObCnUB9ZCSpI6PAIY2GCQaXh+l6TRMdDzgCQthheNPUjyD8DDgX9qm/q/um2SdlKSG4DPtOv9Y5JvJ/leks8lOahd/pR2+Q4d+/zVdtR8ktwvyZok/5nk1iTnJdlzjjhOB/4H8NY2jre2y3/SJDvJWUne3jap25zkX5M8LMkbk9ye5BtJntCxz32SfDDJd5JcbxNXbUuSU5Pc1DaHvibJUcBrgF9vj7evtOvNaM6f5HVJ3tvx/EVJvtUe7/+rY/nDknw/yU93LPu59vg8BHgH8PPte93Rvn5Wkr9sH69qZyR4dZJNSTYmOTbJkUn+I8ltSV7Tse+uck/qVpLPAE/jvs/pV7ZdCO5McmPbsm563XmbN8+RMzPWbbuanZ7kX4HvA49K8t+SXNQe59ckeV7H9kcm+Xqbuzcl+cM+FoMmwBzng2ck2SHJa9rP1LuSXJZk/47Nnpnk2vZ65G1J0u7rfkn+uD0vbEryniQP6niv5yS5Kskd7bH/+HliOjzJl9t8uyXJGe3yVUk2zFr3J+epNt8+kOTcNu7Lk/zskheaJkKP54Hfal+7PclLkzwpyVfbY/6tHes/Jsk/p/mu8d0k587az/071v1Jl+QkJ6b5TvCWdttvJHnGwApFmkf7WXxae41ye5K/T7Jzkj2SfDTNd4Db28f7dWzXeXzvkORv2pz4JnDU0P6gZcJKoR5U1YuAG4Bfabtqnde+9EvA44Fntc8/ARwAPBS4HDi73f6LwN3A0zt2+xvA+9rHrwCObfe3D3A78LY54vhfwL8AL6+q3arq5fOE/Dzgj4G9gHtopu+7vH3+AWD6Iul+wD8BXwH2BZ4BvCrJs+bYpyZcksfRTI35pKranea4/wbwV8C57TG54EV0kgOB/0szleI+wE8D+wFU1beBKZpjeNoLgXOq6krgpcAX2vd68Dxv8TBgZ5pj+k+Bv2v38XM0lap/mvvG4Ooq96RuVdXT6ficpvl8/U3gwTQXKb+b5NglersXAScDuwPfAS6iOa88FHg+8Pa0NyeAdwG/0+buwbQ3M6RezHM+WA/8Ac2xdyTwQODFNJWW044GngT8LM3n/PT1xontz9OAR9F0q5y+8fVY4P3Aq4CHAB+nuUn3gDlCexPwpqp6IPBo7rte68YxwD8Ce9Lk0UeS7LiI7SWg5/PAk2m+Q/w68EbgfwHPBA4Cnpfkl9r1/gL4FLAHzbXTWxYR2pOBb9J8H3gt8KF4I0yj4QU054NHA4+l+R57P+DvaVrdPRz4L9rzwhxeQnN+eQJwGPBrfY532bNSaGm9rqrurqr/Aqiqd1fVXVV1D/A64Gc77nS9n+ZCiSS701wwvb997XeA/1VVGzq2/bX03i3tw1V1WVX9APgw8IOqek9V3QucS5Mw0FyYPaSq/ryqflhV36T5An18j++r8XYvsBNwYJIdq2p9Vf1nD/v5NeCjVfW59nj/E+DHHa+vo6nEIU3ruucD/7CI/f8IOL2qfgScQ3Px86Y2N6+iac79M+26S5170gxVNVVVV1bVj6vqqzSf+7+00HZdOquqrqqqLcBqYH1V/X1Vbamqy4EPct+F0Y9ocveBVXV7+7rUq/nOB78N/HFVXVONr1TVrR3bra2qO9rxtD4LHNoufwFwRlV9s6o2A6cBx7efxb8OfKyqLmo/1/8G2AX473PE9SPgMUn2qqrN7U25bl1WVR9o3+MMmpsLT1nE9tKcujwP/EVV/aCqPkVzI/n9VbWpqm6iqWCavnb/Ec2X5H3a9T+/iFA2AW+sqh9V1bnANdiiQqPhrVV1Y1XdBpwOPL+qbq2qD1bV96vqrnb5fNdPz6M5tqf38dcDinvZslJoad04/aBttra2bTJ9J80dM2i+kEJz1+m5SXYCngtcXlXfal97BPDhtonoHcDVNBdcK3qM65aOx/81x/PpgU0fAewz/b7te79mO95XY6yqrqO5U/s6YFOSc5Ls08Ou9qEjd6rqbqDzS8P5NF80HgUcAXyvqi5dxP5vbStAoTneYds5sJS5J82Q5MlJPts2f/4eTWu3vRbarks3djx+BPDkWZ/nL6BpOQfw/9HcjPhW2/Xg55coBk2gbZwP9ge2dbPg2x2Pv899n8X7AN/qeO1bNJOjrJj9WlX9mObY33eO/Z9Ec5f5G0m+lOTo7v+qGeelHwMb2veWtkuX54Fur91fDQS4NE2XyhcvIpSbqqo6nn8Lj3GNhs7rmW/RfD/9qST/L0234juBzwEPTsdwLB32mWMf2gYrhXpXCyz7DZqmx88EHgSsbJcHoKq+TnOAPpuZXcegOYifXVUP7vjZub070E0cvboRuH7W++5eVUcu4XtojFTV+9qZvx5Bcyy+nrmPybuBn+p4/rCOxxtpvjgAkOSnaLqQTb/HD2ia/L+ApntMZyuhpTz+YXG5J/XifcAFwP5V9SCacbHSxXbbyqFpnflwI/DPs47l3arqdwGq6ktVdQxN17KPsLhuNdJW5jkf3EjT/H+xbm73M+3hwBaaL8YzXksSmnPIVp/TVXVtVT2f5jh/PfCBJLsyK5/aLxUPmbV553npfjRdc27u4W+RZuv1PLCVqvp2Vb2kqvahae389jTjit7drrKt88a+bf5Mezge4xoNnWPPTR+XpwCPA57cdgn+xfb1uXJnxneLdh/aBiuFencLTT/3+exOM37PrTQfyH81xzrvoxnD5Bdp+q1PewdwepJHACR5SJJjeoxjMS4F7kwzWOQubWung5M8aYn2rzGS5HFJnt62dvsBzZ2re2mOyZWZOfvRFTRN/3dMMrtv7weAo5P8QjsmxJ+z9WfTe2jGl3gO8N6O5bcA+80zlkQvFpN7Ui92B26rqh8kOZzmpkA3rgB+McnD227Ipy2w/keBx6YZxH3H9udJSR6f5AFJXpDkQW3XmDtpclfqyTbOB+8E/iLJAWn8TDomDtiG9wO/n+SRSXbjvrHqttBUYB6VZiDrHWm+KNwD/Nsccb0wyUPalj53tIvvBf4D2DnJUe0+/pim+1unn0vy3LbL2qva91hM9zNpPr2eB7aS5LjcN9ju7TQVsvdW1XdoKkpf2F7Pv5itK2gfCryiPT8cRzMu6sd7jUVaQi9Lsl87xtVraIY72Z3m3HJHu/y129j+PJpje78kewBr+h7xMmelUO/+Gvjjtkn+XINXvYemJdBNwNeZ+0Li/cAq4DNV9d2O5W+iuYPwqSR3tds+eZ443kQz5sntSd7cw9/xE20Xm1+h6dN/PfBdmgu6B21jM02unYC1NMfJt2kuLl7DfRWctyaZHqfkT2guRm4H/oyOlnHtuD4va5dtbNeZMStMVf0rzThDl1fV+o6XPkMzJtC3k3TmUK8Wk3tSL/4n8Oft8fWndNlCp6ouorko+ipwGU2lz7bWvwv4ZZox4W6mydHXc98X3xcB69sm2C+lHbdL6tF854MzaI7xT9FUPr6LZvyfhbybplXo52iuR34A/B5AVV1Dc7y+pX2/X6GZ+OOHc+xnNXBVks00n+/Ht+OufI8mF99Jc512N7POOzRdl3+d5pz0IuC5bSWqtL16Og/M40nAJe0xfgHwyqq6vn3tJcAf0dygPoitK04voRnM+rs047P82qwxv6RheR/NeeOb7c9f0gy4vgvN8fpF4JPb2P7vgAtpBnW/HPhQH2MdC5nZlVSSRlOaKV3fV1XvHHYskqTxlWaK8MdUlZWlGktJTgR+u+3yKY2MJOtpjs1PDzuWSeKMOpJGXtuF8Yk043RJkiRJkpaA3cckjbQk64BPA69qu8RIkiRJkpaA3cckSZIkSZImkC2FJEmSupDkwUk+kOQbSa5O8vNJ9kxyUZJr2997dKx/WpLrklyT5FnDjF2SJGkuI9FSaK+99qqVK1fO+drdd9/NrrvuOtiAtpMxD8a2Yr7sssu+W1UPGXBI28U8GL5xi3m55YE5MHzjFvNS50DbnfVfquqdSR4A/BTNLFu3VdXaJGuAParq1CQH0swyejiwD0032Me2M33Oa748WI7/m36yPGaarzyW23kAxu9c0C+WxUzjdD0Engu6ZXnM1PO5oKqG/vNzP/dzNZ/Pfvaz8742qox5MLYVM/DlGoFjezE/5sHwjVvMyy0PzIHhG7eYlzIHgAfSTI+eWcuvAfZuH+8NXNM+Pg04rWO9C4GfX+h95suD5fi/6SfLY6b5ymO5nQdqDM8F/WJZzDRO10PluaBrlsdMvZ4LnH1MkiRpYY8CvgP8fZKfBS4DXgmsqKqNAFW1MclD2/X3Bb7Ysf2GdtlWkpwMnAywYsUKpqamtlpn8+bNcy6fVJbHTIMsjyQ7AF8Gbqqqo5PsCZwLrATWA8+rqtvbdU8DTgLuBV5RVRcOJEhJUtesFJIkSVrY/YEnAr9XVZckeROwZhvrZ45lc/bZr6ozgTMBDjvssFq1atVW60xNTTHX8kllecw04PJ4JXA1Tes5aPLg4rqvC+UaYLoL5fHAQbRdKJMs2IVSkjRYDjQtLSDJzkkuTfKVJFcl+bN2uYOLStLk2ABsqKpL2ucfoKkkuiXJ3gDt700d6+/fsf1+wM0DilXqiyT7AUcB7+xYfAywrn28Dji2Y/k5VXVPVV0PXEczxpa0rDnpgMbNyLcUuvKm73Himo8terv1a4/qQzSaUPcAT6+qzUl2BD6f5BPAc/HOGAAr58jRUw7ZsmDumqfqVi/nAo8vLaWq+naSG5M8rqquAZ4BfL39OQFY2/4+v93kAuB9Sc6gORccAFza6/t7PaQR8Ubg1cDuHcsG0oUS7DbYaZzL4sqbvrfobR75oB0GWR5vAj5ZVb82a9KBvn8v8Fygfhj5SiFp2NrBuTa3T3dsf4rmDtiqdvk6YAo4lY47Y8D1SabvjH1hcFFLkvrg94Cz2y8B3wR+i6bV9XlJTgJuAI4DqKqrkpxHU2m0BXjZON8c0PhLcjSwqaouS7Kqm03mWNZzF0qw22CncS6LXio9zlq960DKI8kDgV8ETgSoqh8CP0zi9wItW1YKSV1oB1W8DHgM8LZ2PAnvjLVOOWTLVstW7DL38k6j9jeNejnPZTnGLC1XVXUFcNgcLz1jnvVPB07vZ0zSAD0VeE6SI4GdgQcmeS9tF8r2WsgulBp3Q510oJvr67mM67Wi18Ez9VoeVgpJXWjv7h6a5MHAh5McvI3VJ+7O2Fx3dE45ZAtvuHLbHzHrX7CqTxH1ZtTLeS7LMWZJ0vJTVacBpwG0LYX+sKpemOT/MIAulNKIGOqkA285+/wFr6/nMmrX3EvF6+CZei0PB5qWFqGq7qBpDroaBxeVJElaCxyR5FrgiPY5VXUVMN2F8pPYhVLjwUkHNHasFJIWkOQhbQshkuwCPBP4Bs0dsBPa1WbfGTs+yU5JHol3xiRJ0hipqqmqOrp9fGtVPaOqDmh/39ax3ulV9eiqelxVfWJ4EUtLo6q+DdyY5HHtoulJB/xeoGXL7mPSwvYG1rXjCt0POK+qPprkCzi4qCRJkjRJnHRAY8VKIWkBVfVV4AlzLL8VBxeVJEmSJoaTDmjc2H1MkiRJkiRpAlkpJEmSJEmSNIGsFJIkSZIkSZpAVgpJkiRJkiRNICuFJEmSJEmSJpCVQpIkSZIkSRPISiFJkiRJkqQJZKWQJEmSJEnSBFqwUijJzkkuTfKVJFcl+bN2+Z5JLkpybft7j45tTktyXZJrkjyrn3+AJEmSJEmSFq+blkL3AE+vqp8FDgVWJ3kKsAa4uKoOAC5un5PkQOB44CBgNfD2JDv0IXZJkiRJkiT1aMFKoWpsbp/u2P4UcAywrl2+Dji2fXwMcE5V3VNV1wPXAYcvZdCSJEmSJEnaPvfvZqW2pc9lwGOAt1XVJUlWVNVGgKramOSh7er7Al/s2HxDu2z2Pk8GTgZYsWIFU1NTc773il3glEO2dPfXdJhvf4OwefPmob5/L4xZkiRJkqTJ0lWlUFXdCxya5MHAh5McvI3VM9cu5tjnmcCZAIcddlitWrVqzp295ezzecOVXYU5w/oXzL2/QZiammK+v2dUGbMkSZIkSZNlUbOPVdUdwBTNWEG3JNkboP29qV1tA7B/x2b7ATdvb6CSJEmSJElaOt3MPvaQtoUQSXYBngl8A7gAOKFd7QTg/PbxBcDxSXZK8kjgAODSJY5bkiRJkiRJ26GblkJ7A59N8lXgS8BFVfVRYC1wRJJrgSPa51TVVcB5wNeBTwIva7ufSZIkSVqmkuyc5NIkX0lyVZI/a5fvmeSiJNe2v/fo2Oa0JNcluSbJs4YXvSRpLgsO1lNVXwWeMMfyW4FnzLPN6cDp2x2dJEmSpFFxD/D0qtqcZEfg80k+ATwXuLiq1iZZA6wBTk1yIHA8cBCwD/DpJI/1hrEkjY5FjSkkSZpsSXZI8u9JPto+9+6wJE2Iamxun+7Y/hRwDLCuXb4OOLZ9fAxwTlXdU1XXA9cBhw8uYknSQhY/rZckaZK9ErgaeGD7fA3eHZakiZFkB+Ay4DHA26rqkiQrqmojQFVtTPLQdvV9gS92bL6hXTZ7nycDJwOsWLGCqampOd978+bN8742aca5LE45ZMuitxnn8pD6zUohSVJXkuwHHEXTPfgP2sXHAKvax+toZqg8lY67w8D1SabvDn9hgCFLkpZYW7l/aDsRzYeTHLyN1TPXLubY55nAmQCHHXZYrVq1as6dTU1NMd9rk2acy+LENR9b9DZnrd51bMtD6jcrhSRJ3Xoj8Gpg945lA7k7vGKXxd85HPYdw+V419KYJXWrqu5IMgWsBm5Jsnd7Htgb2NSutgHYv2Oz/YCbBxupJGlbrBSSJC0oydHApqq6LMmqbjaZY1nPd4ffcvb5vOHKxZ2y1r9g7n0NynK8i2vMC2u7znwZuKmqjk6yJ3AusBJYDzyvqm5v1z0NOAm4F3hFVV04sEClPkjyEOBHbYXQLsAzgdcDFwAn0MxGfAJwfrvJBcD7kpxB05X4AODSgQcuSZqXA01LkrrxVOA5SdYD5wBPT/Je2rvDAN4d1oSYHldr2vS4WgcAF7fPmTWu1mrg7W2FkrSc7Q18NslXgS8BF1XVR2kqg45Ici1wRPucqroKOA/4OvBJ4GWOLSdJo8VKIUnSgqrqtKrar6pW0nzR/UxVvZD77g7D1neHj0+yU5JH4t1hjYGOcbXe2bHYWZc0Marqq1X1hKr6mao6uKr+vF1+a1U9o6oOaH/f1rHN6VX16Kp6XFV9YnjRS5LmYvcxaRm48qbvLXrQvfVrj+pTNNIMa4HzkpwE3AAcB83d4STTd4e34N1hjYc3ssTjakmSJA2TlULSApLsD7wHeBjwY+DMqnqT40hoUlXVFM0sY1TVrcAz5lnvdJqZyqRlr1/jarX7XnDA9V4GW4fhD7jeLw4wPpPlIQ2W48tpnFgpJC1sC3BKVV2eZHfgsiQXASfSjCOxNskamnEkTp01jsQ+wKeTPNZWEpK0rE2Pq3UksDPwwM5xtbZn1qVuBlzvZbB1GP6A6/2yHAdF7yfLQxq46fHlHtg+nx5fzu8FWnYcU0haQFVtrKrL28d30ZwA9sVxJCRpYjiuliQJHF9O48eWQtIiJFkJPAG4hCUYR6KbLgPQW7eBQTYjnyu2bmIetabuy7H5/XKMWRozjqslSZPljfRhfDm7Ei+e18Ez9VoeVgpJXUqyG/BB4FVVdWcy13ARzapzLJtzHIluugxAb90GBtllYK5BsE85ZMuCMY9at4bl2Px+OcYsLXeOqyVJk6mf48vZlXjxvA6eqdfysFJI6kKSHWkqhM6uqg+1i7d7HAlJkiR1x9lYNQL6Nr6cNCyOKSQtIE2ToHcBV1fVGR0vOY6EJEmSNCEcX07jyJZC0sKeCrwIuDLJFe2y1+A4EpIkSZL8XqBlzEohaQFV9Xnm7g8MjiMhSZIkTRzHl9O4sPuYJEmSJEnSBLJSSJIkSZIkaQIt2H0syf7Ae4CHAT8GzqyqNyXZEzgXWAmsB55XVbe325wGnATcC7yiqi7sS/QjYuWsWRBOOWTLgjMjOBOCJEmSJEkapm5aCm0BTqmqxwNPAV6W5EBgDXBxVR0AXNw+p33teOAgYDXw9iQ79CN4SZIkSZIk9WbBSqGq2lhVl7eP7wKuBvYFjgHWtautA45tHx8DnFNV91TV9cB1wOFLHLckSZIkSZK2w6JmH0uyEngCcAmwoqo2QlNxlOSh7Wr7Al/s2GxDu2z2vk4GTgZYsWIFU1NTc77nil2a7liLNd/++mF2fN3EPMj4urF58+aRi2khyzFmSZIkSZJGRdeVQkl2Az4IvKqq7kzmm6F7zqm7a6sFVWcCZwIcdthhtWrVqjl39pazz+cNVy6q7gqA9S+Ye3/9MHv8oFMO2bJgzIOMrxtTU1PM9z8YVcsxZkmSJEmSRkVXs48l2ZGmQujsqvpQu/iWJHu3r+8NbGqXbwD279h8P+DmpQlXkiRJ0jAk2T/JZ5NcneSqJK9sl++Z5KIk17a/9+jY5rQk1yW5Jsmzhhe9JGkuC1YKpWkS9C7g6qo6o+OlC4AT2scnAOd3LD8+yU5JHgkcAFy6dCFLkiRJGgInoJGkMdNNS6GnAi8Cnp7kivbnSGAtcESSa4Ej2udU1VXAecDXgU8CL6uqe/sSvSRJkqSBcAIaSRo/Cw7WU1WfZ+5xggCeMc82pwOnb0dckiRJkkbUcpmAZlwnJRnnCVd6mWRonMtD6rfFj+AsSZIkaWItpwloRm1yl6UyzhOuzJ7Epxtnrd51bMtD6reuBpqWJEmSJCegkaTxYqWQJEmSpAU5AY0kjR+7j0mSJEnqxvQENFcmuaJd9hqaCWfOS3IScANwHDQT0CSZnoBmC05AI0kjx0ohSZIkSQtyAhpJGj92H5MkSZIkSZpAVgpJkiRJkiRNICuFJEmSJEmSJpCVQpIkSZIkSRPISiFJkiRJkqQJZKWQJGlBSfZP8tkkVye5Kskr2+V7JrkoybXt7z06tjktyXVJrknyrOFFL0mSJGkuVgpJkrqxBTilqh4PPAV4WZIDgTXAxVV1AHBx+5z2teOBg4DVwNuT7DCUyCVJkiTNyUohSdKCqmpjVV3ePr4LuBrYFzgGWNeutg44tn18DHBOVd1TVdcD1wGHDzRoaQnZWk6SJI2j+w87AEnS8pJkJfAE4BJgRVVthKbiKMlD29X2Bb7YsdmGdtnsfZ0MnAywYsUKpqam5nzPFbvAKYdsWVSc8+1rUDZv3jz0GBbLmLdpurXc5Ul2By5LchFwIk1rubVJ1tC0ljt1Vmu5fYBPJ3lsVd07iGAlSZK6YaWQ1IUk7waOBjZV1cHtsj2Bc4GVwHrgeVV1e/vaacBJwL3AK6rqwiGELS25JLsBHwReVVV3Jpl31TmW1VYLqs4EzgQ47LDDatWqVXPu7C1nn88brlzcKWv9C+be16BMTU0x398zqox5fm3l53QF6F1JOlvLTQewDpgCTqWjtRxwfZLp1nJf6HuwkqS+SLI/8B7gYcCPgTOr6k1+L9ByZqWQ1J2zgLfSnASmTY+l4t1hTYQkO9JUCJ1dVR9qF9+SZO+2ldDewKZ2+QZg/47N9wNuHly0Uv8sZWu5dn8LtpjrpbUcDL/FXL8sx1Zt/WR5SANjq1GNHSuFpC5U1efaLwGdvDusiZGmSdC7gKur6oyOly4ATgDWtr/P71j+viRn0FwEHQBcOriIpf5Y6tZy0F2LuV5ay8HwW8z1y3Js1dZPloc0GLYa1TiyUkjq3UDuDsPoj6cyV2zdxDxqdzWX453WAcb8VOBFwJVJrmiXvYamMui8JCcBNwDHAVTVVUnOA75Oc1ftZd4V03JnazlJ0jRbjQ7fcrx276dey8NKIWnpLendYRj98VROXPOxrZadcsiWBWMeZIwr54hxtlMOuZc3fP7unzxfv/aofoa0JAY4nsrnmfvYBnjGPNucDpzet6CkAbK1nCRpmq1GR4OtJGfqtTwWnJI+ybuTbErytY5lTr8qtXeHAbw7LEljb7q13NOTXNH+HElTGXREkmuBI9rnVNVVwHRruU9iazlJGgvbajXavu73Ai0rC1YK0Qywu3rWsukBdg8ALm6fM2sgrdXA25PssGTRSqNl+u4wbH13+PgkOyV5JN4dlqRlr6o+X1Wpqp+pqkPbn49X1a1V9YyqOqD9fVvHNqdX1aOr6nFV9Ylhxi9J2n5dtBoFvxdomVmwUqiqPgfcNmvxMTQDaNH+PrZj+TlVdU9VXQ9MD6QlLWtJ3k8zINzjkmxox0/x7rAkSZI0OWw1qrHT65hC2z2QlrScVNXz53nJsVQkSdJESPJu4GhgU1Ud3C7bEzgXWAmsB55XVbe3r50GnATcC7yiqi4cQtjSknGMRY2jpR5ouuuBtPo56xIMd+YlZ10ajOUYsyRJ0jJ2FvBW4D0dy6aHlVibZE37/NRZw0rsA3w6yWNtJSFJo6XXSqHtnn61n7MuwXBnXhq1WZe6sRxHbl+OMUuSJC1XVfW5dhruTscAq9rH64Ap4FQ6hpUArk8yPazEFwYSrCSpK71WCjn9qiRJkqTtHlainz0IxrVV+Ti3mO+ll8g4l4fUbwtWCrUD7K4C9kqyAXgtTWXQee1guzcAx0EzkFaS6YG0tuBAWpIkSdIk6npYiX72IBi11vlLZZxbzM/uhdGNs1bvOrblIfXbgp+qDrArSZIkaR7bPayEJGl4FpySXpIkSZLmMT2sBGw9rMTxSXZK8kgcVkKSRtJSzz4mSZIkaQw5rIQkjR8rhSRJkiQtyGElJGn82H1MkiRJkiRpAlkpJEmSJEmSNIGsFJIkSZIkSZpAVgpJkiRJkiRNICuFJEmSJEmSJpCzj02AlWs+tuA6pxyyhRNnrbd+7VH9CkmSJEmSJA2ZLYUkSZIkSZImkC2FJElaArNbZc7VAnM2W2RKkiRpmKwUkqQR0E03z7mctXrXJY5EkjQsngskSYNm9zFJkiRJkqQJZKWQJEmSJEnSBLJSSJIkSZIkaQJZKSRJkiRJkjSBHGhakiRJkiRpBAx60gFbCkmSJEmSJE0gWwppJPRSG+r0q5IkSZIk9c6WQpIkSZIkSROoby2FkqwG3gTsALyzqtb2672kUWQOaNKZA6OlmxaZpxyyhRNnrbd+7VH9Cmkr49hq1DzQpDMHJPNAo60vLYWS7AC8DXg2cCDw/CQH9uO9pFFkDmjSmQOSeSCZA5J5oNHXr+5jhwPXVdU3q+qHwDnAMX16L2kUmQOadOaAZB5I5oBkHmjEpaqWfqfJrwGrq+q32+cvAp5cVS/vWOdk4OT26eOAa+bZ3V7Ad5c8yP4y5sHYVsyPqKqHDDKYTt3kQLvcPBgt4xbz0PLAHACMeVBGMgdgyfNgOf5v+snymGm+8hinHAD/750si5k8F0wmy2Omns4F/RpTKHMsm1H7VFVnAmcuuKPky1V12FIFNgjGPBgjHvOCOQDmwagx5iVlDhjzQIx4zEuWByP+dw6c5THTCJfHxJ8L+sWymGnEy8NzQZ9YHjP1Wh796j62Adi/4/l+wM19ei9pFJkDmnTmgGQeSOaAZB5oxPWrUuhLwAFJHpnkAcDxwAV9ei9pFJkDmnTmgGQeSOaAZB5oxPWl+1hVbUnycuBCmmn33l1VV/W4uwWbko4gYx6MkY15iXMARvhv3QZjHoyRjNkcAIx5UEY2Zq+H+srymGkky8NzQV9ZFjONbHl4Lugry2OmnsqjLwNNS5IkSZIkabT1q/uYJEmSJEmSRpiVQpIkSZIkSRNoZCuFkrw7yaYkXxt2LN1Ksn+Szya5OslVSV457JgWkmTnJJcm+Uob858NO6ZuJdkhyb8n+eiwY+kHc2AwzIHRlmR1kmuSXJdkzbDj6Ya5OxjLOXe3ZaFjPo03t69/NckThxHnoHRRHquSfC/JFe3Pnw4jzkFY6LNlXI4Nc2Amc+A+k5IDYB7MZh7cpy95UFUj+QP8IvBE4GvDjmURMe8NPLF9vDvwH8CBw45rgZgD7NY+3hG4BHjKsOPqMvY/AN4HfHTYsfTp7zMHBhOzOTCiPzSDMf4n8CjgAcBXRv14auM2dwcT87LN3W38TQse88CRwCfav/8pwCXDjnvI5bFqXD8D5yiPbX62jMOxYQ70VB7mwJgdG+ZBT+VhHmzHsTGyLYWq6nPAbcOOYzGqamNVXd4+vgu4Gth3uFFtWzU2t093bH9GfvTxJPsBRwHvHHYs/WIODIY5MNIOB66rqm9W1Q+Bc4BjhhzTgszdwViuubuAbo75Y4D3tH//F4EHJ9l70IEOyLL8DOiXLj5bxuHYMAdmMgc6TEgOgHkwm3nQoR95MLKVQstdkpXAE2juXI60NF1QrgA2ARdV1cjHDLwReDXw4yHHoXmYA333RsY/B/YFbux4voERr6gYB+buUHVzzE9SXnT7t/58243wE0kOGkxoI2kcjg1zYCZzYHHG5dgwD2YyDxZn0ceGlUJ9kGQ34IPAq6rqzmHHs5CqureqDgX2Aw5PcvCQQ9qmJEcDm6rqsmHHormZA/01QTmQOZYt95YgI83cHbpujvlJyotu/tbLgUdU1c8CbwE+0u+gRtg4HBvmwEzmwOKMy7FhHsxkHizOoo8NK4WWWJIdaS6oz66qDw07nsWoqjuAKWD1cCNZ0FOB5yRZT9N88OlJ3jvckDTNHBiIScmBDcD+Hc/3A24eUixjz9wdCd0c85OUFwv+rVV153Q3wqr6OLBjkr0GF+JIGYdjwxyYyRxYnHE5NsyDmcyDxVn0sWGl0BJKEuBdwNVVdcaw4+lGkockeXD7eBfgmcA3hhrUAqrqtKrar6pWAscDn6mqFw45LGEODMoE5cCXgAOSPDLJA2j+1guGHNNYMndHRjfH/AXAb7azizwF+F5VbRx0oAOyYHkkeVh7/JLkcJpr21sHHuloGIdjwxyYyRxYnHE5NsyDmcyDxVn0sXH/wcS1eEneTzOK+F5JNgCvrap3DTeqBT0VeBFwZTvGAcBr2trKUbU3sC7JDjTJc15Vje301suJOTAw5sCIqqotSV4OXEgz88S7q+qqIYe1IHN3YMYud+c75pO8tH39HcDHaWYWuQ74PvBbw4q337osj18DfjfJFuC/gOOraiy7UMz12UIzwPrYHBvmwEzmwEyTkANgHsxmHszUjzzImJaVJEmSJEmStsHuYyMkycOTbG7vei607juS/Mk2Xn9NknGeqlqSlpUk65M8c47l/yPJNV3uY1V7V0gaeUmuSrJq2HFImttizj/SUkryuCT/nuSuJK8YYhxeVzHC3ccmQZpBYn+7qj4NUFU3ALt1s21VvbRjP6uA91bVfh2v/9VSxipJ6o+q+hfgccOOQ1pqVdXVlMCzr4cGLUkBB1TVdcN4f2lQZh/rnn80RK8GpqrqCcMORLYUkjTiumk51+V+rASXOpgTGgftQJpez2pk+VkrzekRQN/HiTT/uuNJdIkkWZPkP9smcF9P8qsdr70kydUdrz0xyT8ADwf+qe0y9uokK5NUkvsnOT7Jl2e9x+8nuaB9fFaSv0yyK/AJYJ92P5uT7JPkdemYojrJU5L8W5I7knylszl3khOTfLON7/okL+hvaWkStF1l/jDJV5N8L8m5SXZuj7fPz1q3kjymfXxWkv+b5ONJ7gaeluTINnfuSnJTkj/s2PboJFe0x/a/JfmZWTGcmuSrwN1J/ijJB2e991uSvLGvhSHd59A5cmJG0+X2HDHdpPof2/X+snMnSU5JsinJxiS/1bF8pyR/k+SGJLek6Wq8S/vaqiQb2pz4NvD3A/urNZHaz+Bnttck5yV5T3tcX5XksHadra6H2uXbum6ZSnJ6kn+lGUTzUe155KVJrk1ye5K3Jc1MNO02L26vxW5PcmGSR7TLP9eu8pX2/X99IIWjsTbH9cdz2uP+jvb4ffysdf+oPTfcneRdSVYk+USbL59OskfH+v+Y5NvteeRzSQ7qeO2s9tj/WLvtJUke3b621bE+x/ln/yQfSvKdJLcmeWv/S0uTJslngKcBb22PxVe21z13Jrkxyes61p3+fvxb7Wu3t5/1T2pz5o7O4zTN94x/TfK3SW4DXretayO1qsqfJfgBjgP2oalo+3XgbpqZUY4DbgKeBAR4DPCIdpv1wDM79rESKJpufT8F3EXTxHP69S/RjKQOcBbwl+3jVcCGWfG8jqZLGcC+NFPyHdnGd0T7/CHArsCdwOPadfcGDhp2efqz/H/a4/vSNi/2BK4GXgqcCHx+1roFPKZ9fBbwPZrZkO4H7AxsBP5H+/oewBPbx08ENgFPppmN4IT2fXfqiOEKYH9gl/b4vht4cPv6/dvtf27Y5eXP+P9sIyd+8hkOPAD4FvBKmpkkngv8cNbn/Rbgz9vXj6T5UrxH+/obaaYi3RPYHfgn4K9nbft6YCdgl2GXiT/j/dMe889sr0l+0B6vOwB/DXxx9nodz+e9bmlfnwJuAA5qP8d3bM8jHwUeTFPJ9B1gdbv+sTSzsDy+Xf+PgX/reL+fnIP88WcpfmZdf/xse+1xRHusvro9Hh/Qse4XgRXtsb8JuBx4QvtZ/RmaWSyn9/3i9vN9p/Yz/4qO184CbgMOb4/1s4FzOl6fcazPOv/sAHwF+Fua7wc7A78w7LL0Zzx/2s/x324frwIOaT/vfwa4BTi2fW1le9y+oz0mf7k9n3wEeGhHzvxSu/6JNNc6v9fmwC4sfG20YZB/+yj+2FJoiVTVP1bVzVX146o6F7iW5gP5t4H/XVVfqsZ1VfWtLvb3feB84PkASQ4A/hvNAb1YLwQ+XlUfb+O7CPgyzcUWwI+Bg5PsUlUbaxlM+axl481tXtxG8wF8aJfbnV9V/9oerz8AfgQcmOSBVXV7VV3ervcS4P9V1SVVdW9VrQPuAZ4yK4Ybq+q/qmoj8DmaylqA1cB3q+qy7fw7pW4tlBNPobmIeXNV/aiqPkRTkdTpR8Cft69/HNgMPK5tFfES4Per6raqugv4K+D4jm1/TPPl4p6q+q8l/+uk+X2+vQ65F/gHmi/K81nougXgrKq6qqq2VNWP2mVrq+qOasZo/Cz35dfv0HwBuLqqttDkxaHTrYWkPnlzVd0IPAf4WFVd1B6rf0PzRfW/d6z7lqq6papuAv4FuKSq/r2q7gE+TFNBBEBVvbuq7mpfex3ws0ke1LGvD1XVpe2xfjbdX3sdTnPT4o+q6u6q+kFVfX6hjaTtVVVTVXVl+3n/VeD9wC/NWu0v2mPyUzSVrO+vqk0dOdM5NtHNVfWWNgd+wMLXRhPPSqElkuQ3c18XljuAg4G9aO4Q/GePu30fbaUQ8BvAR9rKosV6BHDcdGxtfL8A7F1Vd9O0bHopsLFtbvrfeoxXmu3bHY+/T5cDqQM3znr+/9F8GfhWkn9O8vPt8kcAp8w6tvenuaiZb1/raL5w0P7+hy5jkpbCQjmxD3BTVXP7qjX7GL61vdCZvZ+H0LQyvawjHz7ZLp/2nbaiVRq02cf+zpl/rId5r1s61pmdF3O9x3R+PQJ4U8e+bqNpvb3vov8KqXvTx+g+NC1AAaiqH7evdR5/t3Q8/q85nu8GzTiLSdamGbLiTppWRtB855jW67XX/sC3Zp1fpL5L8uQkn227LX6P5nvpXrNW6ypHWp3nh26ujSaelUJLoL3T9HfAy4GfrqoHA1+jueC4EXj0PJvWPMunfQrYK8mhNJVD7+txPzcC/1BVD+742bWq1gJU1YVVdQTNxdY32r9F6pe7aT6cAUjysDnWmXFMty3tjqFpJvoR4Lz2pRuB02cd2z9VVe+fb1/t9j+T5GDgaJq7aNKo2Ajs2zkWCs2Feje+S3NhdFBHPjyoqjovlBY6X0jDMPu43OZ1yzzbbMuNwO/M2t8uVfVv2x25NL/pY/RmmopJoBkcneZz/aYe9vkbwDE03TIfRNO1BprvHNvrRuDh26islfrlfTS9YfavqgfRdBXbnmO68/zQzbXRxLNSaGnsSnPwfQcgzaCfB7evvRP4wyQ/l8ZjOpor3wI8ar6dtjX1HwD+D00fyIvmWfUW4KdnNR3t9F7gV5I8q73DMD2w6X7tQHbPSTNg9T003RDu7fYPl3rwFeCgJIcm2Zmm6fO8kjwgyQuSPKhtdn0n9x2jfwe8tL3DkCS7Jjkqye7z7a9tJfEBmhPQpW03A2lUfIHm+H55mkkHjqFp0r+g9u7z3wF/m+ShAEn2TfKsvkUrLY3Z10PzXrf0uP93AKelHZA3yYOSHNfx+javx6TtdB5wVJJnJNkROIXmmruXSsnd223///buPtzSur7v/fsjIlLUCEW2A0wcmoweUY6YMyUmnCY7EusoJGNzBTuGGGhpJ55g1avTyuA5rUl6aCenxWh8aDJRynhEcRK1UIkaJO5QUwTBEHEYKRMZYWQOE0GEsQl28Hv+WPfA2nv2w9pr9np+v65rrrXu333fa33nt9Z37Xv91u/hIVo/sP3bZZ6/2Hv9Vlo/TGxtrqeemeTsLmKUluvZwMNV9TdJzqLV+LkivDbqjI1CK6Cq7gKuoHUx/yCtibL+rNn3B8DltL6APkarl8IJzan/Dvi/mq5s/4L5fZTWrwF/sFB3zqr6Oq2xl99oHuvkOfvvp/WrwjtoNVzdD/xLWq//02j9cXqAVnfqnwZ+bXk1IHWuqv47rUlyP09r7q1Oxqu/EdjTdJV+E83wr6q6jdY44fcB36E1ceNFHTzedlp56tAxDZWq+j6tyaUvBh6h9V7/NK0vAZ24lFYefKnJl88DL1r5SKUVNet6aInrlmWrqk/RmmD9miYvvga8pu2QXwe2N8//+u7/G9LhqupuWp/l76XVa+HngJ9rPu+X68O0hqJ9C7iL1gTVy/HrLPBeb+b7+jlai+LcB+ylNcWE1Gu/BvxmkseAf81TIwJWitdGS8jsaQskafwl+WFaQyWfX1WPDjoeaTFJbgF+t6pcQl6SJEkryp5CkiZKkqcB/5zWEq02CGnoJPnpJM9vho9dSGt51s8OOi5JkiSNHycSkzQxmrmzHqTV9Xr9gMORFvIiWl2nn0Vr9cpfrKp9gw1JkiRJ48jhY5IkSZIkSRPI4WOSpI4keW6SP0zy9SS7kvxEkhOS3JDknub2+LbjL0uyO8ndrvIgSZIkDZ+h6Cl04okn1po1a+bd973vfY/jjjuuvwENMevjKYvVxe233/7tqnpen0M6IuZBZ6yL2fqZB0m2A/+1qj6Y5Bm0lsN9B61lRLcm2QIcX1WXJjmd1qqIZwEn01rp4YXN6ibzMgc6Z308ZVL+Fviaz2Z9zLZQfYxTDoCvezvrYrZJ+VswLHz/zTbs9bFUDgzFnEJr1qzhtttum3ffzMwM09PT/Q1oiFkfT1msLpJ8s7/RHDnzoDPWxWz9yoMkzwF+CrgInlw6/ftJNgCHAtgOzNBa+nMDrcm8HwfuTbKbVgPRzQs9hznQOevjKZPyt8DXfDbrY7aF6mOccgB83dtZF7NNyt+CYeH7b7Zhr4+lcmAoGoUkSUPv7wB/BfynJC8DbgfeCkwdmgS5qvYlOak5/hTgS23n723KZkmyCdgEMDU1xczMzLxPfuDAgQX3TSLr4ynWhSRJUvdsFJIkdeLpwI8B/6yqbknyHmDLIsdnnrLDxitX1TZgG8C6detqoV9Zhv0XmH6zPp5iXUiSJHXPiaYlSZ3YC+ytqlua7T+k1Uj0YJJVAM3t/rbjV7edfyrwQJ9ilSRJktQBG4UkSUuqqv8PuD/Ji5qic4C7gOuAC5uyC4Frm/vXARuTHJPkNGAtcGsfQ5Yk9UCSo5L8eZJPN9uuQilJI2zoh4/d+a3vctGW65d93p6t5/YgGknqjTVdfM4BXLW+rysd/DPg6mblsW8A/4jWjws7klwM3AecD1BVO5PsoNVwdBC4ZLGVxyToLg/6nANST43I34K3AruA5zTbW4Ab21ah3AIcWoVyI/ASmlUokyy6CuVSuvle4HcCSSthsc/nzWccXPCzaRQ+g4a+UUiSNByq6g5g3Ty7zlng+MuBy3sZkySpf5KcCpxL67P9nzfFK7YKpSSp/2wUkiRJktSJdwNvB57dVnZEq1BC5ytRTh3b+kV+OcZ1dUJXXpytX/WR5JnATcAxtL5L/2FVvTPJCcDHgTXAHuD1VfWd5pzLgIuBJ4C3VNXneh6otAw2CkmSJElaVJLzgP1VdXuS6U5OmafssFUoofOVKN979bVccefyvr7suWD+xxp1rrw4Wx/r43HglVV1IMnRwBeTfAb4Bfo0jFJaaU40LUmSJGkpZwM/n2QPcA3wyiQfwVUoNUGq5UCzeXTzr2gNl9zelG8HXtfcf3IYZVXdCxwaRikNDXsKaSg4uagkSdLwqqrLgMsAmp5C/6KqfjnJv6e1+uRWDl+F8qNJ3kWrh4SrUGosJDkKuB34UeD9VXVLkr4NoxwGkzh8cbGhq4sNbR2FerJRSJIkSVK3tuIqlJogzfv4zCTPBT6V5KWLHL7iwyiHwSQOX1xs5cPNZxxccGjrKAxhXXL4WJJnJrk1yV8k2ZnkN5ryE5LckOSe5vb4tnMuS7I7yd1JXt3L/4AkSZKk/qmqmao6r7n/UFWdU1Vrm9uH2467vKp+pKpeVFWfGVzE0sqrqkdorba3HodRaoR1MqfQocm0XgacCaxP8gpak2fdWFVrgRubbeZMprUe+EDTxU6SJEmSpJGU5HlNDyGSHAv8LPB1WsMlL2wOmzuMcmOSY5KchsMoNYSWHD5WVQUsNJnWdFO+nVYr6aW0TaYF3Jvk0GRaN69k4JIkSZIk9dEqYHvT6eFpwI6q+nSSm3EYpUZUR3MK9WIyrU4n0lps0qbFjMKETt0Y10m9unmNx7UuJEmSJA2fqvoq8PJ5yh8CzlngnMuBy3scmtS1jhqFejGZVqcTab336msXnLRpMaMwoVM3xnVSr8Um7lrIVeuPG8u6kCRJkiSpHzqZU+hJTqYlSZIkSZI0HjpZfczJtCRJkiRJksZMJ+OynExLkiRJkiRpzHSy+piTaUmSJEmSJI2ZZc0pJEmSJEmSpPFgo5AkSVKHkhyV5M+TfLrZPiHJDUnuaW6Pbzv2siS7k9yd5NWDi1qSJGl+NgpJkiR17q3ArrbtLcCNVbUWuLHZJsnpwEbgJbRWbf1AMz+jJEnS0LBRSJIkqQNJTgXOBT7YVrwB2N7c3w68rq38mqp6vKruBXYDZ/UpVEmSpI50svqYJFpDBoDbgG9V1XlJTgA+DqwB9gCvr6rvNMdeBlwMPAG8pao+N5CgJUkr6d3A24Fnt5VNVdU+gKral+SkpvwU4Ettx+1tyg6TZBOwCWBqaoqZmZnDjjlw4MC85ZNqXOtj8xkHuzpvXOtDktR7NgpJnTs0ZOA5zfahIQNbk2xpti+dM2TgZODzSV5YVU8MImhJ0pFLch6wv6puTzLdySnzlNV8B1bVNmAbwLp162p6+vCHn5mZYb7ySTWu9XHRluu7Ou+q9ceNZX1IknrP4WNSBxwyIEkT72zg55PsAa4BXpnkI8CDSVYBNLf7m+P3Aqvbzj8VeKB/4UqSJC3NRiGpM++mNWTgB21ls4YMAO1DBu5vO27BIQOSpNFQVZdV1alVtYZWb9A/qapfBq4DLmwOuxC4trl/HbAxyTFJTgPWArf2OWxJkqRFOXxMWkIvhwx0Mo8EOFdAu3GtC+eRkEbWVmBHkouB+4DzAapqZ5IdwF3AQeAShxFr1CV5JnATcAyt7xF/WFXvdJ5FSRpdNgpJSzs0ZOC1wDOB57QPGWgmFu1qyEAn80jA+M6d0I1xrQvnkZBGR1XNADPN/YeAcxY47nLg8r4FJvXe48Arq+pAkqOBLyb5DPALOM+iJI0kh49JS3DIgCRJElTLgWbz6OZf4TyLkjSy7Ckkdc8hA5IkaaIkOQq4HfhR4P1VdUuSWfMsJmmfZ/FLbafPO89ip8Ppp45d/nDrcR1i7fDx2awPqXs2CknL4JABSZI0yZofus5M8lzgU0leusjhHc2z2Olw+vdefS1X3Lm8ry97Lpj/sUbduA6n75b1IXXP4WOSpI4lOSrJnyf5dLN9QpIbktzT3B7fduxlSXYnuTvJqwcXtSRppVXVI7R+KFtPM88iQLfzLEqSBsNGIUnScrwV2NW2vYXW5KJrgRubbeZMLroe+EAz5ECSNKKSPK/pIUSSY4GfBb6O8yxK0siyUUiS1JEkpwLnAh9sK3ZyUUmaHKuALyT5KvBl4Iaq+jSteRZfleQe4FXNNlW1Ezg0z+JncZ5FSRo6zikkSerUu4G3A89uK+vL5KJOIDnbuNbHcieQhfGtC2kYVdVXgZfPU+48i5I0omwUkiQtKcl5wP6quj3JdCenzFPW9eSiTiA527jWx0Vbrl/2OVetP24s60KSJKkfbBSSJHXibODnk7wWeCbwnCQfoZlctOkl5OSikiRJ0ghxTiFJ0pKq6rKqOrWq1tCaQPpPquqXcXJRSZI0IZKsTvKFJLuS7Ezy1qbc1Vg1smwUkiQdCScXlSRJk+IgsLmqXgy8ArikWXHV1Vg1shw+JklalqqaAWaa+04uKkmSJkKzuMahBTYeS7KL1kIaG4Dp5rDttK6TLqVtNVbg3iSHVmO9ub+RSwtbslEoyWrgw8DzgR8A26rqPUlOAD4OrAH2AK+vqu8051wGXAw8Abylqj7Xk+glSZIkSeqzJGtorcZ3C0e4GmvzeB2tyDoMJnHlz8VWSJ06duH9o1BPnfQUOtRF7itJng3cnuQG4CJaXeS2JtlCq4vcpXO6yJ0MfD7JCx02IEmSJEkadUmeBXwCeFtVPZrMt+hq69B5yg5bjRU6X5F1GIzrKqiLWWyF1M1nHOSKO+dvWtlzwXSPIlo5S84pVFX7quorzf3HgPYuctubw7YDr2vuP9lFrqruBQ51kZMkSZIkaWQlOZpWg9DVVfXJpvjBZhVWXI1Vo2ZZcwqtZBe5TrvHLdYVazGj0E2rG+PaVa+b13hc60KSJEnS8EmrS9CHgF1V9a62XYdWY93K4auxfjTJu2iNonE1Vg2djhuFVrqLXKfd49579bULdsVazCh00+rGuHbVW6w73kKuWn/cWNaFJEmSpKF0NvBG4M4kdzRl76DVGLQjycXAfcD50FqNNcmh1VgP4mqsGkIdtbYs1kWu6SVkFzlJkiRJ0hFZ0+WPxf1QVV9k/k4Q4GqsGlGdrD5mFzlJkiRJkqQe66ZhFGDP1nO7Oq+TnkJ2kZMkSZIkSRozSzYK2UVOkiRJkiRp/Cy5JL0kSZIkSZLGj41CkiRJkiRJE8hGIUmSJElLSrI6yReS7EqyM8lbm/ITktyQ5J7m9vi2cy5LsjvJ3UlePbjoJUnzsVFIkiRJUicOApur6sXAK4BLkpwObAFurKq1wI3NNs2+jcBLgPXAB5IcNZDIJUnzslFIkiRJ0pKqal9VfaW5/xiwCzgF2ABsbw7bDryuub8BuKaqHq+qe4HdwFl9DVqStCgbhSRJkiQtS5I1wMuBW4CpqtoHrYYj4KTmsFOA+9tO29uUSZKGxJJL0kuSJEnSIUmeBXwCeFtVPZpkwUPnKat5Hm8TsAlgamqKmZmZeR9s6ljYfMbBZcW60GONugMHDozt/225rzGMd31IvWajkCRJkqSOJDmaVoPQ1VX1yab4wSSrqmpfklXA/qZ8L7C67fRTgQfmPmZVbQO2Aaxbt66mp6fnfe73Xn0tV9y5vK8vey6Y/7FG3czMDAvV06i7aMv1yz7nqvXHjW19SL3m8DFJkiRJS0qrS9CHgF1V9a62XdcBFzb3LwSubSvfmOSYJKcBa4Fb+xWvJGlp9hSSJEmS1ImzgTcCdya5oyl7B7AV2JHkYuA+4HyAqtqZZAdwF62Vyy6pqif6HrUkaUE2CkmSJElaUlV9kfnnCQI4Z4FzLgcu71lQkqQj4vAxSZKkJSRZneQLSXYl2ZnkrU35CUluSHJPc3t82zmXJdmd5O4krx5c9JIkSfOzUUiSJGlpB4HNVfVi4BXAJUlOB7YAN1bVWuDGZptm30bgJcB64ANJjhpI5JIkSQuwUUhagr8OS5Kqal9VfaW5/xiwCzgF2ABsbw7bDryuub8BuKaqHq+qe4HdwFl9DVqSJGkJNgpJS/PXYUnSk5KsAV4O3AJMVdU+aDUcASc1h50C3N922t6mTJIkaWg40bS0hOYi/9AF/2NJ2n8dnm4O2w7MAJfS9uswcG+SQ78O39zfyCVJKy3Js4BPAG+rqkdbK3TPf+g8ZbXAY24CNgFMTU0xMzNz2DEHDhyYt3xSjWt9bD7jYFfnjWt9SJJ6z0YhaRkW+3U4Sfuvw19qO23BX4c7+SIAXuy1G9e68IuANPySHE2rQejqqvpkU/xgklXN34FVwP6mfC+wuu30U4EH5nvcqtoGbANYt25dTU9PH3bMzMwM85VPqnGtj4u2XN/VeVetP24s60OS1Hs2Ckkd6sWvw518EYDxvfjtxrjWxbB/EUiyGvgw8HzgB8C2qnpPkhOAjwNrgD3A66vqO805lwEXA08Ab6mqz/U8UKlH0vrQ/xCwq6re1bbrOuBCYGtze21b+UeTvAs4GVgL3Nq/iCVJkpbmnEJSBxb7dbjZ39Wvw9IIcW4tTbqzgTcCr0xyR/PvtbQag16V5B7gVc02VbUT2AHcBXwWuKSqnhhM6JIkSfOzp5C0BH8dlpxbS6qqLzJ/T1CAcxY453Lg8p4FJUmSdIRsFJKWdujX4TuT3NGUvYNWY9COJBcD9wHnQ+vX4SSHfh0+iL8Oa8ys5NxazqvVnXGtj27m1hrXupAkDackVwLnAfur6qVNmcPpNbJsFJKW4K/D0lNWem4t59XqzrjWRzdzaznBriSpz64C3kdrrsVDDg2n35pkS7N96Zzh9CcDn0/yQn8w1jBZck6hJFcm2Z/ka21lJyS5Ick9ze3xbfsuS7I7yd1JXt2rwCVJ/eXcWpIkadJV1U3Aw3OKN9AaRk9z+7q28muq6vGquhc4NJxeGhqd9BS6CltCJWmiObeWJEnSgo5oOD10PqR+GAzL0O07v/XdZZ9zxik/1NVzLTbEferYhfd3U0/dDKfv9rmgg0ahqrqpmT+inROLStJkcW4tSZKk5eloOD10PqR+GAzLMPZuhp3vuWB6xZ9r8xkHueLO+ZtWunm+bv5f3T4XdD+nUN9aQhdrdVvMMLRc9sKwtMquNCcXlYabc2tJg3Xnt77b3cXv1nN7EI0kaY4Hk6xqvhs7nF4jZaUnml7xltD3Xn3tgq1ui+m2lWzYDUur7EpzclFJkiRJI8rh9BpZ3TYK2RIqSZIkSZooST5GayqVE5PsBd6Jw+k1wpZcfWwBh1pC4fCW0I1JjklyGraESpIkSWPBVYklqKo3VNWqqjq6qk6tqg9V1UNVdU5VrW1uH247/vKq+pGqelFVfWaQsUvz6WRJ+o/Rmij6RUn2Nq2fW4FXJbkHeFWzTVXtBA61hH4WW0IlSZKkcXEVsH5O2aFVidcCNzbbzFmVeD3wgSRH9S9USVInOll97A0L7HJiUUmSJGlCuCqxJI2flZ5oWlIPdLPqjCvOSJKkPhjqVYnHdaXacV6F11WJpf6yUUiSJEnSShuKVYldkXj0uCqx1F/dTjQtSZIkSQ82qxHjqsSSNHpsFJIkSZLULVcllqQR5vAxSZIkSUtqViWeBk5Mshd4J61ViHc0KxTfB5wPrVWJkxxalfggrkosSUPJRiFJkiRJS3JVYkkaPw4fkyRJkiRJmkA2CkmSJEmSJE0gG4UkSZIkSZImkI1CkiRJkiRJE8hGIUmSJEmSpAlko5AkSZIkSdIEslFIkiRJkiRpAj190AFIkrSUO7/1XS7acv2yztmz9dweRSNJkiSNB3sKSZIkSZIkTSAbhSRJkiRJkiaQw8ckSZIkSdKT1iwybH/zGQcXHNbv8P3RY08hSZIkSZKkCWRPIUmSJEmShtxivXcWY+8dLcaeQpIkSZIkSRPIRiFJkiRJkqQJZKOQJEmSJEnSBOpZo1CS9UnuTrI7yZZePY80rMwBTTpzQDIPJHNAMg803HrSKJTkKOD9wGuA04E3JDm9F88lDSNzQJPOHJDMA8kckMwDDb9e9RQ6C9hdVd+oqu8D1wAbevRc0jAyBzTpzAHJPJDMAck80JDr1ZL0pwD3t23vBX68/YAkm4BNzeaBJHcv8FgnAt9ebgD5reWeMTK6qo9x9DO/tWhdvKCfscxjyRyA3uaBOTAZhjgPzIHeMg8aQ5wDsLJ54PXQbOZAm0XyYJxyAPxb0M4caDNBfwu6stJ58JZF3n/DnnO9iG9Y6mOR51o0B3rVKJR5ymrWRtU2YNuSD5TcVlXrViqwUWd9PGXI62LJHADzoBvWxWxDXB/mQA9ZH08Z8rpYsTwY8v9n31kfsw1xffi3oEesi9mGvD5WNA+GwZDXd9+Nen30avjYXmB12/apwAM9ei5pGJkDmnTmgGQeSOaAZB5oyPWqUejLwNokpyV5BrARuK5HzyUNI3NAk84ckMwDyRyQzAMNuZ4MH6uqg0neDHwOOAq4sqp2dvlwI9GFro+sj6cMbV2scA7AEP9fB8C6mG0o68Mc6Dnr4ylDWxdeD/WU9THbUNaHfwt6yrqYbWjrowd5MAyGtr4HZKTrI1WHDWeUJEmSJEnSmOvV8DFJkiRJkiQNMRuFJEmSJEmSJtDQNAolWZ/k7iS7k2yZZ3+S/E6z/6tJfmwQcfZDB3UxneS7Se5o/v3rQcTZD0muTLI/ydcW2D827wtzYDbz4Cnmwaz9Y/N/XYo58BRzYNb+sfm/dsI8eMqk5IE5MJs58JRJyYFhlmR1ki8k2ZVkZ5K3DjqmQUtyVJI/T/LpQcfStaoa+D9aE279JfB3gGcAfwGcPueY1wKfAQK8Arhl0HEPsC6mgU8POtY+1cdPAT8GfG2B/WPxvjAHuqoP82DM3hvmwbLrwhwYs/eFOdBVfZgHY/TeMAe6qg9zYALfGwN8DVYBP9bcfzbw3+e+JyftH/DPgY+Och4OS0+hs4DdVfWNqvo+cA2wYc4xG4APV8uXgOcmWdXvQPugk7qYGFV1E/DwIoeMy/vCHJjNPGhjHswyLv/XpZgDbcyBWcbl/9oJ86DNhOSBOTCbOdBmQnJgqFXVvqr6SnP/MWAXcMpgoxqcJKcC5wIfHHQsR2JYGoVOAe5v297L4W+uTo4ZB53+P38iyV8k+UySl/QntKE0Lu8Lc2A282B5xuW9YR48xRxYnnF5X5gDs5kHyzMO7w1zYDZzYHkm6b0xcEnWAC8HbhlwKIP0buDtwA8GHMcRefqgA2hknrLq4phx0Mn/8yvAC6rqQJLXAv8ZWNvrwIbUuLwvzIHZzIPlGZf3hnnwFHNgecblfWEOzGYeLM84vDfMgdnMgeWZpPfGQCV5FvAJ4G1V9eig4xmEJOcB+6vq9iTTAw7niAxLT6G9wOq27VOBB7o4Zhws+f+sqker6kBz/4+Ao5Oc2L8Qh8q4vC/MgdnMg+UZl/eGefAUc2B5xuV9YQ7MZh4szzi8N8yB2cyB5Zmk98bAJDmaVoPQ1VX1yUHHM0BnAz+fZA+toZ2vTPKRwYbUnWFpFPoysDbJaUmeAWwErptzzHXArzSzyr8C+G5V7et3oH2wZF0keX6SNPfPovU6PtT3SIfDuLwvzIHZzIPlGZf3hnnwFHNgecblfWEOzGYeLM84vDfMgdnMgeWZpPfGQDTvtQ8Bu6rqXYOOZ5Cq6rKqOrWq1tDKzT+pql8ecFhdGYrhY1V1MMmbgc/RmmX/yqrameRNzf7fBf6I1ozyu4H/AfyjQcXbSx3WxS8C/0eSg8BfAxuraiy7Rib5GK1VFU5Mshd4J3A0jNf7whyYzTyYzTyYvDwwB2YzByYvB8A8mGsS8sAcmM0cmG0ScmAEnA28EbgzyR1N2TuaXmoaURnTzwxJkiRJkiQtYliGj0mSJEmSJKmPbBRaQUlelOTPkzyW5C09fq4LkvxxL59DkjTckqxJUkmGYji4NChJppvhJNJQS7Inyc+u8GNelOSLi+w3PzRwS71PNTg2Cq2stwMzVfXsqvqdlXrQ+S76q+rqqvr7K/Uc0jjzi7OGWfMF4a+bHxQeSfLfkrwpiX+jNfaSXJbkj+aU3bNA2cb+RieNpuaa50cHHYek0eAF58p6AbBzvh1JjupzLJKk0fFzVfVsWn9HtgKX0lrdQxp3NwFnH7pOSvJ8WhPH/ticsh9tjpUkSSvIRqEVkuRPgJ8B3pfkQJKPJvmPSf4oyfeAn0lycpJPJPmrJPe2DzFL8rQkW5L8ZZKHkuxIckKz+9BF0CPNY//E3O53zS8Cv9b8kvZYkn+T5EeS3Jzk0ebxntF2/HlJ7mj7Vfp/7UM1SQAk+bG2oZZ/kOTjSf7vZt8/TbI7ycNJrktyctt5P5nky0m+29z+ZNu+Wd2xk/x6ko80m4flUD/+n9JyVdV3q+o64B8CFyZ5aZJzm3x5NMn9SX59ofOTnJDkPyV5IMl3kvznfsUudenLtBqBzmy2fwr4AnD3nLK/BF6dZFfzt+MbSX51oQdNsjrJJ5trroeSvK9n/wNp+c5M8tXmeubjSZ4Ji1+ft31PeCzJXUn+wXwPnOTQNc9fNNc8/7Bt3+Yk+5PsS+LKXOqJJd6rSfLe5r3/9STntO24qPlsf6z5rnxB275/3Hz+fyfJ55K8oG1fpdXD+p5m//uTpG3/P23723FXkh9ryhf7bn5Wktuaa68Hk7yrZxU2BGwUWiFV9UrgvwJvrqpnAd8Hfgm4HHg28N+A/wL8BXAKcA7wtiSvbh7iLcDrgJ8GTga+A7y/2fdTze1zq+pZVXXzAmGsB/434BW0hrJtAy4AVgMvBd4ArS/kwJXArwJ/G/g94LokxxxRJUgdaBonPwVcBZwAfAz4B82+VwL/Dng9sAr4JnBNs+8E4Hrgd2i9b98FXJ/kb3fwtJ3mkDQUqupWYC/w94DvAb8CPBc4l9bSw69b4NT/F/hbwEuAk4Df7nWs0pGoqu8Dt/DU5/RP0bqe+uKcspuA/cB5wHNoLTX924cu7tul1cPo07T+hqyhdd11Tc/+E9LyvZ7WdftpwP8KXNTB9flf0vqb8EPAbwAfSbJq7gNX1aG8eVlzzfPxZvv5zbmnABcD709yfC/+c5p4i71Xfxz4BnAi8E7gk80PWsfRusZ/TdNz+ieBOwCaa553AL8API/W34iPzXnO84C/C7yMVn69ujn3fODXaV1HPQf4eeChtIboL/bd/D3Ae6rqOcCPADuOvFqGl41CvXVtVf1ZVf0AOAN4XlX9ZlV9v6q+Afw+cGh8/K8C/2dV7a2qx2m9eX8xy5sD5beq6tGq2gl8DfjjqvpGVX0X+Azw8ua4fwr8XlXdUlVPVNV24HFajUlSr70CeDrwO1X1P6vqk8Ctzb4LgCur6itNHlwG/ESSNbS+DN9TVf9vVR2sqo8BXwd+rv//BakvHgBOqKqZqrqzqn5QVV+ldSH003MPbi64XgO8qaq+0+TXn/Y5Zqkbf8pTDUB/j9YF/3+dU/anVXV9Vf1ltfwp8MfNvrnOovUD27+squ9V1d9UlZObapj8TlU9UFUP0/pieiZLXJ9X1R805/ygaei5h9Z7vVP/E/jN5m/DHwEHgBet4P9JApZ8r+4H3t28Dz9Oq1fouc2+HwAvTXJsVe1rvtNC63vyv6uqXVV1EPi3tHrbveCpZ2VrVT1SVffR6m16ZlP+T4D/p6q+3Pzt2F1V36TVgLTYd/P/CfxokhOr6kBVfWlla2m42CjUW/e33X8BcHLTHfSRJI/QavGcatv/qbZ9u4An2vZ34sG2+389z/az2p5r85xYVtO6gJJ67WTgW1VVbWX3t+375qHCqjoAPESrBX/WvsY3m33SODoFeDjJjyf5QtO9+bvAm2j9wjbXauDhqvpOX6OUjtxNwP/e9Fp4XlXdQ6uH9U82ZS8FbkrymiRfSmt48SPAa1k4F77ZfHmQhtH/13b/f9C6Rl/0+jzJr7QNLXuEVl7M9/5fyENzcuLQ80oraon36tzvAN8ETq6q79EaOv8mYF+S65P8L80xLwDe0/Z4DwNh9neA+XIKWjn0l/OEudR384uBFwJfT2vKivOWVwujxUah3pr7pffeqnpu279nV9Vr2/a/Zs7+Z1bVt+Y8zkq4H7h8znP9rabnhdRr+4BT2sf60vrAhlbPiPYxwsfR6kL9rbn7Gj/c7IPWEJu/1bbv+W33VzqHpJ5K8ndpXex8EfgocB2wuqp+CPhdWhdDc90PnJDkuf2KU1ohN9MaZrAJ+DOAqnqU1uf+pub2AeATwH8ApqrqucAfsXAu/PAye1tLg7bg9XnTI+L3gTcDf7t5/3+N+d//0sB08F6d+x3gh2l9vlNVn6uqV9GaQuLrzeNAKzd+dU5uHFtV/62DkO6nNfxrvvIFv5tX1T1V9QZaQ/F/C/jD5nvJWLJRqH9uBR5NcmmSY5McldYEon+32f+7wOWHusEleV6SDc2+v6LVne7vrFAsvw+8qfn1OUmOS2si02ev0ONLi7mZVi+4Nyd5evM+P9Sl9KPAP0pyZjOG/t8Ct1TVHloX/y9M8kvNef8QOJ3WvBHQGne8McnRSdYBv9j2nCudQ1JPJHlO82vUNcBHqupOWvPSPVxVf5PkLFrz1R2mqvbRGir8gSTHN7nwU/MdKw2Tqvpr4Dbgn9MaNnbIF5uym4BnAMfQ+jw/mOQ1wN9f4CFvpfUDxNbmGueZSc7uVfzSClns+vw4Wj9w/RVAWpNEv3SRx3oQr3k0GEu9V08C3tJco5wPvBj4oyRTSX6+aXh5nNbwxieac34XuCzJS5rH/KHm3E58EPgXSf63Jq9+tPm+veh38yS/nOR51ZoG5pHmsZ6Y/ylGn41CfVJVT9Ca++RM4F7g27TepD/UHPIeWr8E/3GSx4Av0ZqIi6r6H7QmrP6zpnvbEc39U1W30Rq3/D5aE1rvBi46kseUOlWtSUV/gVa3zEeAX6bVsPN4Vd0I/Ctavwbvo9Wyv7E57yFak8htpjWk7O3AeVX17eah/1Vz/HdoTWr30bbnXNEcknrgvzSf/fcD/yetidQPrQzza8BvNvv/NYtPdvhGWuPgv05r3P7behWwtML+lNaXhfa5f/5rU3ZTVT1Ga1GOHbQ+53+J1nXTYdquuX4UuI/WpO3/cL5jpWGx2PV5Vd0FXEHrh7UHac1V+meLPNyvA9uba57X9y5qabYO3qu3AGtpfRe+HPjF5hr/abSu8R+gNTzsp2ld/1BVn6LVW+eaJI/S6nn0mg7j+YPmeT4KPAb8Z1rzNS713Xw9sDPJAVrf0zdW1d8sqzJGSGYP6ZOk/ktyC/C7VfWfBh2LJEmSJE0KewpJ6rskP53k+c0wsAtpLcf62UHHJUmSJEmTxAn4JA3Ci2gNAXgWrRUBfrGZD0WSJEmS1CcOH5MkSZIkSZpADh+TJEmSJEmaQEMxfOzEE0+sNWvWDDqMRX3ve9/juOOOG3QYQ2PY6+P222//dlU9b9BxLMew58Gwv+b9Ngr1MWp5MOw5AKPxuvfLKNTFqOUADH8ejMLr3k/DXh/mQG8M++veT6NQF+OUB6NQ34eMSqyTEOdSOTAUjUJr1qzhtttuG3QYi5qZmWF6enrQYQyNYa+PJN8cdAzLNex5MOyveb+NQn2MWh4Mew7AaLzu/TIKdTFqOQDDnwej8Lr307DXhznQG8P+uvfTKNTFOOXBKNT3IaMS6yTEuVQOOHxMkiRJkiRpAtkoJEmSJEmSNIFsFJKWkGR1ki8k2ZVkZ5K3NuUnJLkhyT3N7fFt51yWZHeSu5O8enDRS5IkSZI0v6GYU0idW7Pl+mWfs2fruT2IZKIcBDZX1VeSPBu4PckNwEXAjVW1NckWYAtwaZLTgY3AS4CTgc8neWFVPTGg+MdKNzkA5sGo87NPk87PPkmTwr/5C7vzW9/lIutHK8yeQtISqmpfVX2luf8YsAs4BdgAbG8O2w68rrm/Abimqh6vqnuB3cBZfQ1akiRJkqQl2FNIWoYka4CXA7cAU1W1D1oNR0lOag47BfhS22l7m7L5Hm8TsAlgamqKmZmZ3gS+Ag4cODAU8W0+42BX56107MNSH5IkSZLULRuFpA4leRbwCeBtVfVokgUPnaes5juwqrYB2wDWrVtXw7wc4rAs19hNl1mAPRdMr2gcw1IfkiRJktQth49JHUhyNK0Goaur6pNN8YNJVjX7VwH7m/K9wOq2008FHuhXrJIkSZIkdcJGIWkJaXUJ+hCwq6re1bbrOuDC5v6FwLVt5RuTHJPkNGAtcGu/4pUkSZIkqRMOH5OWdjbwRuDOJHc0Ze8AtgI7klwM3AecD1BVO5PsAO6itXLZJa48JkmSJEkaNjYKSUuoqi8y/zxBAOcscM7lwOU9C0qSJEmSpCPk8DFJUseSHJXkz5N8utk+IckNSe5pbo9vO/ayJLuT3J3k1YOLWpIkSdJ8bBSSJC3HW4FdbdtbgBurai1wY7NNktOBjcBLgPXAB5Ic1edYJUmSJC3CRiFJUkeSnAqcC3ywrXgDsL25vx14XVv5NVX1eFXdC+wGzupTqJIkSZI6YKOQJKlT7wbeDvygrWyqqvYBNLcnNeWnAPe3Hbe3KZMkSZI0JJxoWpK0pCTnAfur6vYk052cMk9ZzfO4m4BNAFNTU8zMzMz7YJvPONhpqE9a6LGOxIEDB3ryuKPIupAkSRp9NgpJkjpxNvDzSV4LPBN4TpKPAA8mWVVV+5KsAvY3x+8FVredfyrwwNwHraptwDaAdevW1fT09LxPftGW65cd8J4L5n+sIzEzM8NCMU4a60KSJGn0OXxMkrSkqrqsqk6tqjW0JpD+k6r6ZeA64MLmsAuBa5v71wEbkxyT5DRgLXBrn8OWJEmStAh7CkmSjsRWYEeSi4H7gPMBqmpnkh3AXcBB4JKqemJwYUqSJEmay55CkqRlqaqZqjqvuf9QVZ1TVWub24fbjru8qn6kql5UVZ8ZXMTSkUuyOskXkuxKsjPJW5vyE5LckOSe5vb4tnMuS7I7yd1JXj246CVJkuZno5AkSdLSDgKbq+rFwCuAS5KcDmwBbqyqtcCNzTbNvo3AS4D1wAeSHDWQyKUVYuOoJl2SZya5NclfNDnwG025OaCRtWSjkB/+kiRp0lXVvqr6SnP/MWAXcAqwAdjeHLYdeF1zfwNwTVU9XlX3AruBs/oatLTybBzVpHsceGVVvQw4E1if5BWYAxphnfQU8sNfkiSpkWQN8HLgFmCqqvZBq+EIOKk57BTg/rbT9jZl0siycVSTrloONJtHN/8Kc0AjbMmJppsLnEMXO48laf/wn24O2w7MAJfS9sYH7k1y6I1/80oHL0mS1E9JngV8AnhbVT2aZMFD5ymrBR5zE7AJYGpqipmZmcOO2XzGwW7CnfexjsSBAwdW/DFH2STXx2KNo0naG0e/1HbavI2jneTAMJnk132uXtRFN593/Xw9mg4PtwM/Cry/qm5JckQ50Dzuknkwdezw188ho5InxrnM1cf88J8ZdBhD8yEwLPUhSVK/JDmaVoPQ1VX1yab4wSSrmmuhVcD+pnwvsLrt9FOBB+Z73KraBmwDWLduXU1PTx92zEVbru8q5j0XHP5YR2JmZob54ptUk1ofK9042kkODJNJfd3n04u66ObzbqU/6xbTrKZ6ZpLnAp9K8tJFDu/4B4JO8uC9V1/LFXcufwHxftbPIaOSJ8a5jEYhP/yH480yLB+Sw1IfkiT1Q1oXPh8CdlXVu9p2XQdcCGxtbq9tK/9okncBJwNrgVv7F7HUG71qHJVGTVU9kmSG1pQp5oBGVkerjy324d/s940vSZLG2dnAG4FXJrmj+fdaWo1Br0pyD/CqZpuq2gnsAO4CPgtc0vy6LI2sDhpH4fDG0Y1JjklyGjaOasQleV7TQ4gkxwI/C3wdc0AjbMmeQv4yJkmSJl1VfZH5e0MDnLPAOZcDl/csKKn/DjWO3pnkjqbsHbS+D+xIcjFwH3A+tBpHkxxqHD2IjaMafauA7c28Qk8DdlTVp5PcjDmgEdXJ8DE//DXxklwJnAfsr6qXNmW/DvxT4K+aw95RVX/U7LsMuBh4AnhLVX2u70FLkiStIBtHNemq6qu05tidW/4Q5oBGVCerj/nhL8FVwPuAD88p/+2q+g/tBUlOBzYCL6HVW+7zSV5o46gkSZIkaZh0NKeQNOmq6ibg4Q4P3wBcU1WPV9W9wG7grJ4FJ0mSJElSF5a/np2kdm9O8ivAbcDmqvoOcArwpbZj9jZlh0myCdgEMDU1xczMTG+jPQIHDhwYivg2n3Gwq/NWOvZhqQ9JkiRJ6paNQlL3/iPwb4Bqbq8A/jHzD7es+R6gqrYB2wDWrVtX09PTPQl0JczMzDAM8V205fquzttzwfSKxjEs9SFJkiRJ3bJRSAtas8iX781nHJz3y/meref2MqShUlUPHrqf5PeBTzebe4HVbYeeCjzQx9COyEKv+0KvOUzW6y5JkiRJ48I5haQuJVnVtvkPgK81968DNiY5JslpwFrg1n7HJ62kJM9McmuSv0iyM8lvNOUnJLkhyT3N7fFt51yWZHeSu5O8enDRS5IkSZqPPYWkDiT5GDANnJhkL/BOYDrJmbSGhu0BfhWgqnYm2QHcBRwELnHlsdHUTW85GNueU48Dr6yqA0mOBr6Y5DPALwA3VtXWJFuALcClrsInSZIkDT8bhaQOVNUb5in+0CLHXw5c3ruIpP6qqgIONJtHN/+K1mp70035dmAGuJS2VfiAe5McWoXv5v5FLUmSJGkxDh+TJHUkyVFJ7gD2AzdU1S3AVFXtA2huT2oOPwW4v+30BVfhkyRJkjQY9hSSJHWkGfp1ZpLnAp9K8tJFDu9oFb4km4BNAFNTU8zMzMz7YJvPOLjccBd8rCNx4MCBnjzuKLIuJEmSRp+NQpKkZamqR5LMAOuBB5Osqqp9zeTr+5vDOlqFr6q2AdsA1q1bV9PT0/M+50LzNy1mzwXzP9aRmJmZYaEYJ411IUmSNPocPiZJWlKS5zU9hEhyLPCzwNdprbZ3YXPYhcC1zX1X4ZMkSZKGnD2FJEmdWAVsT3IUrR8UdlTVp5PcDOxIcjFwH3A+uAqfJEmSNApsFJIkLamqvgq8fJ7yh4BzFjjHVfgkSZKkIebwMUmSJEmSpAlkTyFJkiQ9ac0iE7tvPuPgghO/79l6bq9CkiRJPWJPIUmSJEmSpAlko5AkSZIkSdIEslFIkiRJkiRpAtkoJEmSJEnSEpKsTvKFJLuS7Ezy1qb8hCQ3JLmnuT2+7ZzLkuxOcneSVw8ueml+NgpJkiRJkrS0g8Dmqnox8ArgkiSnA1uAG6tqLXBjs02zbyPwEmA98IEkRw0kcmkBNgpJkiRJkrSEqtpXVV9p7j8G7AJOATYA25vDtgOva+5vAK6pqser6l5gN3BWX4OWluCS9JI0BBZbAnoxLgEtSZLUf0nWAC8HbgGmqmoftBqOkpzUHHYK8KW20/Y2ZfM93iZgE8DU1BQzMzOHHTN1LGw+4+CyY53vsXrtwIEDA3ne5TJOG4UkSZIkSepYkmcBnwDeVlWPJlnw0HnKar4Dq2obsA1g3bp1NT09fdgx7736Wq64c/lf4fdccPhj9drMzAzz/R+GjXE6fEzqSJIrk+xP8rW2MieUkyRJkiZIkqNpNQhdXVWfbIofTLKq2b8K2N+U7wVWt51+KvBAv2KVOmGjkNSZq2hNDtfOCeUkSZKkCZFWl6APAbuq6l1tu64DLmzuXwhc21a+MckxSU4D1gK39iteqRMOH5M6UFU3NeOG220Appv724EZ4FLaJpQD7k1yaEK5m/sSrCRJksaacxEOzNnAG4E7k9zRlL0D2ArsSHIxcB9wPkBV7UyyA7iL1spll1TVE32PWlqEjUJS9/oyoVy/LTR53WIT2/Uz7m4m14PuYlzsuVa6Pvr5/5KkUecXYkmDUFVfZP55ggDOWeCcy4HLexaUdIRsFJJW3opOKNdvFy1wob35jIMLTmzXz8nrFopvKd3EuNhzrXR99PP/1Y0kq4EPA88HfgBsq6r3JDkB+DiwBtgDvL6qvtOccxlwMfAE8Jaq+lxfgpUkSZLUEecUkrrnhHKaJAeBzVX1YuAVwCXN/FnOrSVJkiSNKBuFpO45oZwmRlXtq6qvNPcfA3bRGha5gdacWjS3r2vuPzm3VlXdCxyaW0uSJEnSkHD4mNSBJB+jNan0iUn2Au/ECeU0oZpJ118O3MIKzK0lSZIkaTBsFJI6UFVvWGCXE8ppoiR5FvAJ4G1V9WhrZdb5D52n7LC5tTqdbL2bibh7MQn3gQMHnNy7MYl1keRK4Dxgf1W9tClzXi1JkjSylmwUmqQLoMVWsth8xsEFJ4J1JQtJkyDJ0bQahK6uqk82xQ8mWdX0Elr23FqdTrbezUTcvZiEe2ZmhmGYEH4YTGhdXAW8j9ak64ccmldra5Itzfalc+bVOhn4fJIX2nNUo2ySvhdotoW+J/kdSRp9ncwpdBWtSULbObGoJE2QtLoEfQjYVVXvatvl3FqaGFV1E/DwnGLn1dIkuQq/F0jSWFmyp1BV3dTMH9FuA635VaB1ATQDXErbBRBwb5JDF0A3r1C8kqTBOBt4I3Bnkjuasnfg3FqS82ppYvi9QBpNi42IWYg9vSZHt3MKHfEFUKfzSPTTYnNWTB278P5+xt7PeTW6qY9heB0lrbyq+iLzzxMEzq0lzaejebWgs2uibv7+Q3d/l8f1eghWPsZJnFtrHmP5vWAxw/C69zsHFnq+XnwmDMs8gtKkWOmJpju+AOp0Hol+WmzOis1nHOSKO+evrl7MW7GQfs6r0U199LMuJEkaAkc0rxZ0dk3Uzd9/6O7v8rheD8HKxzihc2t1aqS/FyxmGF73fufAQs/Xi8+EYZlHUJoUncwpNJ8Hmwsfur0AkiRJGgPOq6VJ5/cCSRph3TYKeQEkSZImSpKP0ZoP5UVJ9jZzaW0FXpXkHuBVzTZVtRM4NK/WZ3FeLY0vvxdI0gjrZEn6j9GaPO7EJHuBd+LEopIkacJU1RsW2OW8WpoIg/5e4GS5krTyOll9zAsgSZIkTaTFGiI2n3FwwflPxrExwu8FkjR+uh0+JkmSJEmSpBFmo5AkSZIkSdIEslFIkiRJkiRpAi05p5AkSZIkTZJu5pIax3mkJI0/ewpJkiRJkiRNIBuFJEmSJEnqQJIrk+xP8rW2shOS3JDknub2+LZ9lyXZneTuJK8eTNTSwmwUkiRJkiSpM1cB6+eUbQFurKq1wI3NNklOBzYCL2nO+UCSo/oXqrQ05xTSUFhs3PZChmXcdpI9wGPAE8DBqlqX5ATg48AaYA/w+qr6zqBilCRJknTkquqmJGvmFG8Appv724EZ4NKm/Jqqehy4N8lu4Czg5r4EK3XARiFpZfxMVX27bfvQrwVbk2xpti8dTGiSlqObyUVheBqqJUlS301V1T6AqtqX5KSm/BTgS23H7W3KDpNkE7AJYGpqipmZmcOf5NjWtUg/zPf8y3HgwIEjfox+ME4bhaReWejXAmkkJbkSOA/YX1UvbcoW7BGX5DLgYlo96N5SVZ8bQNiSJEmDlHnKar4Dq2obsA1g3bp1NT09fdgx7736Wq64sz9f4fdccPjzL8fMzAzz/R+GjXHaKCSthAL+OEkBv9d8oC/0a8Esnfwi0G8L/fqw2C8T/Yy7219Huolxseda6fro5/+rS1cB7wM+3FY2b4+4OePnTwY+n+SFVfVEv4KVJEnqoweTrGqu+1cB+5vyvcDqtuNOBR7oe3TSImwUko7c2VX1QNPwc0OSr3d6Yie/CEB/51xaaGjM5jMOLvjLxJH+krAcC8W3lG5iXOy5Vro++vn/6obj5yVJkhZ0HXAhsLW5vbat/KNJ3kXrh7K1wK0DiVBagKuPSUeoqh5obvcDn6L15ffB5lcC5vxaII2TWT3igPbx8/e3Hbfg+HlJkqRRkuRjtH7oelGSvUkuptUY9Kok9wCvarapqp3ADuAu4LPAJfac1rCxp5B0BJIcBzytqh5r7v994DdZ+NcCaRJ0PH6+0yGU3Qyv63ZoXT+HDY6yUZmYUZKklVRVb1hg1zkLHH85cHnvIpKOjI1C0pGZAj6VBFr59NGq+mySLwM7ml8O7gPOH2CMUq8c8fj5TodQdjO8rtuhdf0cNjjKRmViRkmSJC3MRiHpCFTVN4CXzVP+EAv8WiCNEcfPS5IkSSPMRiFJ0pKa8fPTwIlJ9gLvpNUYdFiPuKrameTQ+PmDOH5ekiRJGkpD3yjUzapL0P3KS5Kkwzl+XpJ6z+teSVK/ufqYJEmSJEnSBLJRSJIkSZIkaQLZKCRJkiRJkjSBhn5OIUmSJEmS1D/OcTY57CkkSZIkSZI0gWwUkiRJkiRJmkA2CkmSJEmSJE0g5xSSJEmSJElH7NBcRJvPOMhFHc5L5DxEg2WjkCRJI6abyR+94JIkSdJcDh+TJEmSJEmaQDYKSZIkSZIkTSAbhSRJkiRJkiaQjUKSJEmSJEkTyEYhSZIkSZKkCdSz1ceSrAfeAxwFfLCqtvbquaRhZA5o0pkD42Ghlc4WW2q225XOxnFVNfNAk84ckMyDpXTz979bw37dMAg96SmU5Cjg/cBrgNOBNyQ5vRfPJQ0jc0CTzhyQzAPJHJDMAw2/XvUUOgvYXVXfAEhyDbABuKtHzycNG3NAk84ckMwDyRyQzIOhMrdX0mI9n9uNcw+jVNXKP2jyi8D6qvonzfYbgR+vqje3HbMJ2NRsvgi4e8UDWVknAt8edBBDZNjr4wVV9bxBPXknOdCUj1IeDPtr3m+jUB8Dy4MxzQEYjde9X0ahLvxbsPJG4XXvp2GvD3OgN4b9de+nUaiLccqDUajvQ0Yl1kmIc9Ec6FVPocxTNqv1qaq2Adt69PwrLsltVbVu0HEMC+tjSUvmAIxWHviaz2Z9LGnscgB83dtZFx0ZuzzwdZ/N+ljS2OUA+Lq3sy46smJ5MEr1PSqxGmfvVh/bC6xu2z4VeKBHzyUNI3NAk84ckMwDyRyQzAMNuV41Cn0ZWJvktCTPADYC1/XouaRhZA5o0pkDknkgmQOSeaAh15PhY1V1MMmbgc/RWnbvyqra2Yvn6qOR6dLaJ9bHIsyBiWB9LGJMcwB83dtZF0sY0zzwdZ/N+ljEmOYA+Lq3sy6WsMJ5MEr1PSqxTnycPZloWpIkSZIkScOtV8PHJEmSJEmSNMRsFJIkSZIkSZpANgotIsnqJF9IsivJziRvHXRMwyDJUUn+PMmnBx2Les88OJw5MFnMgfmZB5PFPDicOTBZzIH5mQf9k2R9kruT7E6yZdDxHJLkyiT7k3ytreyEJDckuae5PX6QMTYxzZvDwxZrkmcmuTXJXzRx/kav47RRaHEHgc1V9WLgFcAlSU4fcEzD4K3ArkEHob4xDw5nDkwWc2B+5sFkMQ8OZw5MFnNgfuZBHyQ5Cng/8BrgdOANQ/T+uwpYP6dsC3BjVa0Fbmy2B22hHB62WB8HXllVLwPOBNYneQU9jNNGoUVU1b6q+kpz/zFaH3inDDaqwUpyKnAu8MFBx6L+MA9mMwcmjzlwOPNg8pgHs5kDk8ccOJx50FdnAbur6htV9X3gGmDDgGMCoKpuAh6eU7wB2N7c3w68rp8xzWeRHB6qWKvlQLN5dPOv6GGcNgp1KMka4OXALQMOZdDeDbwd+MGA49AAmAeAOTDRzIEnvRvzYGKZB4A5MNHMgSe9G/OgX04B7m/b3stwN0pOVdU+aDXGACcNOJ5Z5uTw0MXaDMu8A9gP3FBVPY3TRqEOJHkW8AngbVX16KDjGZQk5wH7q+r2Qcei/jMPzIFJZw60mAeTzTwwByadOdBiHvRd5imrvkcxBkYhh6vqiao6EzgVOCvJS3v5fDYKLSHJ0bTeNFdX1ScHHc+AnQ38fJI9tLosvjLJRwYbkvrBPHiSOTChzIFZzIMJZR48yRyYUObALOZBf+0FVrdtnwo8MKBYOvFgklUAze3+AccDLJjDQxkrQFU9AszQmrOpZ3GmygbGhSQJrfF6D1fV2wYczlBJMg38i6o6b8ChqMfMg/mZA5PDHFiYeTA5zIP5mQOTwxxYmHnQe0meDvx34BzgW8CXgV+qqp0DDazRDMf6dFW9tNn+98BDVbW1WSnthKp6+4BjnDeHhy3WJM8D/mdVPZLkWOCPgd8CfrpXcdpTaHFnA2+k1fJ9R/PvtYMOSuoz80CTzhyQzAPJHNDAVNVB4M3A52hNkLxjiBqEPgbcDLwoyd4kFwNbgVcluQd4VbM9aAvl8LDFugr4QpKv0mr8u6GqPk0P47SnkCRJkiRJ0gSyp5AkSZIkSdIEslFIkiRJkiRpAtkoNKSSbExyS5LvJdnf3P+1tPxMki8k+W4z4780dpbIgX+Z5GtJHktyb5J/Oeh4pV5YIg/eluQbSR5N8kCS324mopQkSZI6YqPQEEqyGXgP8O+B5wNTwJtoTY71DOB7wJWAX4Q1ljrIgQC/AhxPa4nGNyfZOJhopd7oIA/+C/BjVfUc4KXAy4C3DCZaSZIkjSInmh4ySX4IeAD4lar6xBLH/izwwapa04/YpH5YTg60nfM7tD7P/llPg5P6ZLl5kORvAx8H/ntV/Vqv45MkSdJ4sKfQ8PkJ4Bjg2kEHIg3IsnIgSYC/BwzFspzSCukoD5L8UpJHgW/T6in0e32ITZIkSWPCRqHhcyLw7ao6eKggyX9L8kiSv07yUwOMTeqH5ebAr9P6LPtPfYxR6rWO8qCqPtoMH3sh8LvAg4MJV5IkSaPIRqHh8xBwYvtkoVX1k1X13Gafr5nGXcc5kOTNtOYWOreqHu93oFIPLetvQVXdQ6u33Af6GaQkSZJGmw0Mw+dm4HFgw6ADkQakoxxI8o+BLcA5VbW3H4FJfdTN34KnAz/Sm3AkSZI0jmwUGjJV9QjwG8AHkvxikmcleVqSM4HjAJrtZwJHtzbzzCTPGFjQ0grqMAcuAP4t8Kqq+sbAgpV6pMM8+CdJTmrunw5cBtw4oJAlSZI0glx9bEg1X3rfSmuZ4e8B3wA+BFwF/CTwhTmn/GlVTfcxRKmnlsiBu4FTafWkOOQjVfWmPocp9dQSefB7wGuBZwF/BfwB8K+q6m8GEqwkSZJGjo1CkiRJkiRJE8jhY5IkSZIkSRPIRiFJkiRJkqQJZKOQJEmSJEnSBLJRSJIkSZIkaQI9fdABAJx44om1Zs2aefd973vf47jjjutvQCPCulnY7bff/u2qet6g41gO86A71s3CRi0PzIHuWDcLG7UckCRJ6rehaBRas2YNt91227z7ZmZmmJ6e7m9AI8K6WViSbw46huUyD7pj3Sxs1PLAHOiOdbOwUcsBSZKkfnP4mCRJkiRJ0gSyUUiStKQkq5N8IcmuJDuTvLUpPyHJDUnuaW6PbzvnsiS7k9yd5NWDi16SJEnSfGwUkiR14iCwuapeDLwCuCTJ6cAW4MaqWgvc2GzT7NsIvARYD3wgyVEDiVySJEnSvIZiTiF1bs2W65+8v/mMg1zUtr2QPVvP7WVIUl+tmfOeNw/6o6r2Afua+48l2QWcAmwAppvDtgMzwKVN+TVV9Thwb5LdwFnAzf2NfDz5t0CSJEkrwUYhSdKyJFkDvBy4BZhqGoyoqn1JTmoOOwX4Uttpe5uyuY+1CdgEMDU1xczMzLzPeeDAgQX3TaLNZxx88v7UsbO3F2L9SZIkaS4bhSRJHUvyLOATwNuq6tEkCx46T1kdVlC1DdgGsG7dulpoFS1X2Jrtojk9ha64c+k/53sumO5hRJIkSRpFzikkSepIkqNpNQhdXVWfbIofTLKq2b8K2N+U7wVWt51+KvBAv2KVJEmStDQbhSRJS0qrS9CHgF1V9a62XdcBFzb3LwSubSvfmOSYJKcBa4Fb+xWvJEmSpKU5fEyS1ImzgTcCdya5oyl7B7AV2JHkYuA+4HyAqtqZZAdwF62Vyy6pqif6HrUkSZKkBdkoJElaUlV9kfnnCQI4Z4FzLgcu71lQkiRJko6Iw8ckSZIkSZImkI1CkiRJkiRJE8hGIUmSJEmSpAlko5AkSZIkSdIEcqJpSRNhzZbrl33Onq3n9iASSZIkSRoO9hSSJEmSJEmaQDYKSZIkSZIkTSAbhSRJkiRJkiaQjUKSJEmSJEkTyEYhSZIkSZKkCbRko1CSK5PsT/K1trJfT/KtJHc0/17btu+yJLuT3J3k1b0KXOon80CSJEmSNG46WZL+KuB9wIfnlP92Vf2H9oIkpwMbgZcAJwOfT/LCqnpiBWJVl7pZihtcjnuOqzAPJEmSJEljZMmeQlV1E/Bwh4+3Abimqh6vqnuB3cBZRxCfNBTMA0mSJEnSuOmkp9BC3pzkV4DbgM1V9R3gFOBLbcfsbcoOk2QTsAlgamqKmZmZeZ/kwIEDC+6bRJvPOPjk/aljZ2+vNOu9I+ZBn819z/cyD6xzSZIkSeOs20ah/wj8G6Ca2yuAfwxknmNrvgeoqm3ANoB169bV9PT0vE80MzPDQvsm0UVtQ8E2n3GQK+48kna9xe25YLpnjz0mzIMBuGjOcMhe5oE5IEmSJGmcdbX6WFU9WFVPVNUPgN/nqaExe4HVbYeeCjxwZCFKw8k8kCRJkiSNsq4ahZKsatv8B8ChFZmuAzYmOSbJacBa4NYjC1EaTuaBJokr8EmSJEnjZ8kxF0k+BkwDJybZC7wTmE5yJq0hMXuAXwWoqp1JdgB3AQeBS1xxSePAPJBcgW/UuRKlJEmS5lqyUaiq3jBP8YcWOf5y4PIjCUoaNuaBJl1V3ZRkTYeHP7kCH3BvkkMr8N3cq/gkSZIkLV9Xw8ckSWq8OclXm+FlxzdlpwD3tx2z4Ap8kiRJkgand0tXSZLG3RGvwJdkE7AJYGpqipmZmXmf6MCBAwvum0Sbzzj45P2pY2dvrzTrXZIkaXzZKCRJ6kpVPXjofpLfBz7dbHa8Al9VbQO2Aaxbt66mp6fnfa6ZmRkW2jeJLmqbH2jzGQe54s7e/Tnfc8F0zx5bkiRJg+XwMUlSV1yBT5IkSRpt9hSSJC3JFfgkSZKk8WOjkCRpSa7AJ0mSJI0fh49JkiRJkiRNIBuFJEmSJEmSJpCNQpIkSZIkSRPIRiFJkiRJkqQJZKOQJEmSJEnSBLJRSJIkSZIkaQLZKCRJkiRJkjSBbBSSJEmSJEmaQDYKSZIkSZIkTaCnDzoASZNrzZbrBx2CJEmSJE0sewpJkiRJkiRNIBuFJEmSJEmSJpDDxwbEYTOSJEmSJGmQlmwUSnIlcB6wv6pe2pSdAHwcWAPsAV5fVd9p9l0GXAw8Abylqj7Xk8jVc900XO3Zem4PIhk880CSJEmSNG466Sl0FfA+4MNtZVuAG6tqa5ItzfalSU4HNgIvAU4GPp/khVX1xMqGLfXdVZgHkiaQPxBIkiSNryXnFKqqm4CH5xRvALY397cDr2srv6aqHq+qe4HdwFkrE6o0OOaBJEmSJGncdDun0FRV7QOoqn1JTmrKTwG+1Hbc3qbsMEk2AZsApqammJmZmfeJDhw4sOC+Ubb5jINH/BhTx67M46ykcXytFmEeHKFhz4NxrHNJkiRJOmSlJ5rOPGU134FVtQ3YBrBu3bqanp6e9wFnZmZYaN8ou2gFJprefMZBrrhzuOYK33PB9KBDGAbmQYeGPQ98Pz/FebUkSZKk8dPtkvQPJlkF0Nzub8r3AqvbjjsVeKD78KShZh5oklwFrJ9TdmherbXAjc02c+bVWg98IMlR/QtVkiRJUie6bRS6DriwuX8hcG1b+cYkxyQ5DVgL3HpkIUpDyzzQxHBeLUmSJGn8dLIk/ceAaeDEJHuBdwJbgR1JLgbuA84HqKqdSXYAdwEHgUtccUnjwDyQ5uW8WgPSPo+W88tJkiSpW0s2ClXVGxbYdc4Cx18OXH4kQUnDxjyYTN0sxQ0ux43zavVc+3xczi8nSZKkbnU7fEySJOfVkiRJkkaYjUKSpG45r5YkSZI0woarv7kkaSg5r5YkSZI0fmwUkiQtyXm1JEmSpPHj8DFJkiRJkqQJZKOQJEmSJEnSBLJRSJIkSZIkaQLZKCRJkiRJkjSBbBSSJEmSJEmaQDYKSZIkSZIkTSAbhSRJkiRJkiaQjUKSJEmSJEkTyEYhSZIkSZKkCWSjkCRJkiRJ0gR6+qADkCRpUq3Zcv2gQ5AkSdIEs6eQJEmSJEnSBLKnkCRJWlHd9oDas/XcFY5EkiRJi7GnkCRJkiRJ0gSyUUiSJEmSJGkCHdHwsSR7gMeAJ4CDVbUuyQnAx4E1wB7g9VX1nSMLUxpe5oEkSZIkaRStRE+hn6mqM6tqXbO9BbixqtYCNzbb0rgzDyRJkiRJI6UXw8c2ANub+9uB1/XgOaRhZx5oYiTZk+TOJHckua0pOyHJDUnuaW6PH3SckiRJkmY70tXHCvjjJAX8XlVtA6aqah9AVe1LctJ8JybZBGwCmJqaYmZmZt4nOHDgwIL7RtnmMw4e8WNMHbsyj7OSxvG16oB50CXzYKz8TFV9u237UG+5rUm2NNuXDiY0SZIkSfM50kahs6vqgeYL7w1Jvt7pic0X520A69atq+np6XmPm5mZYaF9o+yiLpfrbbf5jINcceeRvoQra88F04MOYRDMgy6ZB2NtAzDd3N8OzGCjkCRJkjRUjuibVFU90NzuT/Ip4CzgwSSrmt4Rq4D9KxCnNLTMA8nect0a195y3RrH11iSJGmYdd0olOQ44GlV9Vhz/+8DvwlcB1wIbG1ur12JQKVhZB5oPmu66AG1Z+u5PYikb+wt16Vx7S3XLXvZSZIk9deRXEVOAZ9KcuhxPlpVn03yZWBHkouB+4DzjzzM4dbNF0CNDfNAE8/ecpIkSdJo6rpRqKq+AbxsnvKHgHOOJChpVJgHmnT2lpMkSZJG13j0N5ckDYq95SRJkqQRZaOQVlS3Q+lGfD4VaWLZW06SJEkaXU8bdACSJEmSJEnqPxuFJEmSJEmSJpCNQpIkSZIkSRPIRiFJkiRJkqQJZKOQJEmSJEnSBLJRSJIkSZIkaQLZKCRJkiRJkjSBnj7oACSNvjVbrh90CJIkSZKkZbKnkCRJkiRJ0gSyUUiSJEmSJGkC2SgkSZIkSZI0gZxTSJKGQLfzMu3Zeu4KRyINTjd5YA5IkiR1z0YhSdLQu/Nb3+WiZTYY2FggSZIkLc5GIUmSVoCr8EmSJGnUOKeQJEmSJEnSBLKnkIaC80hIkiRJktRfNgq1seu/hpXzqUiSJEmSVlrPho8lWZ/k7iS7k2zp1fNIw8oc0KQzByRJkqTh1pOeQkmOAt4PvArYC3w5yXVVdVcvnk8aNqOcA/aY00oY5RyQJEmSJkWvho+dBeyuqm8AJLkG2AAs+8tAN8NmwKEzGrgVywFpRI1sDtgwKkmSpEnRq0ahU4D727b3Aj/efkCSTcCmZvNAkrsXeKwTgW8vN4D81nLPGD1v6bJuxsUSr/EL+hTGQpbMAehtHkxCDoB5MMR5YA70iTmw6O5B/y2QJEkaar1qFMo8ZTVro2obsG3JB0puq6p1KxXYOLFuhtqSOQDmwUqwboaWOdAn1o0kSZK61auJpvcCq9u2TwUe6NFzScPIHNCkMwckSZKkIderRqEvA2uTnJbkGcBG4LoePZc0jMwBTTpzQJIkSRpyPRk+VlUHk7wZ+BxwFHBlVe3s8uGWHFYwwaybIbXCOQC+1ouxboaQOdBX1o0kSZK6kqrDpniQJEmSJEnSmOvV8DFJkiRJkiQNMRuFJEmSJEmSJtBQNwolWZ/k7iS7k2wZdDyDlOTKJPuTfK2t7IQkNyS5p7k9fpAxauWZA08xByaTOTCbeSBJkqSVNLSNQkmOAt4PvAY4HXhDktMHG9VAXQWsn1O2BbixqtYCNzbbGhPmwGGuwhyYKObAvK7CPJAkSdIKGdpGIeAsYHdVfaOqvg9cA2wYcEwDU1U3AQ/PKd4AbG/ubwde18+Y1HPmQBtzYCKZA3OYB5IkSVpJw9wodApwf9v23qZMT5mqqn0Aze1JA45HK8scWJo5MN7Mgc6YB5IkSerKMDcKZZ6y6nsU0uCYA5p05oAkSZLUQ8PcKLQXWN22fSrwwIBiGVYPJlkF0NzuH3A8WlnmwNLMgfFmDnTGPJAkSVJXhrlR6MvA2iSnJXkGsBG4bsAxDZvrgAub+xcC1w4wFq08c2Bp5sB4Mwc6Yx5IkiSpK6ka3p74SV4LvBs4Criyqi4fbESDk+RjwDRwIvAg8E7gPwM7gB8G7gPOr6q5E5BqhJkDTzEHJpM5MJt5IEmSpJU01I1CkiRJkiRJ6o1hHj4mSZIkSZKkHrFRSJIkSZIkaQLZKCRJkiRJkjSBbBSSJEmSJEmaQDYKSZIkSZIkTSAbhSRJkiRJkiaQjUKSJEmSJEkT6P8Hj7q7csNNKUQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x1080 with 36 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "import matplotlib.pyplot as plt\n",
    "std.hist(bins=10, figsize=(20,15))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5a22cb15",
   "metadata": {},
   "outputs": [],
   "source": [
    "std_one=std.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4291d315",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</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>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</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>1</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>649 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  ...  \\\n",
       "0         0    1   18        0        1        1     4     4     3     0  ...   \n",
       "1         0    1   17        0        1        0     1     1     3     4  ...   \n",
       "2         0    1   15        0        0        0     1     1     3     4  ...   \n",
       "3         0    1   15        0        1        0     4     2     1     2  ...   \n",
       "4         0    1   16        0        1        0     3     3     4     4  ...   \n",
       "..      ...  ...  ...      ...      ...      ...   ...   ...   ...   ...  ...   \n",
       "644       1    1   19        1        1        0     2     3     2     4  ...   \n",
       "645       1    1   18        0        0        0     3     1     0     2  ...   \n",
       "646       1    1   18        0        1        0     1     1     4     4  ...   \n",
       "647       1    0   17        0        0        0     3     1     2     2  ...   \n",
       "648       1    0   18        1        0        0     3     2     2     4  ...   \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",
       "644       5         4      2     1     2       5         4  10  11  10  \n",
       "645       4         3      4     1     1       1         4  15  15  16  \n",
       "646       1         1      1     1     1       5         6  11  12   9  \n",
       "647       2         4      5     3     4       2         6  10  10  10  \n",
       "648       4         4      1     3     4       5         4  10  11  11  \n",
       "\n",
       "[649 rows x 33 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "2d9cc70c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "519"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "ace13a65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train :\n",
      "     school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  ...  \\\n",
      "538       1    1   16        1        0        0     2     2     4     4  ...   \n",
      "411       0    0   17        0        0        1     4     4     1     4  ...   \n",
      "546       1    1   17        0        1        0     4     4     1     1  ...   \n",
      "565       1    0   16        1        0        0     1     1     3     4  ...   \n",
      "93        0    1   16        0        1        0     4     2     0     2  ...   \n",
      "\n",
      "     romantic  famrel  freetime  goout  Dalc  Walc  health  absences  G1  G2  \n",
      "538         1       4         3      3     2     2       5         2  11  11  \n",
      "411         0       4         4      2     1     2       4         2  12  15  \n",
      "546         0       5         2      5     1     1       5         0  13  15  \n",
      "565         0       4         5      5     2     4       5         0  10  10  \n",
      "93          0       5         3      3     1     1       1         2  13  14  \n",
      "\n",
      "[5 rows x 32 columns]\n",
      "(519, 32)\n",
      " \n"
     ]
    }
   ],
   "source": [
    "print('X_train :')\n",
    "print(X_train.head())\n",
    "print(X_train.shape)\n",
    "print(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "35f17bef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test :\n",
      "     school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  ...  \\\n",
      "482       1    1   15        1        0        0     1     1     3     4  ...   \n",
      "167       0    1   17        0        1        0     4     4     4     0  ...   \n",
      "339       0    0   18        1        0        0     3     2     2     4  ...   \n",
      "350       0    0   19        1        0        0     2     1     3     2  ...   \n",
      "390       0    1   18        1        1        0     2     2     3     4  ...   \n",
      "\n",
      "     romantic  famrel  freetime  goout  Dalc  Walc  health  absences  G1  G2  \n",
      "482         1       5         2      1     1     3       4         0   9  10  \n",
      "167         1       4         2      1     1     1       4         0  13  13  \n",
      "339         0       5         4      2     1     1       4         0  14  15  \n",
      "350         1       4         3      1     1     1       5         0   9  10  \n",
      "390         0       4         4      4     1     1       4         6  14  13  \n",
      "\n",
      "[5 rows x 32 columns]\n",
      "(130, 32)\n",
      " \n"
     ]
    }
   ],
   "source": [
    "print('X_test :')\n",
    "print(X_test.head())\n",
    "print(X_test.shape)\n",
    "print(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "5237609e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_train :\n",
      "538    11\n",
      "411    15\n",
      "546    16\n",
      "565     9\n",
      "93     14\n",
      "Name: G3, dtype: int64\n",
      "(519,)\n",
      " \n"
     ]
    }
   ],
   "source": [
    "print('y_train :')\n",
    "print(y_train.head())\n",
    "print(y_train.shape)\n",
    "print(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "27b80aa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y_test :\n",
      "482     9\n",
      "167    13\n",
      "339    15\n",
      "350    11\n",
      "390    14\n",
      "Name: G3, dtype: int64\n",
      "(130,)\n",
      " \n"
     ]
    }
   ],
   "source": [
    "print('y_test :')\n",
    "print(y_test.head())\n",
    "print(y_test.shape)\n",
    "print(' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "a6b7d80d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     103\n",
       "10     86\n",
       "8      83\n",
       "11     80\n",
       "7      72\n",
       "12     54\n",
       "6      40\n",
       "13     38\n",
       "14     25\n",
       "5      23\n",
       "15     20\n",
       "16     14\n",
       "4       3\n",
       "17      1\n",
       "18      0\n",
       "0       0\n",
       "1       0\n",
       "3       0\n",
       "2       0\n",
       "19      0\n",
       "Name: G1_cat, dtype: int64"
      ]
     },
     "execution_count": 120,
     "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": [
    "std[\"G1_cat\"] = pd.cut(std[\"G2\"], bins=[0, 1, 2, 3, 4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21], labels=[0,1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15,16,17,18,19])\n",
    "std[\"G1_cat\"].describe()\n",
    "std[\"G1_cat\"].hist()\n",
    "std[\"G1_cat\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "8ff4fe2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     103\n",
       "10     86\n",
       "8      83\n",
       "11     80\n",
       "7      72\n",
       "12     54\n",
       "6      40\n",
       "13     38\n",
       "14     25\n",
       "5      23\n",
       "15     20\n",
       "16     14\n",
       "4       3\n",
       "17      1\n",
       "18      0\n",
       "0       0\n",
       "1       0\n",
       "3       0\n",
       "2       0\n",
       "19      0\n",
       "Name: G2_cat, dtype: int64"
      ]
     },
     "execution_count": 121,
     "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": [
    "std[\"G2_cat\"] = pd.cut(std[\"G2\"], bins=[0, 1, 2, 3, 4,5,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21], labels=[0,1, 2, 3, 4, 5,6,7,8,9,10,11,12,13,14,15,16,17,18,19])\n",
    "std[\"G2_cat\"].describe()\n",
    "std[\"G2_cat\"].hist()\n",
    "std[\"G2_cat\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "859a5484",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>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",
       "      <th>G1_cat</th>\n",
       "      <th>G2_cat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</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",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</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",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</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",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</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",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>649 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  ...  \\\n",
       "0         0    1   18        0        1        1     4     4     3     0  ...   \n",
       "1         0    1   17        0        1        0     1     1     3     4  ...   \n",
       "2         0    1   15        0        0        0     1     1     3     4  ...   \n",
       "3         0    1   15        0        1        0     4     2     1     2  ...   \n",
       "4         0    1   16        0        1        0     3     3     4     4  ...   \n",
       "..      ...  ...  ...      ...      ...      ...   ...   ...   ...   ...  ...   \n",
       "644       1    1   19        1        1        0     2     3     2     4  ...   \n",
       "645       1    1   18        0        0        0     3     1     0     2  ...   \n",
       "646       1    1   18        0        1        0     1     1     4     4  ...   \n",
       "647       1    0   17        0        0        0     3     1     2     2  ...   \n",
       "648       1    0   18        1        0        0     3     2     2     4  ...   \n",
       "\n",
       "     goout  Dalc  Walc  health  absences  G1  G2  G3  G1_cat  G2_cat  \n",
       "0        4     1     1       3         4   0  11  11       9       9  \n",
       "1        3     1     1       3         2   9  11  11       9       9  \n",
       "2        2     2     3       3         6  12  13  12      11      11  \n",
       "3        2     1     1       5         0  14  14  14      12      12  \n",
       "4        2     1     2       5         0  11  13  13      11      11  \n",
       "..     ...   ...   ...     ...       ...  ..  ..  ..     ...     ...  \n",
       "644      2     1     2       5         4  10  11  10       9       9  \n",
       "645      4     1     1       1         4  15  15  16      13      13  \n",
       "646      1     1     1       5         6  11  12   9      10      10  \n",
       "647      5     3     4       2         6  10  10  10       8       8  \n",
       "648      1     3     4       5         4  10  11  11       9       9  \n",
       "\n",
       "[649 rows x 35 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "std\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "739b97f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<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>school</th>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.018211</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.031597</td>\n",
       "      <td>0.034666</td>\n",
       "      <td>0.044632</td>\n",
       "      <td>0.047169</td>\n",
       "      <td>0.014169</td>\n",
       "      <td>-0.058599</td>\n",
       "      <td>-0.163933</td>\n",
       "      <td>-0.292626</td>\n",
       "      <td>-0.269776</td>\n",
       "      <td>-0.284294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>0.083050</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.043662</td>\n",
       "      <td>0.025503</td>\n",
       "      <td>0.098205</td>\n",
       "      <td>0.064700</td>\n",
       "      <td>-0.119127</td>\n",
       "      <td>-0.083913</td>\n",
       "      <td>0.114223</td>\n",
       "      <td>0.054325</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.083473</td>\n",
       "      <td>-0.146305</td>\n",
       "      <td>-0.058178</td>\n",
       "      <td>-0.282696</td>\n",
       "      <td>-0.320785</td>\n",
       "      <td>-0.139547</td>\n",
       "      <td>-0.021336</td>\n",
       "      <td>0.104109</td>\n",
       "      <td>0.104005</td>\n",
       "      <td>0.129077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.087170</td>\n",
       "      <td>0.043662</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025848</td>\n",
       "      <td>0.002470</td>\n",
       "      <td>0.005631</td>\n",
       "      <td>-0.107832</td>\n",
       "      <td>-0.121050</td>\n",
       "      <td>0.096202</td>\n",
       "      <td>0.094789</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.020559</td>\n",
       "      <td>-0.004910</td>\n",
       "      <td>0.112805</td>\n",
       "      <td>0.134768</td>\n",
       "      <td>0.086357</td>\n",
       "      <td>-0.008750</td>\n",
       "      <td>0.149998</td>\n",
       "      <td>-0.174322</td>\n",
       "      <td>-0.107119</td>\n",
       "      <td>-0.106505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>address</th>\n",
       "      <td>0.354520</td>\n",
       "      <td>0.025503</td>\n",
       "      <td>0.025848</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.046113</td>\n",
       "      <td>-0.094635</td>\n",
       "      <td>-0.190320</td>\n",
       "      <td>-0.141493</td>\n",
       "      <td>0.128512</td>\n",
       "      <td>0.069587</td>\n",
       "      <td>...</td>\n",
       "      <td>0.033897</td>\n",
       "      <td>0.036647</td>\n",
       "      <td>-0.015475</td>\n",
       "      <td>0.047304</td>\n",
       "      <td>0.012416</td>\n",
       "      <td>-0.003787</td>\n",
       "      <td>-0.073653</td>\n",
       "      <td>-0.157127</td>\n",
       "      <td>-0.154600</td>\n",
       "      <td>-0.167637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>famsize</th>\n",
       "      <td>-0.022252</td>\n",
       "      <td>0.098205</td>\n",
       "      <td>0.002470</td>\n",
       "      <td>0.046113</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.239608</td>\n",
       "      <td>0.014325</td>\n",
       "      <td>0.039538</td>\n",
       "      <td>0.059604</td>\n",
       "      <td>-0.045834</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004641</td>\n",
       "      <td>0.021257</td>\n",
       "      <td>0.004312</td>\n",
       "      <td>-0.060482</td>\n",
       "      <td>-0.081958</td>\n",
       "      <td>-0.002448</td>\n",
       "      <td>-0.004645</td>\n",
       "      <td>-0.047230</td>\n",
       "      <td>-0.038891</td>\n",
       "      <td>-0.045016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pstatus</th>\n",
       "      <td>-0.028120</td>\n",
       "      <td>0.064700</td>\n",
       "      <td>0.005631</td>\n",
       "      <td>-0.094635</td>\n",
       "      <td>-0.239608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.057174</td>\n",
       "      <td>0.031856</td>\n",
       "      <td>0.025812</td>\n",
       "      <td>0.057947</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.051303</td>\n",
       "      <td>-0.037585</td>\n",
       "      <td>-0.031086</td>\n",
       "      <td>-0.041513</td>\n",
       "      <td>-0.070976</td>\n",
       "      <td>-0.012638</td>\n",
       "      <td>0.117492</td>\n",
       "      <td>-0.015251</td>\n",
       "      <td>-0.018689</td>\n",
       "      <td>0.000754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medu</th>\n",
       "      <td>-0.254787</td>\n",
       "      <td>-0.119127</td>\n",
       "      <td>-0.107832</td>\n",
       "      <td>-0.190320</td>\n",
       "      <td>0.014325</td>\n",
       "      <td>0.057174</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.647477</td>\n",
       "      <td>-0.536829</td>\n",
       "      <td>-0.236021</td>\n",
       "      <td>...</td>\n",
       "      <td>0.024421</td>\n",
       "      <td>-0.019686</td>\n",
       "      <td>0.009536</td>\n",
       "      <td>-0.007018</td>\n",
       "      <td>-0.019766</td>\n",
       "      <td>0.004614</td>\n",
       "      <td>-0.008577</td>\n",
       "      <td>0.260472</td>\n",
       "      <td>0.264035</td>\n",
       "      <td>0.240151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fedu</th>\n",
       "      <td>-0.209806</td>\n",
       "      <td>-0.083913</td>\n",
       "      <td>-0.121050</td>\n",
       "      <td>-0.141493</td>\n",
       "      <td>0.039538</td>\n",
       "      <td>0.031856</td>\n",
       "      <td>0.647477</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.376360</td>\n",
       "      <td>-0.363574</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020256</td>\n",
       "      <td>0.006841</td>\n",
       "      <td>0.027690</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>0.038445</td>\n",
       "      <td>0.044910</td>\n",
       "      <td>0.029859</td>\n",
       "      <td>0.217501</td>\n",
       "      <td>0.225139</td>\n",
       "      <td>0.211800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mjob</th>\n",
       "      <td>0.151608</td>\n",
       "      <td>0.114223</td>\n",
       "      <td>0.096202</td>\n",
       "      <td>0.128512</td>\n",
       "      <td>0.059604</td>\n",
       "      <td>0.025812</td>\n",
       "      <td>-0.536829</td>\n",
       "      <td>-0.376360</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.313979</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.002603</td>\n",
       "      <td>-0.045453</td>\n",
       "      <td>0.012780</td>\n",
       "      <td>-0.011831</td>\n",
       "      <td>-0.059446</td>\n",
       "      <td>-0.067353</td>\n",
       "      <td>0.054078</td>\n",
       "      <td>-0.169317</td>\n",
       "      <td>-0.158552</td>\n",
       "      <td>-0.166790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fjob</th>\n",
       "      <td>0.018211</td>\n",
       "      <td>0.054325</td>\n",
       "      <td>0.094789</td>\n",
       "      <td>0.069587</td>\n",
       "      <td>-0.045834</td>\n",
       "      <td>0.057947</td>\n",
       "      <td>-0.236021</td>\n",
       "      <td>-0.363574</td>\n",
       "      <td>0.313979</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013539</td>\n",
       "      <td>0.040034</td>\n",
       "      <td>0.039938</td>\n",
       "      <td>-0.039808</td>\n",
       "      <td>0.006927</td>\n",
       "      <td>0.008617</td>\n",
       "      <td>0.041174</td>\n",
       "      <td>-0.093530</td>\n",
       "      <td>-0.085288</td>\n",
       "      <td>-0.065454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reason</th>\n",
       "      <td>0.257056</td>\n",
       "      <td>0.028519</td>\n",
       "      <td>0.017911</td>\n",
       "      <td>0.163674</td>\n",
       "      <td>0.055543</td>\n",
       "      <td>-0.065814</td>\n",
       "      <td>-0.100401</td>\n",
       "      <td>-0.060966</td>\n",
       "      <td>-0.014290</td>\n",
       "      <td>-0.074679</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009077</td>\n",
       "      <td>0.041952</td>\n",
       "      <td>0.015300</td>\n",
       "      <td>0.026597</td>\n",
       "      <td>0.015567</td>\n",
       "      <td>0.072961</td>\n",
       "      <td>-0.111391</td>\n",
       "      <td>-0.151003</td>\n",
       "      <td>-0.146615</td>\n",
       "      <td>-0.157546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guardian</th>\n",
       "      <td>0.033364</td>\n",
       "      <td>-0.019301</td>\n",
       "      <td>0.171760</td>\n",
       "      <td>0.026165</td>\n",
       "      <td>0.019892</td>\n",
       "      <td>-0.007587</td>\n",
       "      <td>-0.111274</td>\n",
       "      <td>0.006902</td>\n",
       "      <td>0.146910</td>\n",
       "      <td>-0.009709</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.036994</td>\n",
       "      <td>-0.003350</td>\n",
       "      <td>-0.025124</td>\n",
       "      <td>0.117029</td>\n",
       "      <td>0.004215</td>\n",
       "      <td>-0.003584</td>\n",
       "      <td>0.059490</td>\n",
       "      <td>-0.046779</td>\n",
       "      <td>-0.044246</td>\n",
       "      <td>-0.029474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>traveltime</th>\n",
       "      <td>0.252936</td>\n",
       "      <td>-0.040880</td>\n",
       "      <td>0.034490</td>\n",
       "      <td>0.344902</td>\n",
       "      <td>-0.012794</td>\n",
       "      <td>-0.040633</td>\n",
       "      <td>-0.265079</td>\n",
       "      <td>-0.208288</td>\n",
       "      <td>0.143728</td>\n",
       "      <td>0.102847</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009521</td>\n",
       "      <td>0.000937</td>\n",
       "      <td>0.057454</td>\n",
       "      <td>0.092824</td>\n",
       "      <td>0.057007</td>\n",
       "      <td>-0.048261</td>\n",
       "      <td>-0.008149</td>\n",
       "      <td>-0.154120</td>\n",
       "      <td>-0.154489</td>\n",
       "      <td>-0.127173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>studytime</th>\n",
       "      <td>-0.137857</td>\n",
       "      <td>0.206214</td>\n",
       "      <td>-0.008415</td>\n",
       "      <td>-0.062023</td>\n",
       "      <td>0.010945</td>\n",
       "      <td>0.008748</td>\n",
       "      <td>0.097006</td>\n",
       "      <td>0.050400</td>\n",
       "      <td>-0.027832</td>\n",
       "      <td>-0.037830</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004127</td>\n",
       "      <td>-0.068829</td>\n",
       "      <td>-0.075442</td>\n",
       "      <td>-0.137585</td>\n",
       "      <td>-0.214925</td>\n",
       "      <td>-0.056433</td>\n",
       "      <td>-0.118389</td>\n",
       "      <td>0.260875</td>\n",
       "      <td>0.240498</td>\n",
       "      <td>0.249789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>failures</th>\n",
       "      <td>0.113788</td>\n",
       "      <td>-0.073888</td>\n",
       "      <td>0.319968</td>\n",
       "      <td>0.063824</td>\n",
       "      <td>0.066068</td>\n",
       "      <td>0.009881</td>\n",
       "      <td>-0.172210</td>\n",
       "      <td>-0.165915</td>\n",
       "      <td>0.089040</td>\n",
       "      <td>0.083033</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.062645</td>\n",
       "      <td>0.108995</td>\n",
       "      <td>0.045078</td>\n",
       "      <td>0.105949</td>\n",
       "      <td>0.082266</td>\n",
       "      <td>0.035588</td>\n",
       "      <td>0.122779</td>\n",
       "      <td>-0.384210</td>\n",
       "      <td>-0.385782</td>\n",
       "      <td>-0.393316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>schoolsup</th>\n",
       "      <td>-0.123340</td>\n",
       "      <td>0.111202</td>\n",
       "      <td>-0.167841</td>\n",
       "      <td>-0.017956</td>\n",
       "      <td>0.056405</td>\n",
       "      <td>0.009456</td>\n",
       "      <td>-0.022168</td>\n",
       "      <td>0.023572</td>\n",
       "      <td>0.063127</td>\n",
       "      <td>-0.046903</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012038</td>\n",
       "      <td>-0.015611</td>\n",
       "      <td>-0.058124</td>\n",
       "      <td>-0.028076</td>\n",
       "      <td>-0.098275</td>\n",
       "      <td>0.022760</td>\n",
       "      <td>-0.059503</td>\n",
       "      <td>-0.071779</td>\n",
       "      <td>-0.056624</td>\n",
       "      <td>-0.066405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>famsup</th>\n",
       "      <td>-0.063720</td>\n",
       "      <td>0.129467</td>\n",
       "      <td>-0.101894</td>\n",
       "      <td>-0.005577</td>\n",
       "      <td>0.039819</td>\n",
       "      <td>-0.010203</td>\n",
       "      <td>0.120491</td>\n",
       "      <td>0.135191</td>\n",
       "      <td>-0.092705</td>\n",
       "      <td>-0.063976</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015228</td>\n",
       "      <td>0.003764</td>\n",
       "      <td>0.017262</td>\n",
       "      <td>-0.016844</td>\n",
       "      <td>-0.065605</td>\n",
       "      <td>0.018803</td>\n",
       "      <td>0.041980</td>\n",
       "      <td>0.038255</td>\n",
       "      <td>0.038141</td>\n",
       "      <td>0.059206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paid</th>\n",
       "      <td>-0.007905</td>\n",
       "      <td>-0.079300</td>\n",
       "      <td>-0.005458</td>\n",
       "      <td>0.030483</td>\n",
       "      <td>0.050253</td>\n",
       "      <td>-0.015923</td>\n",
       "      <td>0.113973</td>\n",
       "      <td>0.094628</td>\n",
       "      <td>-0.022049</td>\n",
       "      <td>0.027030</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031937</td>\n",
       "      <td>-0.049574</td>\n",
       "      <td>-0.006683</td>\n",
       "      <td>0.051986</td>\n",
       "      <td>0.035682</td>\n",
       "      <td>0.063203</td>\n",
       "      <td>-0.035959</td>\n",
       "      <td>-0.062784</td>\n",
       "      <td>-0.033925</td>\n",
       "      <td>-0.054898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>activities</th>\n",
       "      <td>-0.088604</td>\n",
       "      <td>-0.124707</td>\n",
       "      <td>-0.054279</td>\n",
       "      <td>0.009278</td>\n",
       "      <td>0.014790</td>\n",
       "      <td>-0.101555</td>\n",
       "      <td>0.119354</td>\n",
       "      <td>0.079700</td>\n",
       "      <td>-0.118474</td>\n",
       "      <td>-0.057043</td>\n",
       "      <td>...</td>\n",
       "      <td>0.057597</td>\n",
       "      <td>0.150329</td>\n",
       "      <td>0.088582</td>\n",
       "      <td>0.022592</td>\n",
       "      <td>0.032824</td>\n",
       "      <td>0.013001</td>\n",
       "      <td>-0.015115</td>\n",
       "      <td>0.080123</td>\n",
       "      <td>0.067154</td>\n",
       "      <td>0.059791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nursery</th>\n",
       "      <td>0.004659</td>\n",
       "      <td>0.043603</td>\n",
       "      <td>-0.021441</td>\n",
       "      <td>-0.018077</td>\n",
       "      <td>-0.100686</td>\n",
       "      <td>0.032724</td>\n",
       "      <td>0.125951</td>\n",
       "      <td>0.074863</td>\n",
       "      <td>-0.107690</td>\n",
       "      <td>-0.011730</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041055</td>\n",
       "      <td>-0.007096</td>\n",
       "      <td>0.018679</td>\n",
       "      <td>-0.078376</td>\n",
       "      <td>-0.075748</td>\n",
       "      <td>0.001701</td>\n",
       "      <td>-0.040574</td>\n",
       "      <td>0.031172</td>\n",
       "      <td>0.039867</td>\n",
       "      <td>0.028752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>higher</th>\n",
       "      <td>-0.136112</td>\n",
       "      <td>0.058134</td>\n",
       "      <td>-0.265497</td>\n",
       "      <td>-0.076706</td>\n",
       "      <td>-0.004523</td>\n",
       "      <td>-0.022726</td>\n",
       "      <td>0.213896</td>\n",
       "      <td>0.191735</td>\n",
       "      <td>-0.134293</td>\n",
       "      <td>-0.064027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048239</td>\n",
       "      <td>-0.102618</td>\n",
       "      <td>-0.069105</td>\n",
       "      <td>-0.131663</td>\n",
       "      <td>-0.084327</td>\n",
       "      <td>0.017290</td>\n",
       "      <td>-0.129891</td>\n",
       "      <td>0.349030</td>\n",
       "      <td>0.331953</td>\n",
       "      <td>0.332172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>internet</th>\n",
       "      <td>-0.240486</td>\n",
       "      <td>-0.065911</td>\n",
       "      <td>0.013115</td>\n",
       "      <td>-0.175794</td>\n",
       "      <td>-0.013357</td>\n",
       "      <td>-0.059754</td>\n",
       "      <td>0.266052</td>\n",
       "      <td>0.183483</td>\n",
       "      <td>-0.206238</td>\n",
       "      <td>-0.022623</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082214</td>\n",
       "      <td>0.063268</td>\n",
       "      <td>0.092869</td>\n",
       "      <td>0.042811</td>\n",
       "      <td>0.060651</td>\n",
       "      <td>-0.022792</td>\n",
       "      <td>0.067301</td>\n",
       "      <td>0.139931</td>\n",
       "      <td>0.147909</td>\n",
       "      <td>0.150025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>romantic</th>\n",
       "      <td>0.072241</td>\n",
       "      <td>0.110144</td>\n",
       "      <td>0.178810</td>\n",
       "      <td>0.030939</td>\n",
       "      <td>0.032936</td>\n",
       "      <td>0.053828</td>\n",
       "      <td>-0.030992</td>\n",
       "      <td>-0.067675</td>\n",
       "      <td>0.059196</td>\n",
       "      <td>0.003189</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.044920</td>\n",
       "      <td>0.027112</td>\n",
       "      <td>-0.000520</td>\n",
       "      <td>0.062042</td>\n",
       "      <td>-0.019971</td>\n",
       "      <td>-0.018025</td>\n",
       "      <td>0.079489</td>\n",
       "      <td>-0.074973</td>\n",
       "      <td>-0.097937</td>\n",
       "      <td>-0.090583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>famrel</th>\n",
       "      <td>-0.031597</td>\n",
       "      <td>-0.083473</td>\n",
       "      <td>-0.020559</td>\n",
       "      <td>0.033897</td>\n",
       "      <td>-0.004641</td>\n",
       "      <td>-0.051303</td>\n",
       "      <td>0.024421</td>\n",
       "      <td>0.020256</td>\n",
       "      <td>-0.002603</td>\n",
       "      <td>0.013539</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.129216</td>\n",
       "      <td>0.089707</td>\n",
       "      <td>-0.075767</td>\n",
       "      <td>-0.093511</td>\n",
       "      <td>0.109559</td>\n",
       "      <td>-0.089534</td>\n",
       "      <td>0.048795</td>\n",
       "      <td>0.089588</td>\n",
       "      <td>0.063361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freetime</th>\n",
       "      <td>0.034666</td>\n",
       "      <td>-0.146305</td>\n",
       "      <td>-0.004910</td>\n",
       "      <td>0.036647</td>\n",
       "      <td>0.021257</td>\n",
       "      <td>-0.037585</td>\n",
       "      <td>-0.019686</td>\n",
       "      <td>0.006841</td>\n",
       "      <td>-0.045453</td>\n",
       "      <td>0.040034</td>\n",
       "      <td>...</td>\n",
       "      <td>0.129216</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.346352</td>\n",
       "      <td>0.109904</td>\n",
       "      <td>0.120244</td>\n",
       "      <td>0.084526</td>\n",
       "      <td>-0.018716</td>\n",
       "      <td>-0.094497</td>\n",
       "      <td>-0.106678</td>\n",
       "      <td>-0.122705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goout</th>\n",
       "      <td>0.044632</td>\n",
       "      <td>-0.058178</td>\n",
       "      <td>0.112805</td>\n",
       "      <td>-0.015475</td>\n",
       "      <td>0.004312</td>\n",
       "      <td>-0.031086</td>\n",
       "      <td>0.009536</td>\n",
       "      <td>0.027690</td>\n",
       "      <td>0.012780</td>\n",
       "      <td>0.039938</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089707</td>\n",
       "      <td>0.346352</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.245126</td>\n",
       "      <td>0.388680</td>\n",
       "      <td>-0.015741</td>\n",
       "      <td>0.085374</td>\n",
       "      <td>-0.074053</td>\n",
       "      <td>-0.079469</td>\n",
       "      <td>-0.087641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dalc</th>\n",
       "      <td>0.047169</td>\n",
       "      <td>-0.282696</td>\n",
       "      <td>0.134768</td>\n",
       "      <td>0.047304</td>\n",
       "      <td>-0.060482</td>\n",
       "      <td>-0.041513</td>\n",
       "      <td>-0.007018</td>\n",
       "      <td>0.000061</td>\n",
       "      <td>-0.011831</td>\n",
       "      <td>-0.039808</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.075767</td>\n",
       "      <td>0.109904</td>\n",
       "      <td>0.245126</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.616561</td>\n",
       "      <td>0.059067</td>\n",
       "      <td>0.172952</td>\n",
       "      <td>-0.195171</td>\n",
       "      <td>-0.189480</td>\n",
       "      <td>-0.204719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Walc</th>\n",
       "      <td>0.014169</td>\n",
       "      <td>-0.320785</td>\n",
       "      <td>0.086357</td>\n",
       "      <td>0.012416</td>\n",
       "      <td>-0.081958</td>\n",
       "      <td>-0.070976</td>\n",
       "      <td>-0.019766</td>\n",
       "      <td>0.038445</td>\n",
       "      <td>-0.059446</td>\n",
       "      <td>0.006927</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.093511</td>\n",
       "      <td>0.120244</td>\n",
       "      <td>0.388680</td>\n",
       "      <td>0.616561</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.114988</td>\n",
       "      <td>0.156373</td>\n",
       "      <td>-0.155649</td>\n",
       "      <td>-0.164852</td>\n",
       "      <td>-0.176619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health</th>\n",
       "      <td>-0.058599</td>\n",
       "      <td>-0.139547</td>\n",
       "      <td>-0.008750</td>\n",
       "      <td>-0.003787</td>\n",
       "      <td>-0.002448</td>\n",
       "      <td>-0.012638</td>\n",
       "      <td>0.004614</td>\n",
       "      <td>0.044910</td>\n",
       "      <td>-0.067353</td>\n",
       "      <td>0.008617</td>\n",
       "      <td>...</td>\n",
       "      <td>0.109559</td>\n",
       "      <td>0.084526</td>\n",
       "      <td>-0.015741</td>\n",
       "      <td>0.059067</td>\n",
       "      <td>0.114988</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.030235</td>\n",
       "      <td>-0.051647</td>\n",
       "      <td>-0.082179</td>\n",
       "      <td>-0.098851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>absences</th>\n",
       "      <td>-0.163933</td>\n",
       "      <td>-0.021336</td>\n",
       "      <td>0.149998</td>\n",
       "      <td>-0.073653</td>\n",
       "      <td>-0.004645</td>\n",
       "      <td>0.117492</td>\n",
       "      <td>-0.008577</td>\n",
       "      <td>0.029859</td>\n",
       "      <td>0.054078</td>\n",
       "      <td>0.041174</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.089534</td>\n",
       "      <td>-0.018716</td>\n",
       "      <td>0.085374</td>\n",
       "      <td>0.172952</td>\n",
       "      <td>0.156373</td>\n",
       "      <td>-0.030235</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.147149</td>\n",
       "      <td>-0.124745</td>\n",
       "      <td>-0.091379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1</th>\n",
       "      <td>-0.292626</td>\n",
       "      <td>0.104109</td>\n",
       "      <td>-0.174322</td>\n",
       "      <td>-0.157127</td>\n",
       "      <td>-0.047230</td>\n",
       "      <td>-0.015251</td>\n",
       "      <td>0.260472</td>\n",
       "      <td>0.217501</td>\n",
       "      <td>-0.169317</td>\n",
       "      <td>-0.093530</td>\n",
       "      <td>...</td>\n",
       "      <td>0.048795</td>\n",
       "      <td>-0.094497</td>\n",
       "      <td>-0.074053</td>\n",
       "      <td>-0.195171</td>\n",
       "      <td>-0.155649</td>\n",
       "      <td>-0.051647</td>\n",
       "      <td>-0.147149</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.864982</td>\n",
       "      <td>0.826387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G2</th>\n",
       "      <td>-0.269776</td>\n",
       "      <td>0.104005</td>\n",
       "      <td>-0.107119</td>\n",
       "      <td>-0.154600</td>\n",
       "      <td>-0.038891</td>\n",
       "      <td>-0.018689</td>\n",
       "      <td>0.264035</td>\n",
       "      <td>0.225139</td>\n",
       "      <td>-0.158552</td>\n",
       "      <td>-0.085288</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089588</td>\n",
       "      <td>-0.106678</td>\n",
       "      <td>-0.079469</td>\n",
       "      <td>-0.189480</td>\n",
       "      <td>-0.164852</td>\n",
       "      <td>-0.082179</td>\n",
       "      <td>-0.124745</td>\n",
       "      <td>0.864982</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.918548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G3</th>\n",
       "      <td>-0.284294</td>\n",
       "      <td>0.129077</td>\n",
       "      <td>-0.106505</td>\n",
       "      <td>-0.167637</td>\n",
       "      <td>-0.045016</td>\n",
       "      <td>0.000754</td>\n",
       "      <td>0.240151</td>\n",
       "      <td>0.211800</td>\n",
       "      <td>-0.166790</td>\n",
       "      <td>-0.065454</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063361</td>\n",
       "      <td>-0.122705</td>\n",
       "      <td>-0.087641</td>\n",
       "      <td>-0.204719</td>\n",
       "      <td>-0.176619</td>\n",
       "      <td>-0.098851</td>\n",
       "      <td>-0.091379</td>\n",
       "      <td>0.826387</td>\n",
       "      <td>0.918548</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              school       sex       age   address   famsize   Pstatus  \\\n",
       "school      1.000000  0.083050  0.087170  0.354520 -0.022252 -0.028120   \n",
       "sex         0.083050  1.000000  0.043662  0.025503  0.098205  0.064700   \n",
       "age         0.087170  0.043662  1.000000  0.025848  0.002470  0.005631   \n",
       "address     0.354520  0.025503  0.025848  1.000000  0.046113 -0.094635   \n",
       "famsize    -0.022252  0.098205  0.002470  0.046113  1.000000 -0.239608   \n",
       "Pstatus    -0.028120  0.064700  0.005631 -0.094635 -0.239608  1.000000   \n",
       "Medu       -0.254787 -0.119127 -0.107832 -0.190320  0.014325  0.057174   \n",
       "Fedu       -0.209806 -0.083913 -0.121050 -0.141493  0.039538  0.031856   \n",
       "Mjob        0.151608  0.114223  0.096202  0.128512  0.059604  0.025812   \n",
       "Fjob        0.018211  0.054325  0.094789  0.069587 -0.045834  0.057947   \n",
       "reason      0.257056  0.028519  0.017911  0.163674  0.055543 -0.065814   \n",
       "guardian    0.033364 -0.019301  0.171760  0.026165  0.019892 -0.007587   \n",
       "traveltime  0.252936 -0.040880  0.034490  0.344902 -0.012794 -0.040633   \n",
       "studytime  -0.137857  0.206214 -0.008415 -0.062023  0.010945  0.008748   \n",
       "failures    0.113788 -0.073888  0.319968  0.063824  0.066068  0.009881   \n",
       "schoolsup  -0.123340  0.111202 -0.167841 -0.017956  0.056405  0.009456   \n",
       "famsup     -0.063720  0.129467 -0.101894 -0.005577  0.039819 -0.010203   \n",
       "paid       -0.007905 -0.079300 -0.005458  0.030483  0.050253 -0.015923   \n",
       "activities -0.088604 -0.124707 -0.054279  0.009278  0.014790 -0.101555   \n",
       "nursery     0.004659  0.043603 -0.021441 -0.018077 -0.100686  0.032724   \n",
       "higher     -0.136112  0.058134 -0.265497 -0.076706 -0.004523 -0.022726   \n",
       "internet   -0.240486 -0.065911  0.013115 -0.175794 -0.013357 -0.059754   \n",
       "romantic    0.072241  0.110144  0.178810  0.030939  0.032936  0.053828   \n",
       "famrel     -0.031597 -0.083473 -0.020559  0.033897 -0.004641 -0.051303   \n",
       "freetime    0.034666 -0.146305 -0.004910  0.036647  0.021257 -0.037585   \n",
       "goout       0.044632 -0.058178  0.112805 -0.015475  0.004312 -0.031086   \n",
       "Dalc        0.047169 -0.282696  0.134768  0.047304 -0.060482 -0.041513   \n",
       "Walc        0.014169 -0.320785  0.086357  0.012416 -0.081958 -0.070976   \n",
       "health     -0.058599 -0.139547 -0.008750 -0.003787 -0.002448 -0.012638   \n",
       "absences   -0.163933 -0.021336  0.149998 -0.073653 -0.004645  0.117492   \n",
       "G1         -0.292626  0.104109 -0.174322 -0.157127 -0.047230 -0.015251   \n",
       "G2         -0.269776  0.104005 -0.107119 -0.154600 -0.038891 -0.018689   \n",
       "G3         -0.284294  0.129077 -0.106505 -0.167637 -0.045016  0.000754   \n",
       "\n",
       "                Medu      Fedu      Mjob      Fjob  ...    famrel  freetime  \\\n",
       "school     -0.254787 -0.209806  0.151608  0.018211  ... -0.031597  0.034666   \n",
       "sex        -0.119127 -0.083913  0.114223  0.054325  ... -0.083473 -0.146305   \n",
       "age        -0.107832 -0.121050  0.096202  0.094789  ... -0.020559 -0.004910   \n",
       "address    -0.190320 -0.141493  0.128512  0.069587  ...  0.033897  0.036647   \n",
       "famsize     0.014325  0.039538  0.059604 -0.045834  ... -0.004641  0.021257   \n",
       "Pstatus     0.057174  0.031856  0.025812  0.057947  ... -0.051303 -0.037585   \n",
       "Medu        1.000000  0.647477 -0.536829 -0.236021  ...  0.024421 -0.019686   \n",
       "Fedu        0.647477  1.000000 -0.376360 -0.363574  ...  0.020256  0.006841   \n",
       "Mjob       -0.536829 -0.376360  1.000000  0.313979  ... -0.002603 -0.045453   \n",
       "Fjob       -0.236021 -0.363574  0.313979  1.000000  ...  0.013539  0.040034   \n",
       "reason     -0.100401 -0.060966 -0.014290 -0.074679  ...  0.009077  0.041952   \n",
       "guardian   -0.111274  0.006902  0.146910 -0.009709  ... -0.036994 -0.003350   \n",
       "traveltime -0.265079 -0.208288  0.143728  0.102847  ... -0.009521  0.000937   \n",
       "studytime   0.097006  0.050400 -0.027832 -0.037830  ... -0.004127 -0.068829   \n",
       "failures   -0.172210 -0.165915  0.089040  0.083033  ... -0.062645  0.108995   \n",
       "schoolsup  -0.022168  0.023572  0.063127 -0.046903  ... -0.012038 -0.015611   \n",
       "famsup      0.120491  0.135191 -0.092705 -0.063976  ...  0.015228  0.003764   \n",
       "paid        0.113973  0.094628 -0.022049  0.027030  ...  0.031937 -0.049574   \n",
       "activities  0.119354  0.079700 -0.118474 -0.057043  ...  0.057597  0.150329   \n",
       "nursery     0.125951  0.074863 -0.107690 -0.011730  ...  0.041055 -0.007096   \n",
       "higher      0.213896  0.191735 -0.134293 -0.064027  ...  0.048239 -0.102618   \n",
       "internet    0.266052  0.183483 -0.206238 -0.022623  ...  0.082214  0.063268   \n",
       "romantic   -0.030992 -0.067675  0.059196  0.003189  ... -0.044920  0.027112   \n",
       "famrel      0.024421  0.020256 -0.002603  0.013539  ...  1.000000  0.129216   \n",
       "freetime   -0.019686  0.006841 -0.045453  0.040034  ...  0.129216  1.000000   \n",
       "goout       0.009536  0.027690  0.012780  0.039938  ...  0.089707  0.346352   \n",
       "Dalc       -0.007018  0.000061 -0.011831 -0.039808  ... -0.075767  0.109904   \n",
       "Walc       -0.019766  0.038445 -0.059446  0.006927  ... -0.093511  0.120244   \n",
       "health      0.004614  0.044910 -0.067353  0.008617  ...  0.109559  0.084526   \n",
       "absences   -0.008577  0.029859  0.054078  0.041174  ... -0.089534 -0.018716   \n",
       "G1          0.260472  0.217501 -0.169317 -0.093530  ...  0.048795 -0.094497   \n",
       "G2          0.264035  0.225139 -0.158552 -0.085288  ...  0.089588 -0.106678   \n",
       "G3          0.240151  0.211800 -0.166790 -0.065454  ...  0.063361 -0.122705   \n",
       "\n",
       "               goout      Dalc      Walc    health  absences        G1  \\\n",
       "school      0.044632  0.047169  0.014169 -0.058599 -0.163933 -0.292626   \n",
       "sex        -0.058178 -0.282696 -0.320785 -0.139547 -0.021336  0.104109   \n",
       "age         0.112805  0.134768  0.086357 -0.008750  0.149998 -0.174322   \n",
       "address    -0.015475  0.047304  0.012416 -0.003787 -0.073653 -0.157127   \n",
       "famsize     0.004312 -0.060482 -0.081958 -0.002448 -0.004645 -0.047230   \n",
       "Pstatus    -0.031086 -0.041513 -0.070976 -0.012638  0.117492 -0.015251   \n",
       "Medu        0.009536 -0.007018 -0.019766  0.004614 -0.008577  0.260472   \n",
       "Fedu        0.027690  0.000061  0.038445  0.044910  0.029859  0.217501   \n",
       "Mjob        0.012780 -0.011831 -0.059446 -0.067353  0.054078 -0.169317   \n",
       "Fjob        0.039938 -0.039808  0.006927  0.008617  0.041174 -0.093530   \n",
       "reason      0.015300  0.026597  0.015567  0.072961 -0.111391 -0.151003   \n",
       "guardian   -0.025124  0.117029  0.004215 -0.003584  0.059490 -0.046779   \n",
       "traveltime  0.057454  0.092824  0.057007 -0.048261 -0.008149 -0.154120   \n",
       "studytime  -0.075442 -0.137585 -0.214925 -0.056433 -0.118389  0.260875   \n",
       "failures    0.045078  0.105949  0.082266  0.035588  0.122779 -0.384210   \n",
       "schoolsup  -0.058124 -0.028076 -0.098275  0.022760 -0.059503 -0.071779   \n",
       "famsup      0.017262 -0.016844 -0.065605  0.018803  0.041980  0.038255   \n",
       "paid       -0.006683  0.051986  0.035682  0.063203 -0.035959 -0.062784   \n",
       "activities  0.088582  0.022592  0.032824  0.013001 -0.015115  0.080123   \n",
       "nursery     0.018679 -0.078376 -0.075748  0.001701 -0.040574  0.031172   \n",
       "higher     -0.069105 -0.131663 -0.084327  0.017290 -0.129891  0.349030   \n",
       "internet    0.092869  0.042811  0.060651 -0.022792  0.067301  0.139931   \n",
       "romantic   -0.000520  0.062042 -0.019971 -0.018025  0.079489 -0.074973   \n",
       "famrel      0.089707 -0.075767 -0.093511  0.109559 -0.089534  0.048795   \n",
       "freetime    0.346352  0.109904  0.120244  0.084526 -0.018716 -0.094497   \n",
       "goout       1.000000  0.245126  0.388680 -0.015741  0.085374 -0.074053   \n",
       "Dalc        0.245126  1.000000  0.616561  0.059067  0.172952 -0.195171   \n",
       "Walc        0.388680  0.616561  1.000000  0.114988  0.156373 -0.155649   \n",
       "health     -0.015741  0.059067  0.114988  1.000000 -0.030235 -0.051647   \n",
       "absences    0.085374  0.172952  0.156373 -0.030235  1.000000 -0.147149   \n",
       "G1         -0.074053 -0.195171 -0.155649 -0.051647 -0.147149  1.000000   \n",
       "G2         -0.079469 -0.189480 -0.164852 -0.082179 -0.124745  0.864982   \n",
       "G3         -0.087641 -0.204719 -0.176619 -0.098851 -0.091379  0.826387   \n",
       "\n",
       "                  G2        G3  \n",
       "school     -0.269776 -0.284294  \n",
       "sex         0.104005  0.129077  \n",
       "age        -0.107119 -0.106505  \n",
       "address    -0.154600 -0.167637  \n",
       "famsize    -0.038891 -0.045016  \n",
       "Pstatus    -0.018689  0.000754  \n",
       "Medu        0.264035  0.240151  \n",
       "Fedu        0.225139  0.211800  \n",
       "Mjob       -0.158552 -0.166790  \n",
       "Fjob       -0.085288 -0.065454  \n",
       "reason     -0.146615 -0.157546  \n",
       "guardian   -0.044246 -0.029474  \n",
       "traveltime -0.154489 -0.127173  \n",
       "studytime   0.240498  0.249789  \n",
       "failures   -0.385782 -0.393316  \n",
       "schoolsup  -0.056624 -0.066405  \n",
       "famsup      0.038141  0.059206  \n",
       "paid       -0.033925 -0.054898  \n",
       "activities  0.067154  0.059791  \n",
       "nursery     0.039867  0.028752  \n",
       "higher      0.331953  0.332172  \n",
       "internet    0.147909  0.150025  \n",
       "romantic   -0.097937 -0.090583  \n",
       "famrel      0.089588  0.063361  \n",
       "freetime   -0.106678 -0.122705  \n",
       "goout      -0.079469 -0.087641  \n",
       "Dalc       -0.189480 -0.204719  \n",
       "Walc       -0.164852 -0.176619  \n",
       "health     -0.082179 -0.098851  \n",
       "absences   -0.124745 -0.091379  \n",
       "G1          0.864982  0.826387  \n",
       "G2          1.000000  0.918548  \n",
       "G3          0.918548  1.000000  \n",
       "\n",
       "[33 rows x 33 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = std.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c30810a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='G1', ylabel='studytime'>"
      ]
     },
     "execution_count": 31,
     "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": [
    "std.plot(kind=\"scatter\", x=\"G1\", y=\"studytime\", alpha=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fc4e2010",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='G1', ylabel='failures'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IJks2F8yof+ZkjgMvjZI5mZtR/31Hh/nejn72HR2eUf+t+49w5w92s3X/kRn1f3T3If74gW08uvtQU9Y/NJplz6FhhkazM+pf7/jXuw3Vu/1IdMzdo1mw2VogA9zn7qsqTL8W+AhwLXAFcLu7XzHdcnt6ery3t3e2yz2jDZ3IsaP/OPm8k0waq7u7OGtuS03L2H8kw+adhxgPCrSkEqxf1c15C+aG7r/9wEvc/9MD5IIC6VSCm65czhuWnhW6/189sou7H/sFhYKTSBgfXXcRv/0r4T/yasPfPM7m3cdfvv++Ny7h8zeGf+7x67f/gK0Hx16+f9WK+fzd77y1Yev/0Z7DbHxi38vjt2HthVyxYkHo/vWOf73bUL3bj9TPzLa6e0+laZEdEbj7FuClKWa5gWJIuLs/CXSZ2ZKo6omrIF9gR/9x2lJJzm5vpS2VZHv/UE3P6rK5gM07D9HRmmLZ/HY6WlNs2tEf+pld5mSO+396gM62FMsXdNDZluK+J/eFfma67+gwdz/2C+a0JFg8bw5zWhLc8chzoY8Mtu4/wubdx0kAbakECeCbzxwM/cz80d2HXg6BiX+Yx58/FvrIoN71D41m2fjEPjpbU1ywoIPO1hR3b9kb+sig3vGvdxuqd/uR6DXzGsFS4MCk+32lttcws5vNrNfMegcHBxtS3Jkily+QzzutqSQArakk+byTqyEIMrmA8aBAe7r4DLA93cJ4UCAT8h/52MlxckGBztY0AJ2taXJBgWMnx0P133MwQ6HgtJf6t7emKRScPQczofr/6LmjAKRTiVf9nmifziO7DgOv/LMkytqjXv/ASI5cUGBeW/Hvn9dWHL+BkXA78nrHv95tqN7tR6LXzCCwCm0Vz1O5+0Z373H3nkWLFkVc1pklnUyQTBpjQR6AsSBPMmmkk+Ef+o50ipZUgtFccccxmhunJZWgIx3mm05h/pwW0qkEI2PFHdfIWI50KsH8OeFOLVy8pINEwhgt9R8dy5FIGBcv6QjV/60XFU+h5ILCq35PtE9n3cpzAJjY7RXK2qNe/+LONOlUguFs8e8fzhbHb3FnOlT/ese/3m2o3u1HotfMIOgDzpt0fxnQ36RazlipZILV3V1kgzwvjY6RDfKs7u4iVUMQtKVTrF/VTWYsoO/YKJmxgPWrumkL+Y/cMSfNTVcuZyQbsO9ohpFswE1XLqdjTrgd2fIF8/jouos4OV5gYPgkJ8cLfHTdRSxfMC9U/zefv5D3vXEJBSAbFChQPEf/5vMXhur/9kvO5aoV84FXQuCqFfN5+yXnNmT9Xe1tbFh7ISNjAS8czTAyFrBh7YV0tbeF6l/v+Ne7DdW7/Uj0IrtYDGBmy4GHqlwsXg/cyisXi+9w9zXTLVMXi2cmyBfI5Qukk4maQmCybC4gkwvoSKdm9E+cOZnj2Mlx5s9pCb0Tmmzf0WH2HMxw8ZKO0CEw2db9R/jRc0d560ULQu+EJ3t09yEe2XWYdSvPCR0Cs7n+odEsAyM5FnemQ4fAZPWOf73bUL3bj9RnqovFUb5q6CvA1cBC4DDwx0ALgLvfY2YG3AVcQ/H7kD/k7tPu4RUEIiK1myoIIotld3//NNMduCWq9YuISDh6Z7GISMwpCEREYk5BICIScwoCEZGYUxCIiMScgkBEJOYUBCIiMacgEBGJOQWBiEjMKQhERGJOQSAiEnMKAhGRmFMQiIjEnIJARCTmFAQiIjGnIBARiTkFgYhIzCkIRERiTkEgIhJzCgIRkZhTEIiIxJyCQEQk5hQEIiIxpyAQEYm5SIPAzK4xs91mttfMPlFh+tVmdtzMnin9fDLKekRE5LVSUS3YzJLAF4B3An3AU2b2oLv/rGzWx9z9uqjqEBGRqUV5RLAG2OvuP3f3HPBV4IYI1yciIjMQZRAsBQ5Mut9Xaiv3FjPbZmabzezSSgsys5vNrNfMegcHB6OoVUQktqIMAqvQ5mX3nwbOd/fLgDuBByotyN03unuPu/csWrRodqsUEYm5KIOgDzhv0v1lQP/kGdx92N0zpdsPAy1mtjDCmkREpEyUQfAUcJGZXWBmaeBG4MHJM5jZuWZmpdtrSvUcjbAmEREpE9mrhtw9MLNbge8CSeBed99pZh8uTb8H+A1gg5kFwEngRncvP30kIiIRstNtv9vT0+O9vb3NLkNE5LRiZlvdvafSNL2zWEQk5hQEIiIxpyAQEYk5BYGISMwpCEREYk5BICIScwoCEZGYUxCIiMScgkBEJOYUBCIiMacgEBGJOQWBiEjMKQhERGJOQSAiEnMKAhGRmFMQiIjEnIJARCTmFAQiIjGnIBARiTkFgYhIzCkIRERiTkEgIhJzCgIRkZhTEIiIxFwqyoWb2TXA7UAS+N/u/udl0600/VrgBPDv3P3pKGoJ8gVy+QLpZIJUUvk3E9lcQCYX0JFO0ZaufdMZGs0yMJJjcWearva2mvs/uvsQj+w6zLqV5/D2S86tuf+D/3SA72zv55rV3Vz/pvNq7v+Zb2/noZ39XHdpN7//3tU197/+s5t49gj80kJ48PfW19z/3Z/axO4RuKQTvvtHtfd/72c2sf0orF4A3/792vv/5p2beOpF+OWl8Pcfqb3/jX/5ME/+wrnydcZX/+O1Nfe/c/Oul8f/I+9ZWXP/Tdv6+O72ft69upv1ly2ruf+Pnx9gy+5B1l6yiLesWFxz/75jIzw/cIIVi+eybH5nzf2j3IeZu8/qAl9esFkS2AO8E+gDngLe7+4/mzTPtcBHKAbBFcDt7n7FVMvt6enx3t7emmoZOpFjR/9x8nknmTRWd3dx1tyW2v6gmNt/JMPmnYcYDwq0pBKsX9XNeQvmhu7/oz2H2fjEPnJBgXQqwYa1F3LFigWh+//67T9g68Gxl+9ftWI+f/c7bw3df+1/28QvMq/cX3lOO5s/fnXo/ss/selV9+cm4WefCr8zLO8PsO/P1T+sCz6xicl7qnmt8Oyfhu+/7r9v4ufDr9xftaSDh257e+j+H7j7H3ls/+jL99+1chEbP7gmdP8vP/4cd23Z9/I+6OPvuJgbrzg/dP/Z2IeZ2VZ376k0LcqnxmuAve7+c3fPAV8Fbiib5wbgPi96EugysyWzWUSQL7Cj/zhtqSRnt7fSlkqyvX+IIF+YzdWc0bK5gM07D9HRmmLZ/HY6WlNs2tFPNheE6j80mmXjE/vobE1xwYIOOltT3L1lL0Oj2VD9H9196OUQmNhgH3/+GI/uPhSq/4P/dOBVIQCw6/AoD/7TgVD9P/Pt7a9pO5Gv3F7J9Z997U5wqvZy7/5U5fmqtZd772cqz1etvdxv3ll5vmrt5W78y4drai935+ZdlD9dHR4rtoexaVvfq0IAYMfBDJu29YXq/+PnB14OgVRpA/zerkF+/PxAqP59x0a4a8s+5rYkWdI1l7ktST7/wz30HRsJ1b8R+7Aog2ApMPk/ra/UVus8mNnNZtZrZr2Dg4M1FZHLF8jnndZUEoDWVJJ83skpCELL5ALGgwLt6eIzkPZ0C+NBgUzIIBgYyZELCsxrSwMwry1NLigwMJIL1f+RXYeBVzbWRFn7dL6zvb+m9nIP7aw8X7X2cs8eqa293O4q+4tq7eW2H62tvdxTL9bWXu7JX1Q+61CtvVy94//dKo9ztfZyW3YX9zkTITDxe6J9Os8PnCCfdzpK239HW5p83nl+4ESo/o3Yh0UZBFahrfyRDzMP7r7R3XvcvWfRokU1FZFOJkgmjbEgD8BYkCeZNNK6ThBaRzpFSyrBaG4cgNHcOC2pBB0hrxMs7kyTTiUYzhZ3/MPZHOlUgsWd6VD91608B4CJzb5Q1j6da1Z319Re7rpLK89Xrb3cLy2srb3cJVVOJ1drL7e6yhm4au3lfvk1T82mbi935esq/ZtXby9X7/i/u8rjXK293NpLivucoLThTfyeaJ/OisVzSSaNTGn7z2RzJJPGisXhTq02Yh8W5d6wD5h8RW4ZUB7BYeapSyqZYHV3F9kgz0ujY2SDPKu7u3TBuAZt6RTrV3WTGQvoOzZKZixg/aru0BeMu9rb2LD2QkbGAl44mmFkLGDD2gtDXzB++yXnctWK+cArIXDVivmhLxhf/6bzWHlO+6vaVp7THvqC8e+/dzVzk69um5sk9AXjaheGw14wrnZhOOwF42oXhsNeMK52YTjsBeNqF4bDXjD+yHtWMq/11W3zWgl9wXj9ZctYtaTjVW2rlnSEvmD8lhWLedfKV4fBu1aGv2C8bH4nH3/HxZwYz3Nw6AQnxvN8/B0Xh75g3Ih9WJQXi1MULxa/A3iR4sXif+PuOyfNsx64lVcuFt/h7lNegZnJxWLQq4Zmg141pFcN6VVDp++rhqa6WBxZEJRWfC3wFxRfPnqvu3/KzD4M4O73lF4+ehdwDcWXj37I3afcy880CERE4myqIIj0fQTu/jDwcFnbPZNuO3BLlDWIiMjUdI5ERCTmFAQiIjGnIBARiTkFgYhIzEX6qqEomNkgsH+G3RcCId/P2RSnen1w6teo+uqj+upzKtd3vrtXfBfcaRcE9TCz3movnzoVnOr1walfo+qrj+qrz6leXzU6NSQiEnMKAhGRmItbEGxsdgHTONXrg1O/RtVXH9VXn1O9vopidY1AREReK25HBCIiUkZBICISc2dkEJjZNWa228z2mtknKkw3M7ujNP1ZM7u8gbWdZ2b/YGa7zGynmd1WYZ6rzey4mT1T+vlko+orrX+fmW0vrfs1H/Xa5PG7ZNK4PGNmw2b2sbJ5Gj5+ZnavmQ2Y2Y5JbWeb2ffN7LnS7/lV+k65vUZY32fM7J9Lj+E3zayrSt8pt4cI6/sTM3tx0uNY8bOrmzh+X5tU2z4ze6ZK38jHr27ufkb9UPzI6+eB1wNpYBvwhrJ5rgU2U/yGtCuBnzSwviXA5aXbnRS/s6G8vquBh5o4hvuAhVNMb9r4VXisD1F8o0xTxw9YC1wO7JjU9j+BT5RufwL4dJW/YcrtNcL63gWkSrc/Xam+MNtDhPX9CfB7IbaBpoxf2fT/BXyyWeNX78+ZeESwBtjr7j939xzwVeCGsnluAO7zoieBLjNb0oji3P2guz9duj0C7KLC9zSf4po2fmXeATzv7jN9p/mscfctwEtlzTcAXyrd/hLwqxW6htleI6nP3b/n7hNfPP0kxW8IbIoq4xdG08ZvQul7Vf4V8JXZXm+jnIlBsBQ4MOl+H6/d0YaZJ3Jmthx4E/CTCpPfYmbbzGyzmV3a2Mpw4HtmttXMbq4w/ZQYP+BGqv/zNXP8Jpzj7geh+AQAqPS1VqfKWP42xaO8SqbbHqJ0a+nU1b1VTq2dCuP3K8Bhd3+uyvRmjl8oZ2IQVPpG7PLXyIaZJ1Jm1gF8HfiYuw+XTX6a4umOy4A7gQcaWRvwNne/HHgPcIuZrS2bfiqMXxq4Hvj7CpObPX61OBXG8o+AALi/yizTbQ9RuRtYAbwROEjx9Eu5po8f8H6mPhpo1viFdiYGQR8w+QtplwH9M5gnMmbWQjEE7nf3b5RPd/dhd8+Ubj8MtJjZwkbV5+79pd8DwDcpHn5P1tTxK3kP8LS7Hy6f0Ozxm+TwxCmz0u+BCvM0e1v8IHAd8FteOqFdLsT2EAl3P+zueXcvAF+sst5mj18K+DXga9Xmadb41eJMDIKngIvM7ILSs8YbgQfL5nkQuKn06pcrgeMTh/BRK51P/Gtgl7t/rso855bmw8zWUHycjjaovnYz65y4TfGC4o6y2Zo2fpNUfRbWzPEr8yDwwdLtDwLfqjBPmO01EmZ2DfAHwPXufqLKPGG2h6jqm3zd6X1V1tu08Sv5l8A/u3tfpYnNHL+aNPtqdRQ/FF/Vsofiqwn+qNT2YeDDpdsGfKE0fTvQ08DarqJ46Pos8Ezp59qy+m4FdlJ8BcSTwFsbWN/rS+vdVqrhlBq/0vrnUtyxnzWpranjRzGUDgLjFJ+l/ntgAfBD4LnS77NL83YDD0+1vTaovr0Uz69PbIf3lNdXbXtoUH1fLm1fz1LcuS85lcav1P63E9vdpHkbPn71/ugjJkREYu5MPDUkIiI1UBCIiMScgkBEJOYUBCIiMacgEBGJOQWByAyY2Tlm9n/M7Oeljw74sZm9z8wWWPHTZTNmdlez6xQJQ0EgUqPSm9UeALa4++vd/c0U38i0DMgC/xX4veZVKFIbBYFI7dYBOXe/Z6LB3fe7+53uPuruj1MMBJHTgoJApHaXUvxgO5EzgoJApE5m9oXSR14/1exaRGZCQSBSu50Uv60KAHe/heKX5CxqWkUidVAQiNTuEaDNzDZMapvbrGJE6qUPnROZgdJHJH8euAIYBEYpfnrn18xsHzCP4nfoDgHvcvefNalUkWkpCEREYk6nhkREYk5BICIScwoCEZGYUxCIiMScgkBEJOYUBCIiMacgEBGJuf8POKydV07bWMsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "std.plot(kind=\"scatter\", x=\"G1\", y=\"failures\", alpha=.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "77e872e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fd0f8085d90>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "std.plot(kind=\"scatter\", x=\"Medu\", y=\"G1\", alpha=0.4, s=std[\"health\"]/100, label=\"performance\", figsize=(10,7),\n",
    "c=std['famrel']/100, cmap=plt.get_cmap(\"jet\"), colorbar=True, )\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "413f1be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G1            1.000000\n",
       "G2            0.864982\n",
       "G3            0.826387\n",
       "higher        0.349030\n",
       "studytime     0.260875\n",
       "Medu          0.260472\n",
       "Fedu          0.217501\n",
       "internet      0.139931\n",
       "sex           0.104109\n",
       "activities    0.080123\n",
       "famrel        0.048795\n",
       "famsup        0.038255\n",
       "nursery       0.031172\n",
       "Pstatus      -0.015251\n",
       "guardian     -0.046779\n",
       "famsize      -0.047230\n",
       "health       -0.051647\n",
       "paid         -0.062784\n",
       "schoolsup    -0.071779\n",
       "goout        -0.074053\n",
       "romantic     -0.074973\n",
       "Fjob         -0.093530\n",
       "freetime     -0.094497\n",
       "absences     -0.147149\n",
       "reason       -0.151003\n",
       "traveltime   -0.154120\n",
       "Walc         -0.155649\n",
       "address      -0.157127\n",
       "Mjob         -0.169317\n",
       "age          -0.174322\n",
       "Dalc         -0.195171\n",
       "school       -0.292626\n",
       "failures     -0.384210\n",
       "Name: G1, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"G1\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bb93d7bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G2            1.000000\n",
       "G3            0.918548\n",
       "G1            0.864982\n",
       "higher        0.331953\n",
       "Medu          0.264035\n",
       "studytime     0.240498\n",
       "Fedu          0.225139\n",
       "internet      0.147909\n",
       "sex           0.104005\n",
       "famrel        0.089588\n",
       "activities    0.067154\n",
       "nursery       0.039867\n",
       "famsup        0.038141\n",
       "Pstatus      -0.018689\n",
       "paid         -0.033925\n",
       "famsize      -0.038891\n",
       "guardian     -0.044246\n",
       "schoolsup    -0.056624\n",
       "goout        -0.079469\n",
       "health       -0.082179\n",
       "Fjob         -0.085288\n",
       "romantic     -0.097937\n",
       "freetime     -0.106678\n",
       "age          -0.107119\n",
       "absences     -0.124745\n",
       "reason       -0.146615\n",
       "traveltime   -0.154489\n",
       "address      -0.154600\n",
       "Mjob         -0.158552\n",
       "Walc         -0.164852\n",
       "Dalc         -0.189480\n",
       "school       -0.269776\n",
       "failures     -0.385782\n",
       "Name: G2, dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"G2\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "219fe198",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G3            1.000000\n",
       "G2            0.918548\n",
       "G1            0.826387\n",
       "higher        0.332172\n",
       "studytime     0.249789\n",
       "Medu          0.240151\n",
       "Fedu          0.211800\n",
       "internet      0.150025\n",
       "sex           0.129077\n",
       "famrel        0.063361\n",
       "activities    0.059791\n",
       "famsup        0.059206\n",
       "nursery       0.028752\n",
       "Pstatus       0.000754\n",
       "guardian     -0.029474\n",
       "famsize      -0.045016\n",
       "paid         -0.054898\n",
       "Fjob         -0.065454\n",
       "schoolsup    -0.066405\n",
       "goout        -0.087641\n",
       "romantic     -0.090583\n",
       "absences     -0.091379\n",
       "health       -0.098851\n",
       "age          -0.106505\n",
       "freetime     -0.122705\n",
       "traveltime   -0.127173\n",
       "reason       -0.157546\n",
       "Mjob         -0.166790\n",
       "address      -0.167637\n",
       "Walc         -0.176619\n",
       "Dalc         -0.204719\n",
       "school       -0.284294\n",
       "failures     -0.393316\n",
       "Name: G3, dtype: float64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"G3\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "0d21e9db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='G1', ylabel='G1'>,\n",
       "        <AxesSubplot:xlabel='G2', ylabel='G1'>,\n",
       "        <AxesSubplot:xlabel='G3', ylabel='G1'>,\n",
       "        <AxesSubplot:xlabel='higher', ylabel='G1'>,\n",
       "        <AxesSubplot:xlabel='studytime', ylabel='G1'>],\n",
       "       [<AxesSubplot:xlabel='G1', ylabel='G2'>,\n",
       "        <AxesSubplot:xlabel='G2', ylabel='G2'>,\n",
       "        <AxesSubplot:xlabel='G3', ylabel='G2'>,\n",
       "        <AxesSubplot:xlabel='higher', ylabel='G2'>,\n",
       "        <AxesSubplot:xlabel='studytime', ylabel='G2'>],\n",
       "       [<AxesSubplot:xlabel='G1', ylabel='G3'>,\n",
       "        <AxesSubplot:xlabel='G2', ylabel='G3'>,\n",
       "        <AxesSubplot:xlabel='G3', ylabel='G3'>,\n",
       "        <AxesSubplot:xlabel='higher', ylabel='G3'>,\n",
       "        <AxesSubplot:xlabel='studytime', ylabel='G3'>],\n",
       "       [<AxesSubplot:xlabel='G1', ylabel='higher'>,\n",
       "        <AxesSubplot:xlabel='G2', ylabel='higher'>,\n",
       "        <AxesSubplot:xlabel='G3', ylabel='higher'>,\n",
       "        <AxesSubplot:xlabel='higher', ylabel='higher'>,\n",
       "        <AxesSubplot:xlabel='studytime', ylabel='higher'>],\n",
       "       [<AxesSubplot:xlabel='G1', ylabel='studytime'>,\n",
       "        <AxesSubplot:xlabel='G2', ylabel='studytime'>,\n",
       "        <AxesSubplot:xlabel='G3', ylabel='studytime'>,\n",
       "        <AxesSubplot:xlabel='higher', ylabel='studytime'>,\n",
       "        <AxesSubplot:xlabel='studytime', ylabel='studytime'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 25 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "attributes = [\"G1\", \"G2\",\"G3\", \"higher\", \"studytime\"]\n",
    "scatter_matrix(std[attributes], figsize=(12, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "2ec2507f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard ML Models for comparison\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.svm import SVR\n",
    "\n",
    "# Splitting data into training/testing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# Metrics\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, median_absolute_error\n",
    "\n",
    "# Distributions\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ab79ed0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate mae and rmse\n",
    "def evaluate_predictions(predictions, true):\n",
    "    mae = np.mean(abs(predictions - true))\n",
    "    rmse = np.sqrt(np.mean((predictions - true) ** 2))\n",
    "\n",
    "    return mae, rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "70fc31f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate several ml models by training on training set and testing on testing set\n",
    "def evaluate(X_train, X_test, y_train, y_test):\n",
    "    # Names of models\n",
    "    model_name_list = ['Linear Regression',\n",
    "                      'Random Forest', 'SVM',\n",
    "                       'Gradient Boosted']\n",
    "   # X_train = X_train.drop('G3', axis='columns')\n",
    "   # X_test = X_test.drop('G3', axis='columns')\n",
    "    \n",
    "    # Instantiate the models\n",
    "    model1 = LinearRegression()\n",
    "    model2 = RandomForestRegressor(n_estimators=100)\n",
    "    model3 = SVR(kernel='rbf', degree=3, C=1.0, gamma='auto')\n",
    "    model4 = GradientBoostingRegressor(n_estimators=50)\n",
    "    \n",
    "    # Dataframe for results\n",
    "    results = pd.DataFrame(columns=['mae', 'rmse'], index = model_name_list)\n",
    "    \n",
    "    # Train and predict with each model\n",
    "    for i, model in enumerate([model1, model2, model3, model4]):\n",
    "        model.fit(X_train, y_train)\n",
    "        predictions = model.predict(X_test)\n",
    "        \n",
    "        # Metrics\n",
    "        mae = np.mean(abs(predictions - y_test))\n",
    "        rmse = np.sqrt(np.mean((predictions - y_test) ** 2))\n",
    "        \n",
    "        # Insert results into the dataframe\n",
    "        model_name = model_name_list[i]\n",
    "        results.loc[model_name, :] = [mae, rmse]\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "1eb5a9db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mae</th>\n",
       "      <th>rmse</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Linear Regression</th>\n",
       "      <td>0.740115</td>\n",
       "      <td>1.075876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.836308</td>\n",
       "      <td>1.194892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.998736</td>\n",
       "      <td>1.567573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gradient Boosted</th>\n",
       "      <td>0.787112</td>\n",
       "      <td>1.12771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        mae      rmse\n",
       "Linear Regression  0.740115  1.075876\n",
       "Random Forest      0.836308  1.194892\n",
       "SVM                0.998736  1.567573\n",
       "Gradient Boosted   0.787112   1.12771"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = evaluate(X_train, X_test, y_train, y_test)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8dae8f16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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n9P62OX6bGb0/SRpFVXVOkp2m2OU5wGlVdVW7/7Uz8oPPnuEvWtlv2czenySN4xFoSdJ07Qpsk+SsJOcnOWKinZIck2R5kuWrV6+e5RK7qyruuuuuYZchaQ4xQM+QlStX8tCHPpSjjz6aRzziERx++OF85Stf4fGPfzy77LIL5557Lueeey6Pe9zjeOQjH8njHvc4Lr30UgDuvPNO/vZv/5Z99tmHPfbYg/e///1DfjSSNKENgUcBTwEOBI5Psuv4narqpKpaVlXLFi9ePNs1TsvKlSt52MMexl/+5V9yv/vdjwc/+MFT9m+As88+m7322ou99tqLRz7ykdx8880AvPWtb727f7/uda8b5sOSNEsM0DNoxYoVvPzlL+fCCy/kxz/+MR//+Mf5xje+wdve9jbe9KY38dCHPpRzzjmH73//+/zjP/4jr33tawH40Ic+xNZbb815553Heeedxwc+8AGuuOKKIT8aSbqXVcAXq+rWqroOOAfYc8g19e3SSy/liCOO4Pvf/z5XX331lP0b4G1vexvvec97uOCCC/j617/OZpttxplnnslll13GueeeywUXXMD555/POeecM+RHJmnQ5uUY6GHZeeed2X333QF4+MMfzhOf+ESSsPvuu7Ny5UpuuukmjjzySC677DKScMcddwBw5plncuGFF/KpT30KgJtuuonLLrvMOUMljZr/Bt6dZENgY+AxwDuHW1L/dtxxR/bdd19Wrly5zv4N8PjHP55XvvKVHH744Tzzmc9kyZIlnHnmmZx55pk88pGPBOCWW27hsssu4w//8A+H9bAkzYKBBegkJwNPBa6tqkdMsD3AvwFPBn4DPL+qvjeoembDJptscvf1DTbY4O7lDTbYgDVr1nD88cfzhCc8gc985jOsXLmS/fffH2jG373rXe/iwAMPHEbZkgRAkk8A+wPbJVkFvA7YCKCqTqyqS5J8EbgQuAv4YFVNOuXdqNt8883vvr6u/g1w3HHH8ZSnPIUzzjiDfffdl6985StUFa95zWt48YtfPLvFSxqqQQ7hOIUppkMCDgZ2aS/HAO8bYC0j4aabbmL77bcH4JRTTrl7/YEHHsj73ve+u49I/+QnP+HWW28dRomSFrCqOqyqHlBVG1XVkqr6UBucT+zZ561VtVtVPaKqThhiubPupz/9KbvvvjuvfvWrWbZsGT/+8Y858MADOfnkk7nlllsA+NnPfsa1187M5CSSRtfAjkBPYzqkQ4CPVFUB30ly3yQPqKqfr+/PHtVp5171qldx5JFH8o53vIMDDjjg7vVHH300K1euZO+996aqWLx4MZ/97GeHV6gkzaY5Mu3cCSecwNe+9jUWLVrEbrvtxsEHH8wmm2zCJZdcwmMf+1gAtthiCz72sY9x//vff8jVShqkNPl1QHfeBOjPTzKE4/PAm6vqG+3yV4FXV9W9JgRNcgzNUWqWLl36qCuvvHKt7ZdccgkPe9jDZv4BjJiF8jil+SzJ+VU1NxLjDFi2bFktX752W1+IvWwhPmZpPpisZw/zJMKJvtt0wjRfVScBJ0HTjAdZlKThmOkvQJpJo/qpliQN1Ux/CdJMmYVPtYY5jd0qYIee5SXANUOqRZIkSZqWYQbo04Ej0tgXuGl9xj8PcijKKJjvj0/SwrKQetpCeqzSQjHIaeymnA4JOINmCrsVNNPYHdXvz9p00025/vrr2XbbbWlmx5tfqorrr7+eTTfddNilSNJ6m+89u5f9W5qfBjkLx2Hr2F7AX83Ez1qyZAmrVq1i9erVM3F3I2nTTTdlyZIlwy5DktbbQujZvezf0vwzL76JcKONNvJb+yRpjrBnS5rrhjkGWpIkSZpzDNCSJElSBwZoSZIkqQMDtCRJktSBAVqSJEnqwAAtSZIkdWCAliRJkjowQEuSJEkdGKAlSZKkDgzQkiRJUgcGaEmSJKkDA7QkSZLUgQFakiRJ6sAALUmSJHVggJYkSZI6MEBLkiRJHRigJUmSpA4M0JIkSVIHBmhJkiSpAwO0JEmS1IEBWpIkSerAAC1JkiR1YICWJEmSOjBAS5IkSR0YoCVJkqQODNCSJElSBwZoSZIkqQMDtCRJktSBAVqSJEnqwAAtSQIgyclJrk3yo3Xst0+SO5M8a7Zqk6RRYoCWJI05BThoqh2SLALeAnxpNgqSpFFkgJYkAVBV5wA3rGO3lwKfBq4dfEWSNJoM0JKkaUmyPfAM4MR17HdMkuVJlq9evXp2ipOkWWSAliRN1wnAq6vqzql2qqqTqmpZVS1bvHjx7FQmSbNow2EXIEmaM5YBpyYB2A54cpI1VfXZoVYlSbPMAC1Jmpaq2nnsepJTgM8bniUtRAZoSRIAST4B7A9sl2QV8DpgI4CqmnLcsyQtJAZoSRIAVXVYh32fP8BSJGmkeRKhJEmS1IFHoKUBufGNNw67hAltc/w2wy5BkqQ5zSPQkiRJUgcGaEmSJKkDA7QkSZLUgQFakiRJ6sAALUmSJHVggJYkSZI6MEBLkiRJHRigJUmSpA4M0JIkSVIHBmhJkiSpAwO0JEmS1IEBWpIkSerAAC1JkiR1YICWJEmSOjBAS5IkSR0YoCVJkqQODNCSJElSBwZoSZIkqQMDtCRJktSBAVqSJEnqwAAtSZIkdbDhsAvQ3HHjG28cdgkT2ub4bYZdgiRJWkAGegQ6yUFJLk2yIslxE2zfOsnnkvwgyUVJjhpkPZIkSdL6GliATrIIeA9wMLAbcFiS3cbt9lfAxVW1J7A/8PYkGw+qJkmSJGl9DXIIx6OBFVV1OUCSU4FDgIt79ilgyyQBtgBuANYMsCZJkqSJnb182BVMbL9lw65A4wxyCMf2wNU9y6vadb3eDTwMuAb4IfDyqrprgDVJkiRJ62WQAToTrKtxywcCFwAPBPYC3p1kq3vdUXJMkuVJlq9evXqm65QkSZKmbZABehWwQ8/yEpojzb2OAk6rxgrgCuCh4++oqk6qqmVVtWzx4sUDK1iSJElal0EG6POAXZLs3J4YeChw+rh9rgKeCJDkd4CHAJcPsCZJkiRpvQzsJMKqWpPkJcCXgEXAyVV1UZJj2+0nAm8ETknyQ5ohH6+uqusGVZMkSZK0vgb6RSpVdQZwxrh1J/Zcvwb440HWIEmSJM0kv8pbkiRJ6sAALUmSJHVggJYkSZI6MEBLkiRJHRigJUkAJDk5ybVJfjTJ9sOTXNhevpVkz9muUZJGgQFakjTmFOCgKbZfAexXVXvQTEN60mwUJUmjZqDT2EmS5o6qOifJTlNs/1bP4ndovmFWkhYcj0BLkvrxQuALwy5CkobBI9CSpE6SPIEmQP/+JNuPAY4BWLp06SxWJkmzwyPQkqRpS7IH8EHgkKq6fqJ9quqkqlpWVcsWL148uwVK0iwwQEuSpiXJUuA04HlV9ZNh1yNJw+IQDkkSAEk+AewPbJdkFfA6YCOAqjoR+AdgW+C9SQDWVNWy4VQrScNjgJYkAVBVh61j+9HA0bNUjiSNLIdwSJIkSR0YoCVJkqQOHMIhSdJ8dfbyYVcwsf0cOq+5bUEH6BvfeOOwS5jQNsdvM+wSJEmSNAmHcEiSJEkdGKAlSZKkDgzQkiRJUgcGaEmSJKkDA7QkSZLUgQFakiRJ6sAALUmSJHVggJYkSZI6MEBLkiRJHRigJUmSpA4M0JIkSVIHBmhJkiSpAwO0JEmS1IEBWpIkSerAAC1JkiR1YICWJEmSOjBAS5IkSR0YoCVJkqQODNCSJElSBxsOuwBJkqbl7OXDrmBy+y0bdgWSZpFHoCVJkqQODNCSJElSBwZoSZIkqQMDtCRJktSBAVqSJEnqwAAtSZIkdWCAliRJkjowQEuSJEkdGKAlSZKkDgzQkiRJUgcGaEmSJKkDA7QkSZLUgQFakiRJ6sAALUmSJHVggJYkAZDk5CTXJvnRJNuT5N+TrEhyYZK9Z7tGSRoFBmhJ0phTgIOm2H4wsEt7OQZ43yzUJEkjxwAtSQKgqs4Bbphil0OAj1TjO8B9kzxgdqqTpNFhgJYkTdf2wNU9y6vadWtJckyS5UmWr169etaKk6TZYoCWJE1XJlhX91pRdVJVLauqZYsXL56FsiRpdhmgJUnTtQrYoWd5CXDNkGqRpKExQEuSput04Ih2No59gZuq6ufDLkqSZtuGwy5AkjQaknwC2B/YLskq4HXARgBVdSJwBvBkYAXwG+Co4VQqScNlgJYkAVBVh61jewF/NUvlSNLIcgiHJEmS1IEBWpIkSerAAC1JkiR1YICWJEmSOhhogE5yUJJLk6xIctwk++yf5IIkFyU5e5D1SJIkSetrYLNwJFkEvAd4Es3k++clOb2qLu7Z577Ae4GDquqqJPcfVD2SJEnSTBjkEehHAyuq6vKquh04FThk3D7PAU6rqqsAquraAdYjSZIkrbdBBujtgat7lle163rtCmyT5Kwk5yc5YqI7SnJMkuVJlq9evXpA5UqSJEnrNsgAnQnW1bjlDYFHAU8BDgSOT7LrvW5UdVJVLauqZYsXL575SiVJkqRpGuQ3Ea4CduhZXgJcM8E+11XVrcCtSc4B9gR+MsC6JEmSpL4N8gj0ecAuSXZOsjFwKHD6uH3+G/iDJBsmuQ/wGOCSAdYkSZIkrZeBHYGuqjVJXgJ8CVgEnFxVFyU5tt1+YlVdkuSLwIXAXcAHq+pHg6pJkiRJWl+DHMJBVZ0BnDFu3Ynjlt8KvHWQdUiSJEkzxW8ilCRJkjowQEuSJEkdGKAlSZKkDgzQkiRJUgcGaEmSJKkDA7QkSZLUgQFakiRJ6sAALUmSJHVggJYkSZI6MEBLkiRJHRigJWmeSHJAz/Wdx2175uxXJEnzkwFakuaPt/Vc//S4bX8/m4VI0nxmgJak+SOTXJ9oWZLUpykDdJKtpti2dObLkSSth5rk+kTLkqQ+bbiO7WcBewMk+WpVPbFn22fHtkmSRsKDkpxOc7R57Drt8s6T30yS1MW6AnTvR373m2KbJGn4Dum5/rZx28YvS5L6tK4A7ceBkjRHVNXZvctJNgIeAfysqq4dTlWSNP+sK0DfP8kraY42j12nXV480MokSZ0kORF4V1VdlGRr4NvAncD9kvxNVX1iuBVK0vywrlk4PgBsCWzRc31s+YODLU2S1NEfVNVF7fWjgJ9U1e7Ao4BXDa8sSZpfpjwCXVVvmGxbkn1mvhxJ0nq4vef6k4D/AqiqXySetiJJM2VdQzjWkmQ34FDgMOAmYNkgipIk9eVXSZ4K/Ax4PPBCgCQbApsNszBJmk/WGaCT7EgTmA8D1gA7AsuqauVgS5MkdfRi4N+B3wVeUVW/aNc/Efh/Q6tKkuaZKQN0km8BWwOnAs+qqsuSXGF4lqTRU1U/AQ6aYP2XgC/NfkWSND+t6wj0amAJ8Ds0s25chtPXSdJISvLvU22vqpfNVi2SNJ9NOQtHVR0C7A58D3hDkiuAbZI8ejaKkyR1cizw+8A1wHLg/HGXKSU5KMmlSVYkOW6C7Vsn+VySHyS5KMlRM1y/JM0J6xwDXVU3AScDJyf5HeDZwAlJdqiqHQZdoCRp2h4A/BlNn14D/F/g01V147pumGQR8B6a2TtWAeclOb2qLu7Z7a+Ai6vqaUkWA5cm+c+qun2Cu5SkeWtd80Cvpap+WVX/XlWPoznKIUkaEVV1fVWdWFVPAJ4P3Be4KMnzpnHzRwMrquryNhCfytpfDQ7NEL4t08yJtwVwA01Ql6QFZV0nEZ6+jtv/yQzWIkmaAUn2ppk56UnAF5jG8A1ge+DqnuVVwGPG7fNu4HSaISJbAs+uqrsm+PnHAMcALF26tGv5kjTy1jWE47E0DfUTwHdpvsJbkjSCkrwBeCpwCc0R5NdU1XSPEE/U38efNH4gcAFwAPBg4MtJvl5Vv17rRlUnAScBLFu2zBPPJc076wrQv0tzBOMw4Dk084h+ouerYiVJo+N44HJgz/bypvYbCANUVe0xxW1XAb3ntSyhOdLc6yjgzVVVwIr2xPKHAufOTPmSNDes66u87wS+CHwxySY0QfqsJP9YVe+ajQIlSdO283rc9jxglyQ703yT4aE0B056XUXzpSxfb08qfwhNYJekBWU630S4CfAUmvC8E823XJ022LIkSV1V1ZUTrW9n2DgUmHB7e9s1SV5C84Uri4CTq+qiJMe2208E3gickuSHNEe1X11V183ww5Ckkbeukwj/A3gEzUkob6iqH81KVZKkzpJsRTPV3PY0J/t9GXgJ8Dc0Y5f/c6rbV9UZwBnj1p3Yc/0a4I9ntGhJmoPWdQT6ecCtwK7Ay9qxdHDPeLqtBlibJKmbjwI3At8Gjgb+FtgYOKSqLhhiXZI0r6xrDHSneaIlSUP1oKraHSDJB4HrgKVVdfNwy5Kk+cWALEnzxx1jV9qTwK8wPEvSzFvnSYSSpDljzyRjczIH2KxddtidJM0gA7QkzRNVtWjYNUjSQuAQDkmSJKkDA7QkSZLUgQFakiRJ6sAALUmSJHVggJYkSZI6MEBLkiRJHRigJUmSpA4M0JIkSVIHBmhJkiSpAwO0JEmS1IEBWpIkSerAAC1JkiR1YICWJEmSOjBAS5IkSR0YoCVJkqQODNCSJElSBwZoSZIkqQMDtCRJktSBAVqSJEnqwAAtSZIkdWCAliRJkjowQEuSJEkdGKAlSZKkDgzQkiRJUgcGaEmSJKkDA7QkSZLUwUADdJKDklyaZEWS46bYb58kdyZ51iDrkSRJktbXwAJ0kkXAe4CDgd2Aw5LsNsl+bwG+NKhaJEmSpJkyyCPQjwZWVNXlVXU7cCpwyAT7vRT4NHDtAGuRJEmSZsQgA/T2wNU9y6vadXdLsj3wDODEqe4oyTFJlidZvnr16hkvVJIkSZquQQboTLCuxi2fALy6qu6c6o6q6qSqWlZVyxYvXjxT9UmSJEmdbTjA+14F7NCzvAS4Ztw+y4BTkwBsBzw5yZqq+uwA65IkSZL6Nsgj0OcBuyTZOcnGwKHA6b07VNXOVbVTVe0EfAr4S8OzJA3HdGZOSrJ/kguSXJTk7NmuUZJGwcCOQFfVmiQvoZldYxFwclVdlOTYdvuU454lSbOnZ+akJ9F8gnhektOr6uKefe4LvBc4qKquSnL/oRQrSUM2yCEcVNUZwBnj1k0YnKvq+YOsRZI0pbtnTgJIMjZz0sU9+zwHOK2qrgKoKmdPkrQg+U2EkiSYxsxJwK7ANknOSnJ+kiMmuiNnTpI03xmgJUkwvZmTNgQeBTwFOBA4Psmu97qRMydJmucGOoRDkjRnTGfmpFXAdVV1K3BrknOAPYGfzE6JkjQaPAItSYJpzJwE/DfwB0k2THIf4DHAJbNcpyQNnUegJUnTmjmpqi5J8kXgQuAu4INV9aPhVS1Jw2GAliQB05s5qareCrx1NuuSpFHjEA5JkiSpAwO0JEmS1IEBWpIkSerAAC1JkiR1YICWJEmSOjBAS5IkSR0YoCVJkqQODNCSJElSBwZoSZIkqQMDtCRJktSBAVqSJEnqwAAtSZIkdWCAliRJkjowQEuSJEkdGKAlSZKkDgzQkiRJUgcGaEmSJKkDA7QkSZLUgQFakiRJ6sAALUmSJHVggJYkSZI6MEBLkiRJHRigJUmSpA4M0JIkSVIHBmhJkiSpAwO0JEmS1IEBWpIkSerAAC1JkiR1YICWJEmSOjBAS5IkSR0YoCVJkqQODNCSJElSBwZoSZIkqQMDtCRJktSBAVqSJEnqwAAtSZIkdWCAliRJkjowQEuSAEhyUJJLk6xIctwU++2T5M4kz5rN+iRpVBigJUkkWQS8BzgY2A04LMluk+z3FuBLs1uhJI0OA7QkCeDRwIqquryqbgdOBQ6ZYL+XAp8Grp3N4iRplBigJUkA2wNX9yyvatfdLcn2wDOAE6e6oyTHJFmeZPnq1atnvFBJGjYDtCQJIBOsq3HLJwCvrqo7p7qjqjqpqpZV1bLFixfPVH2SNDI2HHYBkqSRsArYoWd5CXDNuH2WAacmAdgOeHKSNVX12VmpUJJGhAFakgRwHrBLkp2BnwGHAs/p3aGqdh67nuQU4POGZ0kLkQFakkRVrUnyEprZNRYBJ1fVRUmObbdPOe5ZkhYSA7QkCYCqOgM4Y9y6CYNzVT1/NmqSpFHkSYSSJElSBwZoSZIkqQMDtCRJktSBAVqSJEnqwAAtSZIkdWCAliRJkjowQEuSJEkdGKAlSZKkDgzQkiRJUgcGaEmSJKkDA7QkSZLUwUADdJKDklyaZEWS4ybYfniSC9vLt5LsOch6JEmSpPU1sACdZBHwHuBgYDfgsCS7jdvtCmC/qtoDeCNw0qDqkSRJkmbCII9APxpYUVWXV9XtwKnAIb07VNW3qurGdvE7wJIB1iNJkiStt0EG6O2Bq3uWV7XrJvNC4AsTbUhyTJLlSZavXr16BkuUJEmSuhlkgM4E62rCHZMn0AToV0+0vapOqqplVbVs8eLFM1iiJEmS1M2GA7zvVcAOPctLgGvG75RkD+CDwMFVdf0A65EkSZLW2yCPQJ8H7JJk5yQbA4cCp/fukGQpcBrwvKr6yQBrkSRJkmbEwI5AV9WaJC8BvgQsAk6uqouSHNtuPxH4B2Bb4L1JANZU1bJB1SRJkiStr0EO4aCqzgDOGLfuxJ7rRwNHD7IGSZIkaSb5TYSSJElSBwZoSZIkqQMDtCRJktSBAVqSJEnqwAAtSZIkdWCAliRJkjowQEuSJEkdGKAlSZKkDgzQkiRJUgcGaEmSJKkDA7QkSZLUgQFakiRJ6sAALUmSJHVggJYkSZI6MEBLkiRJHRigJUmSpA4M0JIkSVIHBmhJEgBJDkpyaZIVSY6bYPvhSS5sL99Ksucw6pSkYTNAS5JIsgh4D3AwsBtwWJLdxu12BbBfVe0BvBE4aXarlKTRYICWJAE8GlhRVZdX1e3AqcAhvTtU1beq6sZ28TvAklmuUZJGggFakgSwPXB1z/Kqdt1kXgh8YaAVSdKI2nDYBUiSRkImWFcT7pg8gSZA//4k248BjgFYunTpTNUnSSPDI9CSJGiOOO/Qs7wEuGb8Tkn2AD4IHFJV1090R1V1UlUtq6plixcvHkixkjRMBmhJEsB5wC5Jdk6yMXAocHrvDkmWAqcBz6uqnwyhRkkaCQ7hkCRRVWuSvAT4ErAIOLmqLkpybLv9ROAfgG2B9yYBWFNVy4ZVsyQNiwFakgRAVZ0BnDFu3Yk9148Gjp7tuiRp1DiEQ5IkSerAAC1JkiR1YICWJEmSOjBAS5IkSR0YoCVJkqQODNCSJElSBwZoSZIkqQMDtCRJktSBAVqSJEnqwAAtSZIkdWCAliRJkjowQEuSJEkdGKAlSZKkDgzQkiRJUgcGaEmSJKkDA7QkSZLUgQFakiRJ6sAALUmSJHVggJYkSZI6MEBLkiRJHRigJUmSpA4M0JIkSVIHBmhJkiSpAwO0JEmS1IEBWpIkSerAAC1JkiR1YICWJEmSOjBAS5IkSR0YoCVJkqQODNCSJElSBwZoSZIkqQMDtCRJktSBAVqSJEnqwAAtSZIkdWCAliRJkjowQEuSJEkdGKAlSZKkDgzQkiRJUgcDDdBJDkpyaZIVSY6bYHuS/Hu7/cIkew+yHknS5OzZkjQ9AwvQSRYB7wEOBnYDDkuy27jdDgZ2aS/HAO8bVD2SpMnZsyVp+gZ5BPrRwIqquryqbgdOBQ4Zt88hwEeq8R3gvkkeMMCaJEkTs2dL0jRtOMD73h64umd5FfCYaeyzPfDz3p2SHENztAPgliSXzmypM2I74LoZuad/mJF7GXU+X934fHU3qs/ZjjN6bzPHnq118Tnrxuerm1F9vibs2YMM0JlgXfWxD1V1EnDSTBQ1KEmWV9WyYdcxV/h8dePz1Z3PWWf2bE3J56wbn69u5trzNcghHKuAHXqWlwDX9LGPJGnw7NmSNE2DDNDnAbsk2TnJxsChwOnj9jkdOKI9s3tf4Kaq+vn4O5IkDZw9W5KmaWBDOKpqTZKXAF8CFgEnV9VFSY5tt58InAE8GVgB/AY4alD1zIKR/rhyBPl8dePz1Z3PWQf2bE2Dz1k3Pl/dzKnnK1X3Gr4mSZIkaRJ+E6EkSZLUgQFakiRJ6sAALY2gJC+fzjpJ0miwby8sjoHWQCV55lTbq+q02aplLknyvarae9y671fVI4dVk6SFwb7dH/v2wjLIL1KZ19oG8xbg/jRfLhCgqmqroRY2ep7W/nt/4HHA/7TLTwDOAmzEPZIcBjwH2DlJ7xRiWwHXD6eq0Zbkwsk20fxN7jGb9Wg02bM7sW93YN/uZr70bAN0//4VeFpVXTLsQkZZVR0FkOTzwG5jc8YmeQDwnmHWNqK+RfO1yNsBb+9ZfzMwWdNZ6O6i+Ta8jwOfA/53uOVoRNmzp8m+3Zl9u5t50bMdwtGnJN+sqscPu465IsmPquoRPcsbABf2rtM9kmwO/G9V3ZVkV+ChwBeq6o4hlzaSkjwUOIzmyNnFNI35zKpaM9TCNDLs2d3Zt7uxb0/ffOjZBug+Jfk34HeBzwK/HVvv2LCJJXk3sAvwCZp3nocCK6rqpUMtbEQlOR/4A2Ab4DvAcuA3VXX4UAubA5I8m+Yo2Vuq6q3DrkejwZ7dnX27G/t2f+ZqzzZA9ynJhydYXVX1glkvZo5I8gzgD9vFc6rqM8OsZ5SNnYyS5KXAZlX1r56MMrkk29O8uD8DuBH4JPCZqrplqIVpZNiz+2Pfnj779vTNh57tGOg+jY0RUyffA26uqq8kuU+SLavq5mEXNaKS5LHA4cAL23X+vU4gydnAljQN+PnADe2mjZPcr6pumOy2Wjjs2X2zb0+ffXsa5kvPdh7oPiVZkuQzSa5N8sskn06yZNh1jaokLwI+Bby/XbU9zUepmtgrgNfQvCO/KMmDgK8Nt6SRtSPNR6YvBs6k+dh0OXB++69kz+6DfbuzV2Dfno550bMdwtGnJF+mGfT+0XbVc4HDq+pJw6tqdCW5AHg08N2xj7OS/LCqdh9qYSMuyeZVdeuw65DmOnt2d/bt/ti3FwaPQPdvcVV9uKrWtJdTgMXDLmqE/baqbh9bSLIhzUkpmkCSxya5GLikXd4zyXuHXNZISnJxkte2R3ukydizu7Nvd2Dfnp750rMN0P27LslzkyxqL8/FCdOncnaS1wKbJXkS8F808z9qYicAB9L+n6qqH3DPiTxa22E04+m+nOS7SV6R5IHDLkojx57dnX27mxOwb0/HvOjZBuj+vQD4c+AXNBOoP6tdp4kdB6wGfkgz7umMqvq74ZY02qrq6nGr7hxKISOuqn5QVa+pqgcDL6cZX/edJP/TjuGUwJ7dD/t2R/btdZsvPdsx0JoVSV5eVf+2rnVqJPkU8A7g3cC+wMuAZVV16FALmyOS7A+8k+Zb1DYZbjXS3GTf7sa+3b+52LMN0B0leVU7t+O7mGAsWFW9bAhljbyx+THHrXN+zEkk2Q74N+CPgNCcqfzyqvIj50kk2Yfmo8E/BVYCpwL/VVXXDbMuDZc9u3/27W7s293M9Z7t/ITdXdL+O2emWhmmJIcBzwF2TnJ6z6atcPzhhJIsAk7w26umJ8mbaD6a/xVNA358Va0aalEaJfbsjuzb3dm3p2++9GwDdEdV9bn23/8YW5dkA2CLqvr10AobXd+iGW+4HfD2nvU3AxcOpaIRV1V3JlmcZOPeM+A1qb2AF1TVOQBJjkjyp8CVwOvnyqT8Ggx7dl/s2x3ZtzvZi3nQsx3C0ackHweOpTlB4Hxga+Adc+l73GdTks2B/62qu5LsCjwU+EJV3THk0kZSkvcDewOnA3fPJ1pV7xhaUSMqyfeAP6qqG5L8Ic0RjZfSNOmHVdWzhlmfRoM9uzv7djf27emZLz3bWTj6t1t79OLpwBnAUuB5Q61otJ0DbJpke+CrwFHAKUOtaLRdA3ye5m90y56L7m2DniMWzwZOqqpPV9XxwO8NsS6NFnt2d/btbuzb0zMverZDOPq3UZKNaJrxu6vqjiQezp9cquo3SV4IvKs9qef7wy5qVFXVGwCSbNks1i1DLmmUbZhkw6paAzwROKZ325Bq0uixZ3dn3+7Avj1t86JnewS6f++nOWt0c+CcJDsCjqebXJI8Fjgc+H/tujnzhzLbkjyifaH6EXBRkvOTPHzYdY2oT9B84cN/A/8LfB0gye8BNw2zMI0Ue3Z39u0O7NvTNi96tmOgZ1DPOyqNk2Q/4K+Bb1bVW9qv8HyFU0hNLMm3gL+rqq+1y/sDb6qqxw2zrlGVZF/gAcCZVXVru25XmhPFvjfU4jSy7NlTs293Y9+evvnQsw3QfUrycuDDNGclfxB4JHBcVZ051MJGnB9tTU+SH1TVnutaJ2l67Nn9s29Pj317YXEIR/9e0J6Q8sfAYpqTK9483JJGV5Ldez7autiPttbp8iTHJ9mpvfw9cMWwi5LmMHt2R/btzuzbC4gBun9p/30y8OGq+kHPOt3b+4FXVtWOVbWU5mPBDwy5plH2ApoX+dPay3Y0L/iS+mPP7s6+3Y19ewFxCEefknwY2B7YGdgTWAScVVWPGmphI8qPtqYnyTOr6rT2+jZVdeOwa5LmA3t2d/bt6bFvL0wG6D6132S1F3B5Vf0qybbA9lXltzRNIMlngO8BH21XPRdYVlVPH1pRIyjJ96pq7/HXJa0fe3Z39u3psW8vTA7h6F8BuwFjZyNvDmw6vHJGXu9HW5/hnjGIWlsmuS5p/dizu7NvT499ewHyCHSfkrwPuAs4oKoelmQbmulY9hlyaSMtyVbAXZ7NPbEkPwYOo3lz+zHgOfQ05LkyvY80auzZ/bNvT82+vTAZoPs09jFNku9X1SPbdY4Nm0SS3YGPAPdrV10HHFlVPxpeVaMnydem2FxVdcCsFSPNI/bs7uzb02PfXpj8RqH+3ZFkEc3HgiRZTHN0QxMbO5u7d4L5kwAnmO9RVU8Ydg3SPGXP7s6+PQ327YXJMdD9+3eaMWH3T/LPwDeANw23pJG2+VgTBqiqs2jGIErSbLBnd2fflibhEI4+tGdz7wvcADyRZqzTV6vqkqEWNsI8m1vSsNiz+2PfliZngO5Tkm9X1WOHXcdc0Z6w8wbg92levM4BXu98mZJmgz27O/u2NDkDdJ+SvAG4EDitfBI1AEn2AHai51yFscn6JXVjz9ZssG8vHAboPiW5mWYs2BrgNpp351VVWw21sBGTZDvgr4AbgZOBtwJ/APwU+OuqWjHE8kZWkpOBPYCLuOdEp6qqFwyvKmnusmdPn327P/bthcUArYFKciawHNiSZuzhKcDpNM348Kraf2jFjbAkF1fVbsOuQ9LCY9/uj317YTFA9ynJRF/VeRNwZVWtme16RtXYPKtJQvPcLO3ZdkFV7TW86kZXkg8Bb6+qi4ddizQf2LOnz77dH/v2wuI80P17L7A38MN2eXfgB8C2SY6tqjOHVtlouROaz7CSXDdum3OwTu4/gG8n+QXwW+75uHmP4ZYlzVn27Omzb/fHvr2AGKD7txJ4YVVdBJBkN+BvgTcCpwE248aDkpxO00jGrtMu7zy8skbeycDzaF7sfcGS1t9K7NnTZd/uj317AXEIR58m+hhrbJ0fcd0jyX5Tba+qs2erlrkkyf/49a/SzLFnT599uz/27YXFI9D9uzTJ+4BT2+VnAz9Jsglwx/DKGi022r79OMnHgc/RfBQIOB2StB7s2dNk3+6bfXsB8Qh0n5JsBvwl90ww/w2aMXa3AfepqluGWJ7muCQfnmC10yFJfbJna9Ds2wuLAXo9tA15aVVdOuxaJElTs2dLmikbDLuAuSrJnwAXAF9sl/fqOdFC4yT5s+msUyPJkiSfSXJtkl8m+XSSJcOuS5qr7Nnd2be7sW8vLAbo/r0OeDTwK4CquoDm6zs1sddMc50aH6b54oIHAtvTjKmb6ONBSdNjz+7Ovt2NfXsB8STC/q2pqpuaeeY1mSQHA08Gtk/y7z2btqL5Sl1NbHFV9TbeU5K8YljFSPOAPXua7Nt9s28vIB6B7t+PkjwHWJRklyTvAr417KJG0DU0Xwl7G3B+z+V04MAh1jXqrkvy3CSL2stzgeuHXZQ0h9mzp8++3R/79gLiSYR9SnIf4O+AP6Y5o/uLwBur6rdT3nCBSrJRVTlV1DQlWQq8G3gsUDQv9C+vqiuHWpg0R9mzu7Nvd2PfXlgM0DMkyUOBv66qFw27llGU5PHA64EdaYYOjX3F6YOGWZekhcmevW72bWlyjoHuKMkewNtoThL4DM27zfcCjwHePsTSRt2HgP9D8zHgnUOuZWS1HytP+q62ql42i+VIc549e73Yt6fBvr0wOQa6uw8AHwf+FLgO+B5wOfB7VfXOYRY24m6qqi9U1bVVdf3YZdhFjaDlNC9WmwJ7A5e1l73wBUzqhz27f/bt6bFvL0AO4egoyQVVtVfP8tXATlXlH8kUkrwZWAScxtpfcfq9oRU1wpJ8DfjjsfGHSTYCzqyqJwy3MmlusWf3z77djX17YXEIR3ebJnkkzVgwgFuAPdLOjWRjmdRj2n+X9awr4IAh1DIXPBDYErihXd6iXSepG3t2/+zb3di3FxCPQHfUvsOcTFWVjUXrLclRNCfvjP1/2w94fVX9x9CKkuYge7Zmi317YTFAa1Yk+R3gTcADq+rgJLsBj62qDw25tJGV5He55wjQd6vqF8OsR9LCYt/uzr69cHgSoWbLKcCXuOfjrJ8ArxhWMXPEImA1cCOwa5I/HHI9khaWU7Bvd2XfXiAcA63Zsl1VfTLJawCqak0ST+KZRJK3AM8GLgLualcXcM7QipK00Ni3O7BvLywG6D60J58sqaqrh13LHHJrkm1p58pMsi9w03BLGmlPBx7it6RJ68+e3Tf7djdPx769YBig+1BVleSzwKOGXcsc8krgdODBSb4JLAaeNdySRtrlwEb0TB0lqT/27L7Zt7uxby8gBuj+fSfJPlV13rALmQuq6ntJ9gMeQjOd1KVjc2VqQr8BLkjyVdaef9VvtJL6Y8/uyL7dmX17AXEWjj4luRjYFbgSuJWmuVRV7THUwkZMkgOq6n+SPHOi7VV12mzXNBckOXKi9U6HJPXHnj199u3+2LcXFo9A9+/gYRcwR+wH/A/wtAm2Fc03XGkcG6404+zZ02ff7oN9e2HxCPR6SnJ/YNOx5aq6aojlaJ5IsgvwL8BurP3/60FDK0qaB+zZGhT79sLiEeg+JfkT4O0082NeC+wIXAI8fJh1jZokr5xqe1W9Y7ZqmWM+DLwOeCfwBOAo7vkqYkkd2bOnz77dN/v2AuIXqfTvjcC+wE+qamfgicA3h1vSSNqyvSwD/gLYvr0cS/MuXRPbrKq+SvMp0ZVV9XrArxyW+mfPnj77dn/s2wuIR6D7d0dVXZ9kgyQbVNXX2knU1aOq3gCQ5Exg76q6uV1+PfBfQyxt1N2WZAPgsiQvAX4G3H/INUlzmT17muzbfbNvLyAG6P79KskWwNeB/0xyLbBmyDWNsqXA7T3LtwM7DaeUOeEVwH2Al9EcOTsAOGKYBUlznD27O/t2N6/Avr1geBJhn5JsDvwvzTCYw4Gtgf+squuHWtiISvJ3wJ8Dn6E5i/sZwCer6k1DLWyOSLIh8Oyq+s9h1yLNRfbs7uzb68e+Pb8ZoNdDkh2BXarqK0nuAywa+6hL95bkUcDvt4vnVNX3h1nPKEqyFfBXNOMNTwe+3C7/DfCDqjpkiOVJc5o9uzv79rrZtxcmA3SfkrwIOAa4X1U9uJ2+5sSqeuKQSxtpTiE1tST/DdwIfJvmJKdtgI2Bl1fVBUMsTZrT7Nn9s29Pzb69MBmg+5TkAuDRwHer6pHtuh9W1e5DLWxETTCF1FLgx1XlFFI9ev8PJVkEXAcs9SiZtH7s2d3Zt6fHvr0wOY1d/35bVXefXNGOdfLdyOTGTyH1RziF1ETuGLtSVXcCV9iEpRlhz+7Ovj099u0FyADdv7OTvBbYLMmTaKb2+dyQaxpld7Qn69w9hRSw15BrGkV7Jvl1e7kZ2GPsepJfD7s4aQ6zZ3dn354e+/YC5DR2/TsOeCHwQ+DFwBnAB4da0Wgbm0LqHJxCalJVtWjYNUjzlD27O/v2NNi3FybHQGtWOIWUJM0t9m1pcgboPiV5PPB6YEeaI/kBqqoeNMy6RlF7UsWXquqPhl2LpIXJnt2NfVuamkM4+vch4P8A5wN3DrmWkVZVdyb5TZKtq+qmYdcjaUGyZ3dg35amZoDu301V9YVhFzGH3Ab8MMmXgVvHVlbVy4ZXkqQFxJ7dnX1bmoRDOPqU5M3AIuA04Ldj66vqe0MraoQlOXKi9VX1H7Ndi6SFx57dnX1bmpwBuk9JvjbB6qqqA2a9GEnSlOzZkmaSAVoDleQQYElVvadd/i6wuN38qqr61NCKkyTdi31bWjfHQHeU5LlV9bEkr5xoe1W9Y7ZrGnGvAg7tWd4E2AfYHPgwYCOWNDD27L7Yt6V1MEB3t3n775YTbPNw/r1tXFVX9yx/o51D9Pp2jlFJGiR7dnf2bWkdHMIxg5K8oqpOGHYdoyTJiqr6vUm2/bSqHjzbNUkS2LMnY9+W1m2DYRcwz0z4EeEC990kLxq/MsmLgXOHUI8kjbFnT8y+La2DR6BnUJKrq2qHYdcxSpLcH/gszbRRY9NFPYpmTN3Tq+qXQypN0gJnz56YfVtaNwP0DEpyVVUtHXYdoyjJAcDD28WLqup/hlmPJNmzp2bfliZngO4oyc1MfOJJgM2qyhMzJWlE2LMlDYIBWpIkSerAkwglSZKkDgzQkiRJUgcGaEmSJKkDA7QkSZLUgQFakiRJ6uD/Ay+qC9C9sZrmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "# Root mean squared error\n",
    "ax =  plt.subplot(1, 2, 1)\n",
    "results.sort_values('mae', ascending = True).plot.bar(y = 'mae', color = 'violet', ax = ax)\n",
    "plt.title('Model Mean Absolute Error') \n",
    "plt.ylabel('MAE')\n",
    "\n",
    "# Median absolute percentage error\n",
    "ax = plt.subplot(1, 2, 2)\n",
    "results.sort_values('rmse', ascending = True).plot.bar(y = 'rmse', color = 'pink', ax = ax)\n",
    "plt.title('Model Root Mean Squared Error') \n",
    "plt.ylabel('RMSE')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4bdc9247",
   "metadata": {},
   "outputs": [],
   "source": [
    "XX=std['G2']\n",
    "YY=std['G3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "171d25ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'G3')"
      ]
     },
     "execution_count": 53,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(XX,YY)\n",
    "plt.xlabel(\"G2\")\n",
    "plt.ylabel(\"G3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b9561481",
   "metadata": {},
   "outputs": [],
   "source": [
    "X1=std['G1']\n",
    "Y1=std['G3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "161aed28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'G3')"
      ]
     },
     "execution_count": 55,
     "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(X1,Y1)\n",
    "plt.xlabel(\"G1\")\n",
    "plt.ylabel(\"G3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22f88640",
   "metadata": {},
   "source": [
    "# Linear Regrision\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4c3cf384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>romantic</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</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>4</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>649 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  ...  \\\n",
       "0         0    1   18        0        1        1     4     4     3     0  ...   \n",
       "1         0    1   17        0        1        0     1     1     3     4  ...   \n",
       "2         0    1   15        0        0        0     1     1     3     4  ...   \n",
       "3         0    1   15        0        1        0     4     2     1     2  ...   \n",
       "4         0    1   16        0        1        0     3     3     4     4  ...   \n",
       "..      ...  ...  ...      ...      ...      ...   ...   ...   ...   ...  ...   \n",
       "644       1    1   19        1        1        0     2     3     2     4  ...   \n",
       "645       1    1   18        0        0        0     3     1     0     2  ...   \n",
       "646       1    1   18        0        1        0     1     1     4     4  ...   \n",
       "647       1    0   17        0        0        0     3     1     2     2  ...   \n",
       "648       1    0   18        1        0        0     3     2     2     4  ...   \n",
       "\n",
       "     romantic  famrel  freetime  goout  Dalc  Walc  health  absences  G1  G2  \n",
       "0           0       4         3      4     1     1       3         4   0  11  \n",
       "1           0       5         3      3     1     1       3         2   9  11  \n",
       "2           0       4         3      2     2     3       3         6  12  13  \n",
       "3           1       3         2      2     1     1       5         0  14  14  \n",
       "4           0       4         3      2     1     2       5         0  11  13  \n",
       "..        ...     ...       ...    ...   ...   ...     ...       ...  ..  ..  \n",
       "644         0       5         4      2     1     2       5         4  10  11  \n",
       "645         0       4         3      4     1     1       1         4  15  15  \n",
       "646         0       1         1      1     1     1       5         6  11  12  \n",
       "647         0       2         4      5     3     4       2         6  10  10  \n",
       "648         0       4         4      1     3     4       5         4  10  11  \n",
       "\n",
       "[649 rows x 32 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=std.drop(columns='G3')\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5f9a8afd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      11\n",
       "1      11\n",
       "2      12\n",
       "3      14\n",
       "4      13\n",
       "       ..\n",
       "644    10\n",
       "645    16\n",
       "646     9\n",
       "647    10\n",
       "648    11\n",
       "Name: G3, Length: 649, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=std['G3']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a4f023b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=23,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f2cc9126",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_val, Y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "59c3e86d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>romantic</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>1</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
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       "      <td>5</td>\n",
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       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
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       "    <tr>\n",
       "      <th>473</th>\n",
       "      <td>1</td>\n",
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       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>212</th>\n",
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       "      <td>17</td>\n",
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       "      <td>4</td>\n",
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       "      <td>2</td>\n",
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       "      <td>14</td>\n",
       "      <td>15</td>\n",
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       "      <td>4</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
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       "<p>104 rows × 32 columns</p>\n",
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      "text/plain": [
       "     school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  ...  \\\n",
       "407       0    1   21        0        0        0     4     4     4     4  ...   \n",
       "509       1    1   16        1        0        0     1     1     3     4  ...   \n",
       "422       0    1   18        0        1        0     3     2     2     4  ...   \n",
       "193       0    0   17        0        1        0     4     3     4     4  ...   \n",
       "478       1    1   16        1        1        0     1     1     3     4  ...   \n",
       "..      ...  ...  ...      ...      ...      ...   ...   ...   ...   ...  ...   \n",
       "260       0    1   16        0        1        0     1     2     4     4  ...   \n",
       "17        0    1   16        0        1        0     3     3     4     4  ...   \n",
       "473       1    0   16        0        0        0     2     1     3     2  ...   \n",
       "212       0    1   17        0        1        0     4     4     2     0  ...   \n",
       "410       0    1   17        0        1        0     2     2     2     2  ...   \n",
       "\n",
       "     romantic  famrel  freetime  goout  Dalc  Walc  health  absences  G1  G2  \n",
       "407         0       3         3      2     1     1       5         0   9  12  \n",
       "509         0       5         3      2     1     1       1         0  16  17  \n",
       "422         1       3         1      2     1     2       1         4  10  13  \n",
       "193         1       5         2      3     1     1       2         4  11  11  \n",
       "478         0       3         4      3     1     1       1         0   7   7  \n",
       "..        ...     ...       ...    ...   ...   ...     ...       ...  ..  ..  \n",
       "260         0       5         3      5     1     2       5         4  12  11  \n",
       "17          0       5         3      2     1     1       4         2  13  14  \n",
       "473         0       2         4      3     2     3       4         4  10   8  \n",
       "212         0       4         2      4     2     3       2        30  14  15  \n",
       "410         0       4         3      4     1     3       4         0  13  17  \n",
       "\n",
       "[104 rows x 32 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "565b3e57",
   "metadata": {},
   "outputs": [
    {
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       "<p>415 rows × 32 columns</p>\n",
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      ],
      "text/plain": [
       "     school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  ...  \\\n",
       "360       0    1   18        0        1        0     3     3     3     4  ...   \n",
       "40        0    1   16        0        0        0     2     2     4     4  ...   \n",
       "352       0    0   19        1        1        0     3     3     4     2  ...   \n",
       "1         0    1   17        0        1        0     1     1     3     4  ...   \n",
       "310       0    1   18        1        1        0     2     1     4     4  ...   \n",
       "..      ...  ...  ...      ...      ...      ...   ...   ...   ...   ...  ...   \n",
       "404       0    1   17        0        1        0     2     1     2     4  ...   \n",
       "495       1    1   16        0        1        0     3     3     4     4  ...   \n",
       "137       0    1   16        0        1        1     2     2     4     4  ...   \n",
       "306       0    0   18        1        0        1     3     4     4     4  ...   \n",
       "453       1    1   16        0        1        0     1     2     4     2  ...   \n",
       "\n",
       "     romantic  famrel  freetime  goout  Dalc  Walc  health  absences  G1  G2  \n",
       "360         0       4         1      4     1     1       3         8  11  12  \n",
       "40          1       3         3      3     1     2       3        16  11  11  \n",
       "352         1       4         5      3     1     2       5         0  10  10  \n",
       "1           0       5         3      3     1     1       3         2   9  11  \n",
       "310         1       4         3      5     1     2       3        12   8   9  \n",
       "..        ...     ...       ...    ...   ...   ...     ...       ...  ..  ..  \n",
       "404         0       4         3      5     2     4       4         4  12  16  \n",
       "495         1       4         5      4     1     1       4         0  14  13  \n",
       "137         0       5         3      4     1     1       5        12  13  11  \n",
       "306         0       4         2      5     3     4       1         6  15  16  \n",
       "453         0       1         3      2     1     2       4         0  10   8  \n",
       "\n",
       "[415 rows x 32 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9d9de29e",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>130 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     school  sex  age  address  famsize  Pstatus  Medu  Fedu  Mjob  Fjob  ...  \\\n",
       "482       1    1   15        1        0        0     1     1     3     4  ...   \n",
       "167       0    1   17        0        1        0     4     4     4     0  ...   \n",
       "339       0    0   18        1        0        0     3     2     2     4  ...   \n",
       "350       0    0   19        1        0        0     2     1     3     2  ...   \n",
       "390       0    1   18        1        1        0     2     2     3     4  ...   \n",
       "..      ...  ...  ...      ...      ...      ...   ...   ...   ...   ...  ...   \n",
       "146       0    0   16        0        0        0     1     1     2     4  ...   \n",
       "263       0    0   18        0        1        0     2     2     4     2  ...   \n",
       "408       0    0   18        0        0        0     2     2     2     2  ...   \n",
       "387       0    0   18        0        1        0     2     2     4     4  ...   \n",
       "135       0    1   16        0        1        1     3     4     2     4  ...   \n",
       "\n",
       "     romantic  famrel  freetime  goout  Dalc  Walc  health  absences  G1  G2  \n",
       "482         1       5         2      1     1     3       4         0   9  10  \n",
       "167         1       4         2      1     1     1       4         0  13  13  \n",
       "339         0       5         4      2     1     1       4         0  14  15  \n",
       "350         1       4         3      1     1     1       5         0   9  10  \n",
       "390         0       4         4      4     1     1       4         6  14  13  \n",
       "..        ...     ...       ...    ...   ...   ...     ...       ...  ..  ..  \n",
       "146         1       4         4      4     1     3       5         0  10  10  \n",
       "263         0       5         5      4     3     5       2        16   8   7  \n",
       "408         0       4         4      4     1     3       3        11   9  11  \n",
       "387         0       5         4      2     1     2       5         6  15  14  \n",
       "135         0       3         2      1     1     4       5        12  15  13  \n",
       "\n",
       "[130 rows x 32 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "2cf84d59",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "538    11\n",
       "411    15\n",
       "546    16\n",
       "565     9\n",
       "93     14\n",
       "       ..\n",
       "237    10\n",
       "31     15\n",
       "488     9\n",
       "40     10\n",
       "595    17\n",
       "Name: G3, Length: 519, dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "1cbba782",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "6889ef8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "r=linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "62afc256",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.fit (X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "5e18fe88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8948631316707356"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c=r.intercept_\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "cdcf5df5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.18691702,  0.11653464,  0.00353512, -0.12251972, -0.00209472,\n",
       "        0.13877438, -0.10155387,  0.02057173, -0.10087105,  0.03479689,\n",
       "       -0.03910199,  0.0892313 ,  0.14916011,  0.02000605, -0.23671009,\n",
       "       -0.22053922,  0.06212858, -0.24988822,  0.08868807,  0.02912112,\n",
       "        0.24683667,  0.04479718, -0.02311584,  0.00236831, -0.07872492,\n",
       "       -0.01674471, -0.09425973, -0.0278346 , -0.06227145,  0.02084654,\n",
       "        0.09950865,  0.89274141])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m=r.coef_\n",
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f35d5dd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10.87304799, 15.25006987, 15.09751371,  9.89055657, 14.95504292,\n",
       "        9.16389155, 13.72217068, 13.64296994, 13.72163785, 10.10969525,\n",
       "       10.14112346, 13.66084892, 12.16301122, 14.52076185, 12.47578813,\n",
       "       12.84270663, 10.38895273,  8.15073593, 15.52714392, 12.49186692,\n",
       "       11.61597799, 12.21104565, 13.32281505,  9.85619526,  8.90770919,\n",
       "       10.95227662, 11.56309437, 13.1946527 ,  8.15586362, 12.59853743,\n",
       "       10.21436896, 14.08559232, 13.23697167,  9.08363506,  7.31371334,\n",
       "       10.99698978,  9.24378053,  9.58486841, 16.41427544, 13.71088276,\n",
       "       14.07449384, 18.49659799,  8.45997998, 11.52556887, 11.47837126,\n",
       "       12.00575729, 11.86231953, 11.7002783 ,  9.82995518, 14.13438318,\n",
       "        8.65751252, 11.06276894, 11.44454148, 16.41387672, 12.41162973,\n",
       "        9.85531012, 12.73255449, 13.84858719, 10.63114995,  6.5102311 ,\n",
       "        9.49175902, 15.33073139,  9.35064186, 14.65478937, 10.22864585,\n",
       "       10.08386533, 13.54215887, 12.14808728, 14.84473913, 11.23434102,\n",
       "       10.93593872, 16.14793526, 16.54739544, 17.30923359, 15.42535074,\n",
       "        1.19967915,  1.03789439, 11.99740736, 12.46495913, 11.25499046,\n",
       "        9.48900595,  8.42030816, 15.38683127, 11.47859689, 11.55731061,\n",
       "       11.84037489, 11.27842913,  9.20324728, 12.30556611,  9.54599583,\n",
       "        6.67215283, 16.63970679, 10.3594214 , 17.67727538,  8.80651689,\n",
       "        6.82745422, 13.31341768,  9.73278855,  5.60004509, 12.57064426,\n",
       "       14.29706142, 12.33733914,  8.18770496, 15.2531398 , 11.09236777,\n",
       "       12.53607354, 12.81770878, 11.13285166,  0.47682352, 13.76673392,\n",
       "       17.21938888, 10.61091267, 10.48139949, 10.94310777, 12.23286958,\n",
       "       13.28258333, 13.88410142, 12.7372786 , 14.89229017, 10.92599547,\n",
       "        7.00279079, 15.8418366 , 15.48821601,  8.15034739, 12.42554159,\n",
       "       12.92455096, 13.61286419, 12.881226  , 13.21765152, 16.63176333,\n",
       "       12.57153213, 11.28721041, 12.41696129,  7.13788563,  7.06268623,\n",
       "       13.24262439, 14.43586156,  9.07550866, 13.1140599 , 13.71374736,\n",
       "       12.08803965, 14.75469792,  7.16445461,  7.15374147, 11.57210413,\n",
       "       10.27215647, 13.22493526, 12.4339866 , 14.33532213,  8.59883995,\n",
       "       13.77602955,  8.64912285,  7.73577105,  9.84509693,  8.22400065,\n",
       "       14.40514617, 17.88651012, 10.39015491, 10.57458945, 10.79070151,\n",
       "       12.49181148,  7.94926764,  6.85932671, 18.13513138, 12.56775243,\n",
       "       15.82207985, 11.63173004, 11.20926157, 12.40025841, 10.99278698,\n",
       "       13.19789175, 16.80791796, 10.50225012,  9.26893294, 13.97255577,\n",
       "       14.46268939, 12.30760388, 10.14641799, 17.98116846, 12.59475165,\n",
       "       10.37657893, 10.31304383,  9.56385676, 14.58117008, 15.33543077,\n",
       "        8.16836789, 10.34214799, 10.86129072,  6.1596134 , 16.74330798,\n",
       "        7.83312312, 13.76805284,  7.418921  , 11.78288702,  8.23259129,\n",
       "       16.82340727, 11.41049738, 14.6855702 , 14.89837124,  9.19964145,\n",
       "        7.66616111, 10.26838739, 11.98644172, 15.66242457, 13.4870026 ,\n",
       "       10.65685554, 13.4229389 , 13.36894046,  9.8682397 , 15.77423208,\n",
       "       12.33932582, 16.20237941, 11.18120943, 11.18306739, 15.14237183,\n",
       "       13.23351035, 11.12459253, 14.13387404, 11.45566091, 12.70480762,\n",
       "        9.34947634, 10.24063889, 15.73471254, 10.01859394,  8.83812494,\n",
       "       15.35618837, 17.65491909, 17.14661615,  8.01030579, 14.57353854,\n",
       "       16.13337336,  8.90282564, 11.06401559, 12.31282967, 10.58956512,\n",
       "       14.9651521 , 11.63505586, 12.23626157, 11.45758943, 12.49845035,\n",
       "       13.20079924, 11.83616018, 11.79694284,  9.1928716 , 12.36950326,\n",
       "       10.20485382, 14.51346376,  5.73045267, 14.61139567, 12.69498712,\n",
       "       11.1029063 , 13.75659541, 11.07926368, 11.04535882, 10.25583745,\n",
       "       13.84656158, 12.69097823,  9.21751911,  8.35939353, 13.03939592,\n",
       "       12.12484965, 10.74055883, 10.19571438,  9.98695564, 16.4323081 ,\n",
       "        9.16034988,  8.31406565, 11.90088297,  5.45390927,  9.54022394,\n",
       "       11.62595523,  9.6707996 , 15.93782101, 10.90365341, 10.29995243,\n",
       "       11.44454763, 11.29577245, 10.02501359, 10.40705303, 13.99264632,\n",
       "        9.71942845,  9.6596029 , 13.51948614, 10.03280048, 11.3462599 ,\n",
       "       11.6694075 , 13.39310438, 15.31585685, 14.65022365, 11.95410905,\n",
       "       16.9321904 , 12.78232071, 11.45893136, 17.89605715, 10.00715322,\n",
       "        8.51888894,  9.52457011, 14.16820579, 14.82795704, 12.62723439,\n",
       "       10.63588947, 12.94005962,  9.32408266, 10.93846715, 16.0944451 ,\n",
       "       10.86133174, 10.01248913, 18.6816892 , 13.25720641, 13.35270696,\n",
       "        8.88527563,  8.58409309,  8.96142525, 10.14950801,  9.17272246,\n",
       "       14.47116609, 13.65342342,  9.15875109, 11.8602155 , 11.48688299,\n",
       "       13.47393137, 14.59782964,  0.79715542, 11.1486249 , 13.36764181,\n",
       "       18.17967112, 12.40673063, 11.70659322, 12.23311969, 15.49796655,\n",
       "       13.48488776,  7.66914303, 11.55290481, 17.58103006, 15.80634623,\n",
       "       11.32305693,  6.25217208, -0.15663252, 15.38576731, 11.9445814 ,\n",
       "        9.97911152, 10.56534021,  9.30108551,  8.97178066, 14.0145391 ,\n",
       "        9.76081053, 11.72297896,  5.31826491,  9.88882204,  8.63423956,\n",
       "       13.73656734,  8.31522787,  8.21933897, 14.23052871,  9.64360212,\n",
       "       13.65546055,  7.36605728, 14.41793018, 14.21839219,  9.72770363,\n",
       "       15.69421509, 14.64459202, 13.50451296, 13.10861614, 17.28675259,\n",
       "       10.30036674, 14.34789485,  8.74785841,  9.57216215, 12.77495299,\n",
       "       10.62682463, 11.53334383, 11.06853856, 12.8287546 ,  8.01058264,\n",
       "       13.00414437, 12.97625295,  8.93895797, 18.4893656 , 13.42275172,\n",
       "       11.00996489, 15.95987041, 11.56830616, 12.1948892 ,  9.87382175,\n",
       "       18.40119435, 10.94298935, 13.13599322,  8.14726138, 11.43421353,\n",
       "       10.27890778,  8.09569432, 11.67411972,  7.58522835, 15.23974626,\n",
       "        9.61747753,  9.52953564, 11.21583969,  8.3388704 , 16.7037785 ,\n",
       "       10.21805142, 12.42171781, 10.56652697, 15.3372461 , 12.32262711,\n",
       "        9.76734461, 12.37447923, 13.34053209, 12.07139927, 15.65655961,\n",
       "       15.62124731, 11.45058013, 13.4610887 , 10.82423467, 12.55723343,\n",
       "       12.24804171, 14.70248412, 12.91083805, 13.25846289,  9.87979628,\n",
       "       10.87488442, 11.27848084,  8.39954437, 11.0041307 , 14.55276817,\n",
       "       12.78605186, 16.18168505, 12.71337454,  8.75781189,  8.41893678,\n",
       "       11.60810603, 12.60665598,  9.21754865, 12.77877158,  5.05851809,\n",
       "       16.47236305, 18.3839289 , 13.15386824, 15.16575083, 10.54599896,\n",
       "        6.94683976,  9.9359793 ,  8.24023036, 13.54771459, 10.25046911,\n",
       "       17.42462262, 18.57951762, 11.42491936,  6.62159528,  5.11898627,\n",
       "       16.13568568, 11.23740897, 12.09301325, 10.01767898, 13.46810351,\n",
       "        9.17513822, 10.68591217, 11.81124371, 10.5432424 , 12.70968213,\n",
       "       10.02199633, 13.69675754,  9.32921714, 12.29594326, 12.95963555,\n",
       "       12.5580772 , 11.28857031, 15.98336151,  7.89424803, 16.40918329,\n",
       "       13.71232168,  7.78884973,  0.58658714, 11.06380404, 11.29021117,\n",
       "       13.02147079, 14.08348981,  5.87714247, 10.2016675 , 14.66309374,\n",
       "        8.71604626, 12.06505277, 16.78003129, 14.55913278, 14.89255033,\n",
       "        8.90986665, 11.53508767, 10.44340225, 13.37630054, 11.03542554,\n",
       "       14.68521834, 17.67875591,  9.32129507, 10.45933049, 14.06417768,\n",
       "       13.71881097, 17.37087243, 15.72644364, 15.66674565, 13.74213046,\n",
       "        9.80822552,  9.14759102, 15.02562093, 13.278135  ,  9.37031239,\n",
       "        9.54049178, 12.2865566 ,  9.41344225, 11.18001288, 12.3602868 ,\n",
       "       12.28930312, 13.62616805,  7.50259838, 10.92690361,  8.36985381,\n",
       "       15.50906658,  8.8708353 , 12.03161394, 17.09728845])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1_train=r.predict(X_train)\n",
    "p1_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "3f969c6c",
   "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y_train,p1_train)\n",
    "plt.xlabel(\"actual\")\n",
    "plt.ylabel(\"pridict\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "ba73668c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "07bc9897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8504154042188079"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_train,p1_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "4a87b7c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10.05262594, 12.89073028, 15.37722555,  9.99980104, 13.67006529,\n",
       "       16.48683747,  9.11676569, 14.57066871, 15.51528588, 11.51964033,\n",
       "        8.57473056,  9.35599351, 15.93161817, 13.43037942, 11.68967953,\n",
       "       16.43074787, 12.8831602 , 11.19734345, 10.79980826, 13.65304222,\n",
       "       15.14685641, 12.73418694, 17.60367273, 14.43607518, 12.46215741,\n",
       "        7.99604021, 19.61888397,  9.3242138 , 12.40309631, 14.40273591,\n",
       "       15.2754924 ,  7.52618389, 14.21976461, 17.70249773, 10.54461022,\n",
       "       17.6633046 , 14.68144729, 13.29141453, 10.38252519,  6.70194436,\n",
       "       14.78941521, 10.49660125, 10.30928671, 15.51162158,  8.58147543,\n",
       "        7.46840945, 12.9669733 , 12.28947792,  8.50445575, 13.22888915,\n",
       "       14.66450342,  9.31320019, 10.96947757, 10.22383057, 11.76233319,\n",
       "        7.12674603, 11.33718624,  8.37778561, 16.09539552,  9.61490444,\n",
       "       12.60045965,  9.34463084, 14.38547115, 13.15567724, 12.20256888,\n",
       "       17.60669003, 18.04671305, 11.98663979, 11.33378774,  8.83172595,\n",
       "       11.57708731,  9.5594353 ,  9.30281978, 18.3235714 , 12.8857615 ,\n",
       "       16.48408937,  9.30835774, 12.75267031, 10.31692194, 18.37152092,\n",
       "       14.37726578, 11.38805888, 12.62596756, 13.57836698, 17.53080876,\n",
       "       10.52837112, 11.2343236 , 10.79469862, 14.57254986, 16.26990189,\n",
       "       13.87634884, 10.07822852, 11.24567354, 12.45526667, 12.36957246,\n",
       "        7.58723144, 11.59268094, 12.6356789 ,  8.1154001 , 11.66121914,\n",
       "       17.51103578, 13.61071112, 10.84524369, 12.24164794, 14.85559654,\n",
       "       11.30727774,  7.81340641, 11.35736396, 12.74117071, 12.98153617,\n",
       "       11.27559178,  9.86001197,  9.21546716, 11.86737236, 10.97114096,\n",
       "        0.293685  , 11.03921892, 13.27042718, 10.74280965,  8.79843924,\n",
       "       11.26327794,  8.35064428, 13.16636363, 11.75487499, 14.57375952,\n",
       "        9.75925297,  7.15731257, 11.21812899, 14.27009279, 14.08504029])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p1_test=r.predict(X_test)\n",
    "p1_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "7990f30e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>9</td>\n",
       "      <td>10.052626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>13</td>\n",
       "      <td>12.890730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>15</td>\n",
       "      <td>15.377226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>11</td>\n",
       "      <td>9.999801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>14</td>\n",
       "      <td>13.670065</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     y_test      y_hat\n",
       "482       9  10.052626\n",
       "167      13  12.890730\n",
       "339      15  15.377226\n",
       "350      11   9.999801\n",
       "390      14  13.670065"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = pd.DataFrame({ 'y_test':y_test,'y_hat':p1_test})\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "8e0be8fc",
   "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": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter(y_test,p1_test)\n",
    "plt.xlabel(\"actual\")\n",
    "plt.ylabel(\"pridict\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "9bc40206",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "affe74c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8803963017535749"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(y_test,p1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "968b1d3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "dc1a1663",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7401145052366878\n",
      "1.1575090219167044\n",
      "1.075875932399598\n"
     ]
    }
   ],
   "source": [
    "print(metrics.mean_absolute_error(y_test, p1_test))\n",
    "print(metrics.mean_squared_error(y_test, p1_test))\n",
    "print(np.sqrt(metrics.mean_squared_error(y_test, p1_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92e26541",
   "metadata": {},
   "source": [
    "# SVR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "9d39d36b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries for data manipulation and visualization\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Import libraries for machine learning\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "ae807052",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the SVR model\n",
    "model = SVR()\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "547e3fdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting the Test set results\n",
    "y_hat = model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "150d6b46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>9</td>\n",
       "      <td>10.015636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>13</td>\n",
       "      <td>13.562910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>15</td>\n",
       "      <td>15.262372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>11</td>\n",
       "      <td>10.437456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>14</td>\n",
       "      <td>13.916401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     y_test      y_hat\n",
       "482       9  10.015636\n",
       "167      13  13.562910\n",
       "339      15  15.262372\n",
       "350      11  10.437456\n",
       "390      14  13.916401"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = pd.DataFrame({ 'y_test':y_test,'y_hat':y_hat})\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "f59da89b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mac/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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.scatterplot(x=y_test, y=y_hat, alpha=0.6)\n",
    "sns.lineplot(y_test, y_test)\n",
    "\n",
    "plt.xlabel('Actual count', fontsize=14)\n",
    "plt.ylabel('Prediced  count', fontsize=14)\n",
    "plt.title('Actual vs Predicted  count (test set)', fontsize=17)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "e932057f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8571949315192033"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "a5106dd0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.17\n",
      "1.38\n",
      "0.742996056110151\n"
     ]
    }
   ],
   "source": [
    "MSE_test = round(np.mean(np.square(y_test - y_hat)),2)\n",
    "RMSE_test = round(np.sqrt(MSE_test),2)\n",
    "print(RMSE_test)\n",
    "print(MSE_test)\n",
    "print(metrics.mean_absolute_error(y_test, y_hat))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f92742c0",
   "metadata": {},
   "source": [
    "# Tuning hyperparameters:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "62f4e20f",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_param_grid = {'C': [1,10,100], 'gamma': [1,0.1,0.01], 'kernel': ['rbf']} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "d954c8ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "af8a0a93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVR(),\n",
       "             param_grid={'C': [1, 10, 100], 'gamma': [1, 0.1, 0.01],\n",
       "                         'kernel': ['rbf']},\n",
       "             verbose=3)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GridSearchCV(estimator=SVR(),param_grid= my_param_grid, refit = True, verbose=3, cv=5 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "34d16561",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid = GridSearchCV(estimator=SVR(),param_grid= my_param_grid, refit = True, verbose=2, cv=5 )\n",
    "# verbose just means the text output describing the process. (the greater the number the more detail you will get)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "eff781bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n",
      "[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ...........................C=1, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .........................C=1, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s\n",
      "[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s\n",
      "[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s\n",
      "[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s\n",
      "[CV] END ........................C=1, gamma=0.01, kernel=rbf; total time=   0.0s\n",
      "[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ..........................C=10, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ........................C=10, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s\n",
      "[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s\n",
      "[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s\n",
      "[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s\n",
      "[CV] END .......................C=10, gamma=0.01, kernel=rbf; total time=   0.0s\n",
      "[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .........................C=100, gamma=1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END .......................C=100, gamma=0.1, kernel=rbf; total time=   0.0s\n",
      "[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s\n",
      "[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s\n",
      "[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s\n",
      "[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s\n",
      "[CV] END ......................C=100, gamma=0.01, kernel=rbf; total time=   0.0s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVR(),\n",
       "             param_grid={'C': [1, 10, 100], 'gamma': [1, 0.1, 0.01],\n",
       "                         'kernel': ['rbf']},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.fit(x_val,y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "5e2718b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 100, 'gamma': 0.01, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "9d0bafa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100, gamma=0.01)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "8d5a23e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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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>y_test</th>\n",
       "      <th>y_hat</th>\n",
       "      <th>y_hat_optimized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>9</td>\n",
       "      <td>10.015636</td>\n",
       "      <td>8.241295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>13</td>\n",
       "      <td>13.562910</td>\n",
       "      <td>12.922618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>15</td>\n",
       "      <td>15.262372</td>\n",
       "      <td>16.449625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>11</td>\n",
       "      <td>10.437456</td>\n",
       "      <td>8.564247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>14</td>\n",
       "      <td>13.916401</td>\n",
       "      <td>13.882668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     y_test      y_hat  y_hat_optimized\n",
       "482       9  10.015636         8.241295\n",
       "167      13  13.562910        12.922618\n",
       "339      15  15.262372        16.449625\n",
       "350      11  10.437456         8.564247\n",
       "390      14  13.916401        13.882668"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_hat_optimized = grid.predict(X_test)\n",
    "\n",
    "predictions = pd.DataFrame({ 'y_test':y_test,'y_hat':y_hat,'y_hat_optimized':y_hat_optimized})\n",
    "predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "a3d8c14c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mac/opt/anaconda3/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(x=y_test, y=y_hat_optimized, alpha=0.6)\n",
    "sns.lineplot(y_test, y_test)\n",
    "\n",
    "plt.xlabel('Actual count', fontsize=14)\n",
    "plt.ylabel('Prediced  count', fontsize=14)\n",
    "plt.title('Actual vs optimized predicted count (test set)', fontsize=17)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "5fc2092c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9991575240449757"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid.score(x_val, y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "3a624b8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.24\n",
      "1.53\n"
     ]
    }
   ],
   "source": [
    "MSE_test_opt = round(np.mean(np.square(y_test - y_hat_optimized)),2)\n",
    "RMSE_test_opt = round(np.sqrt(MSE_test_opt),2)\n",
    "print(RMSE_test_opt)\n",
    "print(MSE_test_opt)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b86b9916",
   "metadata": {},
   "source": [
    "# Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "924a133a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "550bd2db",
   "metadata": {},
   "outputs": [],
   "source": [
    "NMSE = cross_val_score(estimator = SVR(C=10, gamma=0.01), X = X_train, y = y_train, cv = 5 , scoring=\"neg_mean_squared_error\" )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "5532e5ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4025"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MSE_CV = round(np.mean(-NMSE),4)\n",
    "RMSE_CV = round(np.sqrt(MSE_CV),4)\n",
    "RMSE_CV"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d062d7e",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "fc29d93c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "23450bd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor ()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "325b8266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit (X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "9b588d06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 9.97, 13.11, 15.41,  9.99, 13.13, 16.15,  9.63, 15.01, 15.43,\n",
       "       11.58,  8.42,  8.81, 16.37, 13.14, 11.35, 16.3 , 12.47, 11.24,\n",
       "       11.68, 13.23, 15.29, 12.66, 17.82, 15.02, 13.61,  7.76, 18.04,\n",
       "        9.27, 13.  , 14.59, 15.61,  4.64, 12.92, 17.88, 10.37, 17.53,\n",
       "       15.14, 13.09, 10.45,  7.66, 14.88, 10.33,  9.49, 15.36,  8.27,\n",
       "        7.14, 12.37, 13.33,  5.22, 12.89, 14.35, 10.08, 11.67, 10.44,\n",
       "       11.02,  7.94, 11.44,  8.94, 16.4 ,  9.27, 12.73,  9.23, 14.11,\n",
       "       12.71, 12.32, 17.48, 17.95, 12.61, 11.31,  9.25, 11.1 ,  9.43,\n",
       "        9.45, 17.69, 13.21, 16.15,  9.53, 13.17,  9.83, 18.21, 14.19,\n",
       "       11.68, 12.73, 13.08, 17.71, 10.44, 11.81, 11.36, 14.51, 16.32,\n",
       "       14.92,  9.43, 11.35, 12.75, 12.9 ,  6.64, 11.84, 12.45,  4.85,\n",
       "       11.35, 17.09, 12.9 , 10.42, 12.29, 15.52, 11.28,  8.02, 11.14,\n",
       "       12.38, 13.1 , 11.48,  9.73,  9.35, 12.51, 11.15,  1.24, 11.15,\n",
       "       13.29, 10.45,  5.27, 10.8 ,  6.12, 12.73, 12.69, 15.05,  9.52,\n",
       "        7.9 , 10.76, 15.19, 13.51])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = rf.predict (X_test)\n",
    "y_pred "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "e6e368bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5636607692307696"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "c16c80c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2504642214916706"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "af6109d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y_test</th>\n",
       "      <th>y_hat</th>\n",
       "      <th>y_hat_optimized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>9</td>\n",
       "      <td>9.97</td>\n",
       "      <td>8.241295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>13</td>\n",
       "      <td>13.11</td>\n",
       "      <td>12.922618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>15</td>\n",
       "      <td>15.41</td>\n",
       "      <td>16.449625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>11</td>\n",
       "      <td>9.99</td>\n",
       "      <td>8.564247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>14</td>\n",
       "      <td>13.13</td>\n",
       "      <td>13.882668</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     y_test  y_hat  y_hat_optimized\n",
       "482       9   9.97         8.241295\n",
       "167      13  13.11        12.922618\n",
       "339      15  15.41        16.449625\n",
       "350      11   9.99         8.564247\n",
       "390      14  13.13        13.882668"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the best set of parameters to make predictions on the test data\n",
    "y_hat = rf.predict(X_test)\n",
    "y_hat_optimized = grid.predict(X_test)\n",
    "predictions1 = pd.DataFrame({ 'y_test':y_test,'y_hat':y_hat,'y_hat_optimized':y_hat_optimized})\n",
    "predictions1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "a355ad81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 8, 'n_estimators': 100}\n",
      "Mean Squared Error: 1.56\n",
      "R2 Score: 0.84\n"
     ]
    }
   ],
   "source": [
    "# Define the parameter values to try\n",
    "param_grid = {'n_estimators': [10, 100, 1000], 'max_depth': [2, 4, 6, 8]}\n",
    "\n",
    "# Create the Random Forest model\n",
    "model = RandomForestRegressor(n_estimators=100)\n",
    "\n",
    "# Create the grid search object\n",
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5)\n",
    "\n",
    "# Fit the grid search object to the training data\n",
    "grid_search.fit(x_val, y_val)\n",
    "\n",
    "# Get the best set of parameters\n",
    "print(grid_search.best_params_)\n",
    "\n",
    "\n",
    "# Calculate the mean squared\n",
    "mse = mean_squared_error(y_test, y_hat)\n",
    "r2 = r2_score(y_test, y_hat)\n",
    "\n",
    "print(f'Mean Squared Error: {mse:.2f}')\n",
    "print(f'R2 Score: {r2:.2f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "f140fbc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVR: MSE = 1.53, R2 = 0.84\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest: MSE = 1.35, R2 = 0.86\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest is the better model\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Train and evaluate the SVR model\n",
    "model_svr = SVR()\n",
    "param_grid_svr = {'C': [0.1, 1, 10, 100], 'gamma': [0.01, 0.1, 1]}\n",
    "grid_search_svr = GridSearchCV(estimator=model_svr, param_grid=param_grid_svr, cv=5)\n",
    "grid_search_svr.fit(x_val, y_val)\n",
    "y_pred_svr = grid_search_svr.predict(X_test)\n",
    "mse_svr = mean_squared_error(y_test, y_pred_svr)\n",
    "r2_svr = r2_score(y_test, y_pred_svr)\n",
    "print(f'SVR: MSE = {mse_svr:.2f}, R2 = {r2_svr:.2f}')\n",
    " # Plot the SVR predictions\n",
    "plt.scatter(y_test, y_pred_svr)\n",
    "plt.xlabel('True Values')\n",
    "plt.ylabel('Predictions')\n",
    "plt.title('SVR Predictions')\n",
    "plt.show()\n",
    "\n",
    "# Train and evaluate the Random Forest model\n",
    "model_rf = RandomForestRegressor()\n",
    "param_grid_rf = {'n_estimators': [10, 50, 100], 'max_depth': [5, 10, 20]}\n",
    "grid_search_rf = GridSearchCV(estimator=model_rf, param_grid=param_grid_rf, cv=5)\n",
    "grid_search_rf.fit(x_val, y_val)\n",
    "y_pred_rf = grid_search_rf.predict(X_test)\n",
    "mse_rf = mean_squared_error(y_test, y_pred_rf)\n",
    "r2_rf = r2_score(y_test, y_pred_rf)\n",
    "print(f'Random Forest: MSE = {mse_rf:.2f}, R2 = {r2_rf:.2f}')\n",
    "# Plot the rf predictions\n",
    "plt.scatter(y_test, y_pred_rf)\n",
    "plt.xlabel('True Values')\n",
    "plt.ylabel('Predictions')\n",
    "plt.title('RF Predictions')\n",
    "plt.show()\n",
    "# Compare the performance of the two models\n",
    "if mse_svr < mse_rf:\n",
    "    print('SVR is the better model')\n",
    "else:\n",
    "    print('Random Forest is the better model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40138ff8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Attachments",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}