EDA_math.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Импортируем библиотеки и датасет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from itertools import combinations\n",
    "from scipy.stats import ttest_ind\n",
    "\n",
    "pd.set_option('display.max_rows', 50)  # показывать больше строк\n",
    "pd.set_option('display.max_columns', 50)  # показывать больше колонок\n",
    "\n",
    "math = pd.read_csv('stud_math.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Первичный взгляд на датасет"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>reason</th>\n",
       "      <th>guardian</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
       "      <th>schoolsup</th>\n",
       "      <th>famsup</th>\n",
       "      <th>paid</th>\n",
       "      <th>activities</th>\n",
       "      <th>nursery</th>\n",
       "      <th>studytime, granular</th>\n",
       "      <th>higher</th>\n",
       "      <th>internet</th>\n",
       "      <th>romantic</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>health</td>\n",
       "      <td>NaN</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>services</td>\n",
       "      <td>other</td>\n",
       "      <td>reputation</td>\n",
       "      <td>mother</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>17</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>other</td>\n",
       "      <td>teacher</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>A</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>services</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu      Mjob     Fjob  \\\n",
       "0     GP   F   18       U     NaN       A   4.0   4.0   at_home  teacher   \n",
       "1     GP   F   17       U     GT3     NaN   1.0   1.0   at_home    other   \n",
       "2     GP   F   15       U     LE3       T   1.0   1.0   at_home    other   \n",
       "3     GP   F   15       U     GT3       T   4.0   2.0    health      NaN   \n",
       "4     GP   F   16       U     GT3       T   3.0   3.0     other    other   \n",
       "5     GP   M   16       U     LE3       T   4.0   3.0  services    other   \n",
       "6     GP   M   16     NaN     LE3       T   2.0   2.0     other    other   \n",
       "7     GP   F   17       U     GT3       A   4.0   4.0     other  teacher   \n",
       "8     GP   M   15       U     LE3       A   3.0   2.0  services    other   \n",
       "9     GP   M   15       U     NaN     NaN   3.0   4.0     other    other   \n",
       "\n",
       "       reason guardian  traveltime  studytime  failures schoolsup famsup paid  \\\n",
       "0      course   mother         2.0        2.0       0.0       yes     no   no   \n",
       "1      course   father         1.0        2.0       0.0        no    yes   no   \n",
       "2       other   mother         1.0        2.0       3.0       yes     no  NaN   \n",
       "3        home   mother         1.0        3.0       0.0        no    yes  yes   \n",
       "4        home   father         1.0        2.0       0.0        no    yes  yes   \n",
       "5  reputation   mother         1.0        2.0       0.0        no    yes  yes   \n",
       "6        home   mother         1.0        2.0       0.0        no     no   no   \n",
       "7        home   mother         2.0        2.0       0.0       yes    yes   no   \n",
       "8        home   mother         1.0        2.0       0.0        no    yes  yes   \n",
       "9        home   mother         1.0        2.0       0.0        no    yes  yes   \n",
       "\n",
       "  activities nursery  studytime, granular higher internet romantic  famrel  \\\n",
       "0         no     yes                 -6.0    yes      NaN       no     4.0   \n",
       "1         no      no                 -6.0    yes      yes       no     5.0   \n",
       "2         no     yes                 -6.0    yes      yes      NaN     4.0   \n",
       "3        yes     yes                 -9.0    yes      yes      yes     3.0   \n",
       "4         no     yes                 -6.0    yes       no       no     4.0   \n",
       "5        yes     yes                 -6.0    yes      yes       no     5.0   \n",
       "6         no     yes                 -6.0    yes      yes       no     4.0   \n",
       "7         no     yes                 -6.0    yes       no       no     4.0   \n",
       "8         no     yes                 -6.0    yes      yes       no     NaN   \n",
       "9        yes     yes                 -6.0    yes      yes       no     5.0   \n",
       "\n",
       "   freetime  goout  health  absences  score  \n",
       "0       3.0    4.0     3.0       6.0   30.0  \n",
       "1       3.0    3.0     3.0       4.0   30.0  \n",
       "2       3.0    2.0     3.0      10.0   50.0  \n",
       "3       2.0    2.0     5.0       2.0   75.0  \n",
       "4       3.0    2.0     5.0       4.0   50.0  \n",
       "5       4.0    2.0     5.0      10.0   75.0  \n",
       "6       4.0    4.0     3.0       0.0   55.0  \n",
       "7       1.0    4.0     1.0       6.0   30.0  \n",
       "8       2.0    2.0     1.0       0.0   95.0  \n",
       "9       5.0    1.0     5.0       0.0   75.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(math.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Информация о колонках"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 395 entries, 0 to 394\n",
      "Data columns (total 30 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   school               395 non-null    object \n",
      " 1   sex                  395 non-null    object \n",
      " 2   age                  395 non-null    int64  \n",
      " 3   address              378 non-null    object \n",
      " 4   famsize              368 non-null    object \n",
      " 5   Pstatus              350 non-null    object \n",
      " 6   Medu                 392 non-null    float64\n",
      " 7   Fedu                 371 non-null    float64\n",
      " 8   Mjob                 376 non-null    object \n",
      " 9   Fjob                 359 non-null    object \n",
      " 10  reason               378 non-null    object \n",
      " 11  guardian             364 non-null    object \n",
      " 12  traveltime           367 non-null    float64\n",
      " 13  studytime            388 non-null    float64\n",
      " 14  failures             373 non-null    float64\n",
      " 15  schoolsup            386 non-null    object \n",
      " 16  famsup               356 non-null    object \n",
      " 17  paid                 355 non-null    object \n",
      " 18  activities           381 non-null    object \n",
      " 19  nursery              379 non-null    object \n",
      " 20  studytime, granular  388 non-null    float64\n",
      " 21  higher               375 non-null    object \n",
      " 22  internet             361 non-null    object \n",
      " 23  romantic             364 non-null    object \n",
      " 24  famrel               368 non-null    float64\n",
      " 25  freetime             384 non-null    float64\n",
      " 26  goout                387 non-null    float64\n",
      " 27  health               380 non-null    float64\n",
      " 28  absences             383 non-null    float64\n",
      " 29  score                389 non-null    float64\n",
      "dtypes: float64(12), int64(1), object(17)\n",
      "memory usage: 92.7+ KB\n"
     ]
    }
   ],
   "source": [
    "math.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['school', 'sex', 'age', 'address', 'famsize', 'Pstatus', 'Medu', 'Fedu',\n",
       "       'Mjob', 'Fjob', 'reason', 'guardian', 'traveltime', 'studytime',\n",
       "       'failures', 'schoolsup', 'famsup', 'paid', 'activities', 'nursery',\n",
       "       'studytime, granular', 'higher', 'internet', 'romantic', 'famrel',\n",
       "       'freetime', 'goout', 'health', 'absences', 'score'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Кол-во пропусков в признаках"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABGoAAAMECAYAAADn7sOiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOzdeZR1WVkf4Pct0DAoCoILNRIBESVMahQEQ5RYalRIREvjLBrRpYlGlkZxCIpxSNQsFeOsrStRgyVkiUPSlEhrkICC0OCEGnBsh2WiSJyhdv449/JVf13363O/Pvfu99zzPGv1sqtOY+11zzn77PPu3943W2sBAAAAQH9HvRsAAAAAwEChBgAAAKAIhRoAAACAIhRqAAAAAIpQqAEAAAAo4s7XOnh8dOIroZjUjbfc3LsJt/FBb/+I3k0AAGBCxpxAdWfnp7npmEQNAAAAQBHXTNTA1CrOJJhxAQA4LMZSwJwp1LB4HuQALIkJCuij4r0X4f6Diix9AgAAAChCogZgYhVnzMyWAWv6A+jDvQeMJVEDAAAAUIREDcDEzJgBAADXS6IGAAAAoAiFGgAAAIAiLH06UBU3M42wJAQAAACuRaIGAAAAoAiJmgMluQIAAADzo1ADAACwUBW3TDDpzNJZ+gQAAABQhEQNe1WxYl+RWQQAAPbBuBPqUahhryo+CG685eaS7QIAAGB5LH1i8RRpAAAAqEKihsWruBxL8QgAAGCZJGoAAAAAipCoYfGkVwAADovE9Hg+K6hHoQYAADgoFV/0KxZEImp+VrB0lj4BAAAAFCFRAwAAsGOSK8BYEjUAAAAARUjUsHgV1wubcQEAAFgmhRoWT1EEAACAKix9AgAAAChCogZgYpbTMaWK11OEa2qsiufPuZs31xTA4ZOoAQAAACgiW2sbDx4fnWw+CADA7EhkAEB/Z+enuemYRA0AAABAEQo1AAAAAEXYTBgAYEEsMwIushwS6pGoAQAAAChCogYYxWwLcJE+Yb6cO+Ai9x/Uo1ADjOIhPp6XIJbANTWO/mDenD8AerD0CQAAAKCIbK1tPHh8dLL5IAAAAABbOzs/zU3HJGoAAAAAirBHDQAAAHCHVdzbK2J++3sp1LB4FTuTuXUkAAAA3mOmYekTAAAAQBESNSyeqi9Tk9KaL+eOJXCdQx8V770I9x9UJFEDAAAAUIREDcDEzEzNl3M3bxVnqyteUxXbBABcoVDD4hnYAxyGin2nZwyw5t4DxrL0CQAAAKAIiRoWz+zGfFWcqY5wTQFX6A8AgG1J1AAAAAAUIVEDzJaZaqZWMaXlOgcALmPccrgUagAm5qE5Xz4nlkAfBXAY9J2Hy9InAAAAgCIkatgrs3gsgWsKqEwfNZ5xC/Th3mPpsrW28eDx0cnmgwDMhgEP9OHeAwAuc3Z+mpuOWfoEAAAAUISlTwALYAYd+nDvsQSSYwDTkqgBAAAAKEKiBgBWzArPm/MHfbjOAaalUMNeGUQDlekP5s35AwAOgaVPAAAAAEVI1LBXZjuByqT+AADoTaIGAAAAoAiJGgC6kF5hCVznAMC2FGoA6MLLIkvgOgcAtmXpEwAAAEAREjUAADti6RMAsC2JGgAAAIAiJGoA6ELSgCVwTQFrFZ97EfopqEihBhil4uDCwGLenD8AlsRzDxjL0icAAACAIiRqgFHMAgGwKxVTmxGefSxDxfvPvcfSSdQAAAAAFCFRAzAxM1MAwFwYI0A9CjUAEzPgAdiOfhMArrD0CQAAAKAIiRpgFMt5AAAAdk+iBgAAAKAIiRpgFOmV8aSPAACA66VQAzAxRRGWQEESWNMfAEzL0icAAACAIiRqAOjCDOy8+ayANf0BwLQkagAAAACKkKgBoAszsAAAcFsKNQAAO1JxiV9FCrcsQdX+wP0H9Vj6BAAAAFCERM2BUrGHfiref+49YE1/wBJ4FgNzJlEDAAAAUES21jYePD462XwQAO4As50AACzV2flpbjpm6ROL52Vxviqeuwjnbyyf07xVvP9cU/NV8XqKcE0xLdc5MJalTwAAAABFWPoEAAAAM1MxpSWhNZ6lTwAAAHBAFEUOl6VPAAAAAEVI1ACjiFYCHAb9OQDUJlEDAAAAUIREDQDAgkivAEBtCjXAKBUH9hXj+1VVPH8VVbymnLt5c00BANuy9AkAAACgCIkaYLbMCs+bpAFL4JoCALYlUQMAAABQhEQNAF1IGrAEkmMAwLYUagAAdkRRBADYlqVPAAAAAEVI1LB4YuksgescAADmQaIGAAAAoAiJGhbPrD5L4DoHAIB5UKgBAAAOiiW/wJxZ+gQAAABQhEQNAABwUKRXgDmTqAEAAAAoQqKGxbOGeRyfEwDA9as4loownoKKsrW28eDx0cnmgwBcquJAzCBsHOduPJ/VfDl3ANDf2flpbjpm6RMAAABAERI1AAAAAHskUQMAAAAwAzYTBoAVe3cAANCbQg0ArCiKAADQm6VPAAAAAEVI1AAAACyUZb9Qj0QNAAAAQBESNQALYLYMAADmQaEGYAEURQCAy1QcI5hgYuksfQIAAAAoQqLmQFWsQkeoRAMAsExVx+cVeWdg6RRqDlTVzq3iA6rqZwUAMAfGd+NUbBNQk6VPAAAAAEVI1LBXZhKgD7OdAOyK/hxgWhI1AAAAAEVI1LBXZvWhD9c59OG5BwBsS6EGADgIiiIAwCGw9AkAAACgCIka9srMIgC74hkDrEnYAXMmUQMAAABQhEQNi2fGBfpw70EfFe+9qvQJ8+XcAXOmUMPieZBDH+49YE1/AABXWPoEAAAAUIREDQDAgkivAEBtEjUAAAAARUjUAAAA163iBtWSY8CcKdQAAMAlFCDGqdgmgDmz9AkAAACgCIkaAAC4hKTIfElDAXMmUQMAAABQhEQNAABwUKRXgDlTqAFmq2KsOcLgEOBQVHzOeMYAa/qow2XpEwAAAEAREjXAbKnYA7BLnjPAmvQK+yRRAwAAAFCERA0AXZiZAgDmwhiBfVKoAaALA55xKha0qnJNsQQV+wT3HsC0LH0CAAAAKEKi5kBVnG2JMOMyVsXz59xBH+494CJ9AvRhfM4+SdQAAAAAFJGttY0Hj49ONh8EAAAAYGtn56e56ZilTwAAAAtlSQ/UY+kTAAAAQBESNQAAAAslvQL1SNQAAAAAFCFRc6AqrjWNqFmxr/hZVfycADgMnntMzTUFMC3f+gQAAACwR9f61idLnwAAAACKsPQJgC5E5VkC1zn04d4D5szSJwAAuqr4Uh3hxRqA3bH0CQAAAGAGLH0CAKAryRUAuEKiBgAAAKAIiRoAAADgDrPn2DQUagAAABaq4ov13F6qucK5m4alTwAAAABFSNQAAAAslAQE1CNRAwAAAFCERA0wWxXXVEeYmZqziteU64mpuc4BoLZsrW08eHx0svkgAAAAAFs7Oz/NTccsfQIAAAAoQqEGAAAAoAiFGgAAAIAibCYMMDEbdbIErnMAgN1QqGHxvGwwNecPAAC4XpY+AQAAABQhUcPiST8AbE/fCQCwGxI1AAAAAEVI1AAAAAfFHoTAnCnUAAAAB0VRBJgzS58AAAAAipCoYfFEY1kC1/k4FT+nqiqeP1iCiv2U/mCciucuwvmDiiRqAAAAAIrI1trGg8dHJ5sPAgAAALC1s/PT3HTM0if2qmLkU9wTYHv6cwCA3bD0CQAAAKAIiRr2ymwnAAC7JvUHzJlEDQAAAEAREjUAwNbMDDOliumHCNf5nDl3wJwp1AAA0JWXaqZWsfjnOgfGsvQJAAAAoAiJGoCJmcUDAObCuAXqkagBAAAAKCJbaxsPHh+dbD4IAAAAwNbOzk9z0zFLnwAmJkIMAMASVRwHR8xvLGzpEwAAAEAREjUAE5tbxR4AAKZgHDwNiRoAAACAIhRqAAAAAIpQqAEAAAAoQqEGAAAAoAiFGgAAAIAifOsTALC1G2+5uXcTbsM3TQBr+ihgziRqAAAAAIqQqAEAtmZmmClVTD9EuM7nzLkD5kyiBgAAAKAIiRoAgB2pmBSpmDSo2CYA6EWhhr0yYGVKFa+nCNcUcIX+AADYlqVPAAAAAEVI1ACzZaaaJZAcAy6q2CfoD4C1in1UxPz6KYkaAAAAgCKytbbx4PHRyeaDAAAAAGzt7Pw0Nx2z9InFqxjPm1s0DwAApmJ8ztJZ+gQAAABQhEQNi6c6DqyZwQM4DPrzefNZsXQSNQAAAABFSNQAwIoZPAAAelOoAYAVUXmAw6DvBObM0icAAACAIiRqACYmlTFfPicAAHqTqAEAAAAoQqIGYGJSGUBlUn/ARfoEqEehBhjFQ3zenL9xfE4sgWsKuEifAPVY+gQAAABQhEQNMIrZFqYmvQIAALclUQMAAABQhEQNi2dWnyVwTQFAXxXHnBHGCFCRQg2L5+HEElQcHLr3gLWKfVSEfoppuZ6AsSx9AgAAAChCogZgAcziAbAkFVNansXAWAo1wCgVBzwRBj1My3UOfbjG561i3+maAubM0idgtgzCWALXOQDAsmRrbePB46OTzQcBAAAA2NrZ+WluOiZRAwAAAFCEPWoA6MKeBgAAcFsKNcBsVXzRj/CyP5bPCQDg+lUcCxvfTcPSJwAAAIAiJGqA2VKxB6oz2wnArujPD5dEDQAAAEAREjUsntlOYE1/wNScP2Ct4jMmQj8FFWVrbePB46OTzQcBAJidii+LXhQBWJqz89PcdMzSJwAAAIAiLH1i8cwsArArnjEsgescYFoSNQAAAABF2KMGAAAAuMMqJuwiaqbsrrVHjaVPABOr+ICq+HCCqbn3AKAvz71pWPoEAAAAUIREDQBwEMziMTUpLQB6kKgBAAAAKEKihsUzW8bUnD+WQN/JErimgMo8iw+Xb30CAAAA2KNrfeuTpU8AAAAARVj6BAAAHJSKS0KqslQF6pGoAQAAAChCogaYraqzZWamAKAvz2JgzhRqgNkyCGNqFYt/rnMAgGWx9AkAAACgCIkaFs8MOrDm3htP3wms6Q8ApiVRAwAAAFBEttY2Hjw+Otl8kNIqzmxEmN2Ys4rXlOsJAACYo7Pz09x0zNKnA+UFlqm5psZT1Jov5w76qHjvRbj/WIaK9597j6Wz9AkAAACgCIkaYBSzLSyBawr6cO9BP+4/qEeiBgAAAKAIiRpgFLMt4/msAKCvikngqoxboB6FGgAAuqr6Uu0Fdr6cO2DOLH0CAAAAKEKiBoAuKs6gm4FlCdx7AIdBf364srW28eDx0cnmgwAAAABs7ez8NDcds/QJAAAAoAhLn4DZqhj3jBD5BAAArp9EDQAAAEAREjXsVcUEhPTDfDl3QHWeewDAthRqDlTFgWGEwSEAy+K5BwBsy9InAAAAgCIkag6UGTzop2KiTZ8AfegPAIBtSdQAAAAAFCFRAzAxs9UsgaTIOBXbBFPTHwBMS6IGAAAAoAiFGgAAAIAiLH1ir0RjAQ6DvhNY0x8AaxXf9yLm109J1AAAAAAUIVHDXs2tkgnsTsUZF30UAMD1M5aahkINi+dlcb4qnrsI528snxMAANyWpU8AAAAARUjUsHhm9efLuQMAAA6NRA0AAABAEQo1AAAAAEVY+gTMls2EAQCAQyNRAwAAAFCERA0wW5IrANurmEbUnwPAFRI1AAAAAEVI1AAALIj0CgDUplADjCIqD7A9fScAsC1LnwAAAACKkKgBRjEDy9QkDVgC1xRLoD8fp+LnFFHzs4Klk6gBAAAAKCJbaxsPHh+dbD4IB6Li7IaZjXlzTc2XcwcAwD6cnZ/mpmOWPgFMzIv1fDl3AAD0ZukTAAAAQBESNSyeGXQAlsQSPwCoTaEGYCGqvZx5MYN+3H8sgeceMFeWPgEsQLXBKtCPl0WWwHMPmDOJGhav4oPcIBoA4PoZSwFzJlEDAAAAUIREDYtnxoUlcJ0DAMA8SNQAAAAAFKFQAwAAAFCEpU8AAAAwM74U5XBJ1AAAAAAUIVEDAACwYxXTDxESEHPm3B0uhRoAAOCgVCyKeKkGxrL0CQAAAKAIiRoAgAWRNGAJXFPAnEnUAAAAABQhUcPiVZxZrMjMFMBh0J8DQG0KNSyeASsAwGExETeesTDUY+kTAAAAQBESNexVxdkNswjjOHcA29N3Qh+u8/H0U1CPRA0AAABAEdla23jw+Ohk80EAZsNsGQAA1HF2fpqbjln6xOJ5gWUJXFMAADAPlj4BAAAAFCFRAwAAHBSJaWDOJGoAAAAAipCoYfHMbgCwJJIGLIFrCpgzhRoAAAC4BkVu9snSJwAAAIAiJGoAAAAoQ3qFqVW8pq5FogYAAACgiGytbTx4fHSy+SBch4qVTNVxAADgWrzHMLWz89PcdMzSJ/ZKZwIAAMyN9xj2ydInAAAAgCIkagCArYmAA2ynYr8Zoe+EiiRqAAAAAIqQqAEAtmYGFmA7+k1gLIkaAAAAgCIUagAAAACKsPQJAICubLIKAFco1AAA0JWCCMD2FLkPl6VPAAAAwB2mSDMNiRoAAACYGUWRwyVRAwAAAFCERA2LV3Ftp+o4AADAMinUsHiKIgCHQeEdADgElj4BAAAAFCFRAwAcBOmV+aqYhqrKdQ5w+CRqAAAAAIqQqAEAoCspEQC4QqEGANhaxaUqXvbHce4AoDZLnwAAAACKkKgBALYmAQFUJjkGfVS89yLmd/9J1AAAAAAUka21jQePj042HwTgUhVnEuY2iwAAAIfs7Pw0Nx2z9AlgYooiAMBcmGCCeix9AgAAAChCogYAYEHMngMXuf+gHokaAAAAgCIkaoDZqjgrHGFmCqhNHwUAtSnUALPlZQMAgKWqOGlpfD4NS58AAAAAipCoYa9UfQEAgLnxHsM+SdQAAAAAFCFRw16p+gKwJGZgAQ6DvpN9UqgBoAsvsCyBawoA2JalTwAAAABFSNQAs1UxkRFhBn0snxMAANyWRA0AAABAERI1wGxJZAAAAIdGoQYAVioup1OQBABYFkufAAAAAIqQqGHxzKADaxXvvYp9VETNzwqWoGqfUI0+Cvqo2kfNrU+QqAEAAAAoIltrGw8eH51sPgjXoWKFdW7VVQAAAObt7Pw0Nx2z9Im9UhRhCRQkAYC5MG6Beix9AgAAAChCogZgYmaBAIC5MG6BeiRqAAAAAIpQqAEAAAAowtInYBQbzQFsT98JAGxLogYAAACgCIkaYBQzsADb03cCANtSqGHxxNKBNf0BU3NNAQDbsvQJAAAAoAiJGvbKzCKwpj9gCVxTAMC2JGoAAAAAipCoYa/MLM6X9ANTc/7mTZ8wX84dwGHQnx+ubK1tPHh8dLL5IFwHnQkAS+K5BwBc5uz8NDcds/QJAAAAoAhLn9grs3gALInnHgCwLYkaAAAAgCIkali8ivsHVGRWGACYC+O78YzxoB6FGhbPwwn6qDiI1h8AHAb9OTBnlj4BAAAAFCFRc6AqzlRHmN0ArqjYH1TsOyt+TgCwNMYI7JNEDQAAAEAR2VrbePD46GTzQQAAAAC2dnZ+mpuOSdQAAAAAFKFQAwAAAFCEzYQBAACAg1VxM+hrkagBAAAAKEKihsWrWF31VXsAAADTqPh+dXa++ZhCDYtX8aYF4DBUnAyoyLMYAK6w9AkAAACgCIkaAIAdkRQBALYlUQMAAABQhEINAAAAQBGWPgEAwCUqbgZtOR3A4ZOoAQAAAChCogZgYmZgAQ6DvnMczz2AaSnUAEzM4BCAJan63FNAAubK0icAAOCgKNIAcyZRw15VfGhW5EEOAHD9jKWAOZOoAQAAAChCoobFM+MCwJJUTLd6FgPAFQo1B6riIAwA6E9RBIBdqfoeOrdnn6VPAAAAAEVI1ByouVUMAQAAmDfvodOQqAEAAAAoQqIGAGBBKu4fYAYWYHv688OlUAMAsCAG0SyBF1iWwDV1uCx9AgAAAChCogaALsx2AgDAbUnUAAAAABQhUQOMIv3A1Jw/AIDrZ3x+uLK1tvHg8dHJ5oNwIHRw81Xx3EU4fwAAwLWdnZ/mpmOWPgEAAAAUYekTiyf9MF/OHVOrmNKqep37rAC2U7HfjNB3QkUSNQAAAABF2KMGAAAAYI+utUeNpU8AC1Axbi1qDX3oDwCgNkufAAAAAIqQqAFYALPVwJr+AABqk6gBAAAAKEKihsWzVn++Kp67COcPAIBlMj6fhkINMFtVO9yKD6iqnxW3r+L1FOGagl4q9gn6g3EqnrsI52/OKl5TrqdpWPoEAAAAUES21jYePD462XwQAIBrMtsJAFzm7Pw0Nx2TqAEAAAAowh417FXFmcWKzHaOU/V6cv7mq+I15XoCAFgWhRr2ygsHU3I9MTXXFAAAvVn6BAAAAFCERM2BqhjfjzBbDcCyeO4BsCRV30PnRqIGAAAAoAhfz83iVaz6moEFOAyeMQDAZa719dyWPrF4BqwAAABUYekTAAAAQBESNQAAOyK1CX1YdgjMmUINAHAQvJgBa+49YM4sfQIAAAAoQqIGADgIZtABgEMgUQMAAABQhEQNAMCO2DcHqE4/BfUo1ACzVXFgEWFwAVyhPwCq009BPZY+AQAAABQhUQPMlhkgAADg0EjUAAAAABQhUQNAFxX3GJLSYmoVr/OK3HsAcIVCDXtVccBqcDiOc8fUnL950yeMU7FNQB8V+82Imv1Uxc+q4ufE4bL0CQAAAKAIiRr2SiUa4DDoz4E16YdxKrapKp8VSydRAwAAAFCERA2LZxZonIptAgD6M0YAmJZCDYtncAHArpgMANievpOpVbymrsXSJwAAAIAisrW28eDx0cnmgwAAAABs7ez8NDcdk6gBAAAAKEKhBgAAAKAImwkDo1TcgMumbgAAwKGRqAEAAAAoQqEGAAAAoAiFGgAAAIAi7FEDjGI/GAAAgN2TqAEAAAAoQqEGAAAAoAhLn4BRfD03AMD1qziWYjzjTvZJogYAAACgCIkaYJSKswhVZ6YqflYVVTx/zh0Au+IZM17FMQLsU7bWNh48PjrZfJDSqnZuHlAALEnF57FnMQD0d3Z+mpuOWfoEAAAAUISlTwfKbBkA9Od5DKxVTNhF6KegIokaAAAAgCIkagAmVnHGzGwZAPTlWQyMpVADMDEDMQAA4HpZ+gQAAABQhEINAAAAQBEKNQAAAABFKNQAAAAAFGEzYRbPN/QAAABQhUQNAAAAQBESNSye9AoAAABVKNQAAMAlLI8GoAdLnwAAAACKkKgBAIBLSK8A0INEDQAAAEAREjUAADtijxMAYFsKNQAT82IGrLn3AIBtWfoEAAAAUIREDcDEzKADAADXS6IGAAAAoAiJGgCABbGPFgDUplADALAgiiIAUJulTwAAAABFSNSweCLgAAAAVCFRAwAAAFCERA2LJ70CAHBYKiamqzIWhnoUagAmVnFwaBAGrOmjWALX1Hj6BKjH0icAAACAIrK1tvHg8dHJ5oPAophtYQlc5wAA7MPZ+WluOiZRAwAAAFCEPWqAUczqswSucwDoT8KVpVOoAQAAoIyKRRHFo3mreP6uxdInAAAAgCJsJsxeza2S2YvqOFBdxf68Yt9Z8XOqqOK5A4BdspkwAAAAwAzYo4a9MmMGsJ2qiQz9+Tg+J6ZWsU9wnTM11zlLZ+kTAAAAwB5Z+gQAAAAwA5Y+AUxMXBcAALheCjUAE1MUAQDmwgQT1GPpEwAAAEAREjUAdGEGD/pw7wEXuf+gHokaAAAAgCIkagDowgweAADclkINALA1y2fmy+fE1PQHANOy9AkAAACgCIkaAGBrFWerzepDH65zgGlJ1AAAAAAUIVEDABwEs/oA7IrUJvukUAMAALBQChDjVGwTh8vSJwAAAIAiJGoAAAAWSlIE6pGoAQAAAChCoQYAAACgCIUaAAAAgCIUagAAAACKsJkwAMCO+NpbAGBbEjUAAAAARUjUAKOYFQYAANg9hRpgFEURgO3pOwGAbVn6BAAAAFCERA0AAMCOVVxGHiH5N2cVrynX0zQkagAAAACKkKgBoAuzQACHQX8+TsU2MW+uqcOlUANAFwYXQHUKEONUbBPAnFn6BAAAAFCERA2LZ7YMALiM5/F8Gd8BcyZRAwAAAFCERA2LZ3YDAOCwGN8Bc6ZQA7AAIuDQh3sPANiWpU8AAAAARUjUAEzMDDqw5t6bN/05AD1I1AAAAAAUIVEDzFbFmc4Is50Ah0J/zhJUHE+591g6hRpgtjzEAQDumIrjKcUjls7SJwAAAIAiJGoAAAB2rGJKJKJmUqRim2CfFGoAAOASFV+svcCO49wBc2bpEwAAAEAREjUAEzOLB1Smj2IJXFPAnEnUAAAAABSRrbWNB4+PTjYfhANhZhEAgF2rOOaMMO6cs4rXlOtpvLPz09x0TKEGAAAAYI+uVaix9AkAAACgCJsJs1fiecCa/mDenD8AgN2QqAEAAAAowh41ABOTNAAAAK7lWnvUWPoEMDFFEQAA4HpZ+gQAAABQhEQNAF1YIgZwGPTn8+b8QT0SNQAAAABFSNQcqIqV8QjVceCKiv1Bxb6z4ucEcJF+CmBaCjUHygOTqXmBZWquKQAAuC1LnwAAAACKyNbaxoPHRyebD1JaxZnqCLPVAMB8VBxPGUsBbK9if35031/Ljcf22RAAAAAANpOoAQAAANijs/PTjYkamwkDAAAAd1jFJUYR81s2aukTAAAAQBESNQAAAMAdNrfkSlUSNQAAAABFSNQcKGsDAQAAYH4Uag6UgggAAHB7Kk7wepdh6Sx9AgAAAChCogYAYEHMngMXuf+gHokaAAAAgCIkalg8M4sALIlnDADUplDD4hmwAgAAUIWlTwAAAABFSNQcqIrLeSKkVwCA+ag4njKWAjh8EjUAAAAARUjUHKiqsy1mpgCAuTBGYAmMz6EehRr2SqcLAAB1GJ9DPZY+AQAAABQhUQNAF6LWAIdBfw4wLYUagIlVHLBWZBDNElTsD9x7TM01BTAtS58AAAAAipCoYfHMdjI1528c9x5L4JoCALYlUQMAAABQhEQNi2e2k6lJigAcBv05AD1ka23jweOjk80HAQAAANja2flpbjpm6RMAAABAEZY+sVcixAAAALCZRA0AAABAERI17JX0CgAAAGwmUQMAAABQhEINAAAAQBEKNQAAAABFKNQAAAAAFGEzYQCABbnxllNXluMAACAASURBVJt7N+E2fNkAAFyhUHOgKg7CIgzEgNoq9p36TabmmoI+Kj5jIvQJUJGlTwAAAABFSNQcKJVx6KfqjFk1Ffupim0CgF2qOG7xPGbpJGoAAAAAipCoAUYx2zJvPisA6MuzGBhLoQYYxeBiPJ8VAABwvSx9AgAAAChCouZAVVymEiFpAFxRsZ/SRwEA0JtEDQAAAEAREjUHyqwwUJ1+CgAAbkuiBgAAAKAIhRoAAACAIhRqAAAAAIpQqAEAAAAowmbCAAAAO3bjLTf3bsKlbO4P9SjUAAAA7JiCCDCWpU8AAAAARUjUACxAxbi1mUWAw+AZAzAthRqABTBgBdieAsQ4FdsEMGeWPgEAAAAUIVEDMDEzsPPl3LEErvPxqraL2+c6B+ZMogYAAACgCIkagImZMRun4mwnLIE+CgBqU6gBoAsviwDsimcMMGeWPgEAAAAUoVADAAAAUIRCDQAAAEARCjUAAAAARdhMGABWKn4TlQ0xAbanPwfmTKIGAAAAoAiJGgC6MNsJwK7oz4E5k6gBAAAAKEKiBoAuzHYCAMBtKdQA0IWlT0zNNQV9uPcApmXpEwAAAEAR2VrbePD46GTzQQAAmEDFREaEVAYAu3N2fpqbjknUAAAAABRhjxqABag4W22mmqm5zufL5wQAV0jUAAAAABRxzT1qJv1DmU9prX3HXv7YSNo0TsU2RdRslzaNo03jVWyXNo2jTeNVbJc2jaNN41VslzaNo03jVWyXNo2jTePto137TNQ8ZY9/ayxtGqdimyJqtkubxtGm8Sq2S5vG0abxKrZLm8bRpvEqtkubxtGm8Sq2S5vG0abxdt4uS58AAAAAilCoAQAAAChin4WacmvLQpvGqtimiJrt0qZxtGm8iu3SpnG0abyK7dKmcbRpvIrt0qZxtGm8iu3SpnG0abydt2tvmwkDAAAAcG2WPgEAAAAUoVADAAAAUIRCDQAAAEARCjWFZObde7fhMpl5lJn36N2OiIjMvGtmPrh3OzbJzHtm5sN7t4PxMvPNM/PhmfmwzHzz3u1Zy8z7ZuYTM/MJmXnf3u2BJXDfcchW47lf7N2Oq63a9Zje7bhaZn7OmN/BWJl5MuZ3ELGDQk1m3uta/0z9966jfV+RmXe+8PM9MvOGzm16TGb+ckT8yurnR2Tmt3Ru0w+sPpu7R8QvR8SrM/PzO7fpCRHxioj4H6ufH5mZz+3ZplU7blp9VveKiJsj4obM/I+d23RDZn7P1f90btMLMvOnrv6nc5s+NCL+d0R8U0R8c0T8Rmb+k55tiojIzH8RET8XEU+KiI+MiBdn5qf0bVVEZt4pM98+M++3/qdjWx6bmWeZ+WuZ+ZrMfG1mvqZXe1ZtepfMfP76RWhVAPySnm1ateNumfmlmfmdq58flJkf1rlNn7PqNzMzvzszfyEzP7Bzm8rdd5l5l8x8amY+JzOfnZmfm5l36dieH83M5276p1e7Vm07ycy3XP37l6w+s/fo3KaXZuZnZeY9e7ZjrbV2HhE39+y7L7Nq19f3bsclPumS333yvhtxUWZ+X2a+9YWf79lrfJeZz8zMb9r0T482XWhbyedxRDxt5O/2puh46oGZ+XdW//5+mfnZF6/7ju1638x88urf75OZ99/p35v6W58y87UR0SIiLzncWmsPmPQPbikzvzoiPjAinhwR942IZ0bEM1tr39yxTS+JYVD43Nbau69+94uttYd2bNMrWmuPzMyPi4j3jIgviIiXtda6pUUy82UR8fiIuOnC5/TKnm1ateHlrbV3Xw3y37G19vTe7crMj7jw410i4sMj4pbW2md3alJk5nte+PEuEfEREfGG1tq/6dSkyMxfjYgPa639xurnB0bEj7fW3rVXm1bteHVEPKa19n9WP79NRLyotdYtTZaZ/yoinh4RfxgR56tft17X+ercfW5EvCwi3rj+/foz69Smn46Iz4+Ib6/Sl6/a8KwYPqdPbK09NDPvGhH/q7X2yI5turm19ojM/KCI+KyI+NKIuKG11u3Fuuh990MR8fqI+C+rX31MRNyztdZlBjYz/9HqX58UwxjqYrt+s7X2RT3aFXFlPJCZ7xsRXx0RXxcRX9Rae1THNr1zDOPNj46Il0bEDRHxvNbxK1dXEyTvFUNR8s/Xv2+tPbFXmyIiMvPLI+KVEfGcnp/Pqi0fExEfGxHvGxH/88Kht4yIN7bWPqBLw+LKmPP2frentlxWyHqT1tr37astV6v2PM5hEvBDIuKjIuJZFw7dIyIe0lp77x7tiig7nnpFRPyDiHiniLgxIp4bEQ9urX1IxzY9fdWmB7fW3iUz3z4iTltrj93V37zz7f8n22mt7bSydEe11p6Wmc+PiJdExJ9ExOPWL2k9tdZ+J/NWta03bvpv9+TNMvPNIuKfRcQ3t9b+NjN7f5f7G1prr7vqc6rgzpn5djF0vl/cuzEREa21Z1/8OTN/MCJ+slNzIiKitfayq371s6sHaU9/dNX9/5qI+KNejbngd2N4OVt7fUT8Tqe2rH1ODA+nbg/uq7yutfbfezfiKndrrf3cVX3UG3o15oIHttY+evXyEa21v8z+Hen6739IDAWamwu0qeJ99+DW2iMu/PyCzLy5V2Naaz8dMaSTW2uPu3DoRzPzZzo1a209bvrQiPjW1tqPZOaXdWxPrJ4vX5yZXxoRHxYR3xMR56sExDe21v5vh2Z9eYe/OcZTI+LuEfHGzPzLGPqI1lrrsfT+RRHx+xFx77h10uf1MRSTejrKzHu21v4kYljJEDt4nxvj6kJMZt69tfbnm/77Pav2PL4lhmLtE2MoiKy9PoYiSU8Vx1PnrbU3ZOaHR8Q3tNaemZkv79ymD4+Id4+IX4iIaK3dsk5x7spOb+zMfGJErB/kN7XWfmyXf2+MzHxcRHxjRDwjIh4WEd+cmZ/SWrulY7N+J4e1uS2HPTI+O1bLoDr69oj4zRiW8vxMZv69iPizri2K+MXM/NiIuFNmPiiGz+lFndsUMVxLN0bEC1trP5+ZD4iIX+/cpqs9KCK6Rp3z1ksfj2JIanXZAyIzn7T611/KzJ+IiB+KIQl4EhE/36NNq3Y9dfWvvxcRL8nMH1m165/GMPvZ0+9ExOs6t+GiF2Tm10bEcyLir9e/bK39Qr8mxR+vUlktIiIzPzKGwX5vf7NK0azb9cC48Jl18rLMfF5E3D8inrYa7Jzfzv9m1y6979b3ZWutx5LWl2fmo1trL46IyMxHRcTPdmjH1e6TmQ9orb0mImIV/75P5zb9XmZ+e0R8QET8+1VsvvtejDnsW/fkGIqSz46I748hqfFTEbH3VNu62FZNa22nLzzbaK39VkT8VkS8z2r8+6DW2k+u+tG7xq0Luvv29RHxosz84dXPJxHxlR3bE5n5PhHx3RHxFhFxv8x8RER8emvtMzs2q9TzuLV2cwzLDr+/tVZhAifyytLQiuOpv11NLn1SRDxh9bs369ieiIi/aa21dXAh97C37ORLn970/zjza2KIVn7/6lcfExEvba31Xof3cxHxya21X179/KSI+KqeSx0y894xFI8+IIYZhOdFxOcUmrmOiIjMvHPPziUz7xZDYmW9j8GNEfHvWmt/1atNVWXm6+PKEsQWEX8QEU+7Ommz5zZdXBb5hoh4bUQ8o7X2wg5tuda+VK211mVfilWscqPWWreZ0Mz87oh4cET8eNz6Qd5lP6bMfMElv26ttcfvvTErqyLtd0TEY2JIbL42Ij6+tfabvdq0atdxRHxJRDwkhufLY2N4Dt7UsU1HMbykvqa19qerZUbv0FrrNltd8f7LzF+J4b777dWv7hfDRM559F16+MExXOvrfQzeKYYXsxt7tGfVprtFxAdHxKtaa7++Sro+rLX2vI5tellE/GkML7HPbq399YVjz2mtPWnj/3h3bXp0DMv+3y0i3jwi7hQRf94puXKxXRkRHxcR92+tfUVmvmNEvF1rrdskRWZ+WkQ8JSLu1Vp74GqS8Ntaa/+4V5tW7XpIDFsBZEQ8f/1O07E9FbdwqPo8Xo+Fb6V12Bpkwzhqrfd46iER8RkxLNP+wdVkwEe31r6mY5s+L4aJ7+MYltd+SkT8QGvtmTv7mzss1LwyIh7Zhg3CIjPvFBEv7zWouNCuO7XW3njV796mWlGktxx2tb8hhlmD74oh6vWFnQc8/zCG/QLeeOF379G54rt+6b+s0+2++SvztkoZtNba/yvQlktfYnsWj6pazbIctdZ6zrreyqoQ8ugYBvYvbq39cef2rF/KHtBae0YOm5vet+dLWUWr2fyNVjP/XawSK+tJrl+9WIToJYf9aR7UWrshM+8TEW/RWnttp7YcxTBu+qoef3+TzHxpRPzziDiNYb+FT4zhM+u2v9CqXd8aQwHy8a21d8thA+bntdbeq2ObXhER7x0RL7lQgHhVa+1hHdpyj9ban+WGL2bptIwuIoZCTWvtUXlhr5xc7UPWq00X2lbqebx6Fq/dJYZE1L1aa/+2U5PiYjryWr/bt1WC7X6ttVf3bMdFq4mvD4xhLHVja+1sl39v12sa3zoi1h3HW+34b41178z8qhhm7j54VbFbR/a6yMt3Rn9dDAmkH9l3e1Y+pbX2jTls9HifGGK7N8QwG9vLjRHx85n5Ua21P1z97rsiouu3OkTExSV9b9q4t0dD8na+4aJHUevCMqNLtdaes6+2XC0z/24MM4uPjaHY9sIY0my/26tNq3Y9NCL+c0Tca/XzH8ewEewv9WrTuiBTrHj0oRHx92O47yIiorX2jI7teesYXnreKYa9q9Zt6raJ9wXvEMPM+Z0j4nGZ2fXei4hvidVLWQzLR18fw7KQni9lL4jLi+7dZhXjkvZERLTWfvuy3+9aZj6+tfZTl/TrD+x9TeWFjR5jGK+8WQybHe9so8draa2dr5JHpQo1EcPeORcmLm/IzArLyB/VWnuPXO1D0Vr7kxy2A+jpr1trf7Puy3P41the+zX+QAz7HL3sqjask9M9v6yl3BYOVZ/HlwQDviEzXxgR3Qo1EfHDcdt3qdMYtijoIodv+v26GFJ/98/MR8aQwu+y6fkqcHJjGzYS32lx5qJdFmq+Ooa11S+IoRN5XHT++rGV743hAb7e9PXXYth9u1uhJoaXjHeN4aaIGL4N55ci4lMz8/1ba/+6Q5sqbvT46oj42oi4KTM/tbX2oohLv11sr65eTpR9N+5db3p3lxgGrDfH8Bk9PIYNtN+3Q5vWa0vfNoYI6vorud8/Im6KYU1sLzfEMPhZf4PKx69+d9ytRYPviIinttZeEBGRme8XEd8Zw+fXRbXiUWZ+W0TcLYbr6LtiiF33TmP8RES8OCJeFf33W3mTHDYufXgMz5U3fWNX9L33Kr6Ufd6Ff3/TN9N1asvaj8eVJaN3iWFPn1fHUKDs4R/F0Ic/4ZJjva+pvW/0OMLZKi7/rLj1Nyx1Sz9ExF+s7rVXZOZ/iGHfjp3vtTDC365ehtb7P9wn+vejP52ZXxQRd13NpH9mRPxoj4a01j5s9X8rfmnLZ8SwhcM7xLAp+/Ni+Da/nqo+jy8WRI5iGKt36acy811jeJa81VXF93vEhQmwTr4shjTbTRERrbVX5I6/CvtaWmtvzMy/yMy3aq3tbb/GnRVqVuvJbophdiwj4gtaa3+wq7+3hXu31n4oM58WEdGGHaV7f8PSO8cQ9XxDxJvin8+L4WXxVZ3aVHGjx9Za+7EcvkL1WauXj97fRHWZbhv3ttbePyIiM/9rRDyltfaq1c8PjVu/hOyzTU9eteHHYvgKwt9f/fx2EfGferTpgvu01i7uV/O9mdmjMHq1u6+LNBERrbWbcg+blt2OasWjx7Thq3hf2Vr78sz8+uj7khgRcZfW2lNv/z/bu0e31h7SuxFXKfdS1gp+M93VSyxWg/xP79ScaK09ffV/n9yrDdew940eR1gvgb740to7/fAJMaTr/mUM3zbzjjEUJXv7poj4bxHxtpn5lTEU37+kb5PiCyPiU2MYi396DC//39WzQZn5/Kv3yLnsd3tsz50i4hNaax/X4+9fQ9Xn8cVvEXtDDF/c8lF9mhIPjiGl9dZx6+L76yPi07q06IrLvum39zvfX0XEqzL/f3v3HWZpXZ5x/HsDKkXAgklEXUQiGAsgoliwdxAiiYgINowaTATUKxoLApYYEQtqRCwhiCLFRlNQkRIISOigYlSiRpQYE5RFUHC588fvPTtnZmd2V2XO8xvm/lzXXjtzDnudhynved/nfYq+yvTE+7xVac1369PDmdr6dCtFWegZfjX0B47eyB9J/SaTe9HuZoziWA/YeMjeVfV8v5SpQY83Dl+z6hMzAbgNCXwsrfKhdOYRTBvcO3It8PqicEYeMErSANi+cigbrHTfUZJm8N/A5lXBDH4uaU/gM8PnuwM9zKu6Wm2d61HD53vSBuFV6i15dNPw942SNqZ936rvNB6lNnzyZKYPXK68ew5wnqQHunjg5AzdXZSpo810c7F9saSy9rBxvbUeAsepbX26y/B7uBctmVymx+qHsblGN9HRqm7bn1Ybvvxk2vnes22Xts8MczY/RvHPEYCktWlVpBupze8ZXcFuAGxcFddwrfLnwPuqYphDl+/HoxuqPRjGa5wg6VG2z6uOZ4YeN/2eMvyZmHlL1GjFrU/7SHq0i7c+Aa8BTqT1U59Lm7/ynNqQOJhWgnomU21i/zBcBFW10Ji2IeRZtPkB61FcBjcaUDZ8/CvguWoDKEu5o5WSY74t6eO0/nzTLvSrV76fKek0WlLEtGGGK5s4Pwl7AR+inWCY9ibQwxDovWgn0J+nHRPOpj5R2lvy6OShB/3dtFYHU3ynE7iZFs+bmEreVt89BziSlqy5lnbCKgo3BkGfF2VMzX4Y30z30sqANKwGH4ySR/9TFM5yPbYe2j5kaE+5nnan+C3zPehxVdQ2Ub2GNhDz5cMFxxa2T17FP52PWK5gJXekK48HY75L+/6tBSBpSdU8puH1H0NrwdhkiGl07Kw4pr8C2I+WlLmIqUTN9dRXJ58r6UOs2OJXueyjy/djSRsCBzBVyHAWbfbKxIsGJH2QqcKF3Wc+XzzP51W0791vaCMKTgPeXhgPto8c2kZHN5m/Y/uW+XzNxbj1aVfaN3tU6rkdsH/xwYThjvALgKtoSZEf2z67MJ5upu9Lep3tgzX70OXqA0l3ZajD668N7M3UG8HZwGEuXmUuaRfGYrL9hcp4YvUNx4CDaHOORDu5OMj2daWBsXz7zNoVJzoz4vg+bfZK6UalmSR9j3axOK1X30Ubg9S24VzuwtWtC4Wmb1sblcl/roNj+eVjrYdbSroz8HnbT6uMqzeSjqVdVL/Q9oPVtpicZ3viFa7qeIMYgKRX0S5g/xtYRgcJZUlX0drDLhpiAmYdCDvJmF7leVwH/PvQ7Gue7dr1zr2+H38OuJJ2AwXatd9Wtle6eGOeYnnRyp63feTKnl9shpb/I2nvw6LlEl40n9fri3Hr0/62jx8uOp5C6xU8jJawKSHpr4B9gXsDl9JWqJ5H24ZRpadBj6O7rDPnB5TqtQwVwPavhzueX3JHa+1olQ9LbX9N0rqS1nfBysSx5N/yuwljTDtufcr29ycc1/tt7yfppJXEdbjt8ycZF7RjAK30dJR4X8/29ZOOQ3NvnUH1m4y+CdxY+Ppz+ZHtE6uDGHHbhnNZ9d3ymYYbOafaXirpzbQtGG+vvJHjYdvaEN8atHXTpUmaQTeth5LOsb39LG3Iowv9DSriGmxme7fR3WrbN0k1ixmqEzGrYV9atVEP7ccjv7T95eogxtn+oNqGpfsydh1n+5OFMXXTzjOm1/fjzWyPz4Q6SG0N/MT1nIgZ5sDsavsXw+d3BY6x/fTCsN4DPG10XSVpc1qXwLxtx1qMW59GGfEdgY/YPkHSgYXxQHtzejhwvu0nqk3hru4b7mbQo+2Thr+XH1DGTlgnfqE4ptsyVEk700o+u1hrN8T0MuDltK1Bm9FmM32E1vowaaPk34VzPH93WtvRVpMJZ7lRW9Ehczy/EfDPtLbEiZJ0NG2zwzLaz/uGkt5r+90TDqXnrTPLaG2sZzC9J756PfdVw/fvJKbHVfm1uifwTUkXML1UvuwYxdSNnO2Bp9N+D6tv5PTyezfTqPXwYKZuopS0Htrefvi7xzbkm4cqmtG51GaM/Q5WmJHQuiNtjfmvihNaAP9F/czImc6Q9G7a+8r4sbMseSvpKNo51KVMXdMYKEvUaMYq7NHjxe99vb4f3yRpe9vnwPL2uptW8W/m1XCN93raueX4zLHKgoGNRkmaIZbrJP1RYTwAdxi/+W37PyTdYT5fcDFufbpGbdjcU4B3DSXzaxTH9OuhAgJJd7J9laQtimPqcdBjVyestg8FDu2xDJVWPjxzrd19C+OBtvXiEbQ14aOh0CUH3dmSfzNJ+tVcz82j/wGwPeemGUk3Ty6caR5o+3pJe9A2X7ye9ns40d8/97115ovDn96sQztRHW9LqU5qVd+MmM34jZzDOrmR08Xv3SwOobXXPpZWAfyvtKRWGUlH2X7Bqh6bsAOAU4H7SPo08BjgxYXxrJDQkvRs2ntzCU3NYbqaNsvuFKZfWL+3JLBmlKTdduwxU1vxvi3tuFC9AWdcj6uwe30/3hs4cphVI1ql9EpbkCbg07T5QjvSrrNeRP0stFvHq26H1s3qn/kLJX2CqZuqezDP3R7z3fq0BvDz4XU2l7R55dyVwXOBZwCH2P6F2orgvyuO6cdDNvqLwFclXQf8pDIg9znoscsT1qEM9cGsmIkuu7vB7Gvtqv3G9s2jmCStRdFBV9JK20Bs72z78EnFM+aLtHYLJH1uRnksMJVkKnCH4c7Bs4EP2b5FwxrcSdL04aorqDyp77WMuMek1sqSkYV6vJHTxe/dLI6krXAdzY7bnXZHv2rNLLQNVMsN7zHzVpK+Omx/VdLFtJZ2Afv2NjPD9hcl/X1hCKPE0Y+GP3cc/pQaKrcPs31cdSwzXEnbRvfTVf2HE9TdKuyO348vBbaStMHweWVnwMjdbX9C0r7De/NZkqrfo98EnDMWx+NoVfmV9qbddN6HqUUfH57PF5zPrU/vAnaj9QiOsqum/U+VsX0jY3cR3dYFlx7sbO8yfHjgUKK3Ie0OTAlNH/R4VVUcs+jyhFVt2OMTaImaLwHPBM6hsAyVPtfanSXpjcA6aps5XklrxajwKFqZ9WdoFT69ZLTG46jeFDTT4bQBapcBZw93NypOMEYn9VvQKjZHSbedKHp/kXSc7edqjq0qLhqGuYpZTKUl4J22X/R4I6eX37uZtrA93hp6hqTLKgKR9AZg9N4y+tqItvXloxUxjQxtDZfaPkXSnsAbJR1aOS9G0+d7rUGr0Cg7lxqfw9STYZbW3wK9JWo2Ar41tI2OVx5Vto12swq71/fjEc3Y+jQkIkq2Po0ZbS76qaQdacUC9y6MB9unStqGqST3qztIcq8FHDq6ITiMCLnTfL7gfG59+g6wpe3SXtz4/Qwlum/obNDjPrQqmsto5XlLaANfH1sc1xW0WSaX2N5K0h8DH7c92wyNScW0Li0b/TTaAe404G2VQyiHBOBLZ8T08Yry3eHg+lTaXeAtgVOAz9j+5qRjmRHXxba3mflxryStZfu3Ra/9FeAvPQyjlrQ+cLztZxTEck/bP9UcW1WqLsok7WT7JM2x2aGnO46j9gvbbyx47but7PmKi42Vqfy9G4vhX2hz/s4fPt+Otv3ilYUxvdN2D7MQl1PbgLoV7X3mk7QZY39h+/GFMR0x9ulok9jHbP+sJqJGsw/R/yVtltzhFecvkvanzQ+ZuXa67JggadafncoqRUl/A7wD+AVjq7BdsMa81/fjEXW09WkspmfR2lfvA3yQthTlIBcvIZB0L2ATps89qtyIfD7wFNs3DJ/fmbYR+dHz9przmKj5Mm1a8w3z8gIxryR9nXa3uqdBjyvo5IT1AtuPGFrFnkgrB7/S9oNW8U8XBXW22WWmob1hd1oL3Vsr5w1JWkb7fRNtrshoY0EP20sY7rQ8iOktfm8tiuUq2snNb4bP7wRcZvsBFfEMMbzL9utX9dikSdrV9vGreqyapPNtP7Lgdf+TdnExW2VdycXGiPoc0omkb9Oq2kbH9iW0Ie23MuGVypIe4Dbbb9bEtmsHv17stkHzLcA1Q3tB90n4CpIOBe5Bq3SFVpV/Le29cIOKWUPDsWGm0mNCj9ThKuyO348vtb31qh5b7ObqzKm8Dq343t3mrU9jJdY30qZtn05f07Zj9XRXijrXCSvDyuBCFw6xfYw2M+cGWoJr4lZn9sqkYhmzyrkrFYYL+x1pSZr70mYtVA5Xxfaala+/Mmrr3telJSM/ThswXvJzPjgKuEDSF2jvObswdYeqylNpVX/jnjnLY5P2BmBmUma2xyamp/YL2yVrpVdTj0M6obWI9eI1tNkF75nluerBr0uH1qw9gccN1ZzzuiVkVdQ2vLyMFZN/e1XFNHio7ceNfX6SpLNtP05SSbVrj8cGSY+kVT38Ga1tdE3q20Z7XIXd6/txj1ufNqcNg/9j2w+WtCWws+23F4b1bFqLbU+dOb+StM0o+S9pW+b5ezcfM2pG624vYmp2QCwwlSWUK9HlCetYqfdHJJ1Ku/NzeVE4Pc5e6W7uiqQjgQcDX6aVd15ZHNJC8GjbW0q63PZBkt5DYWLL9juGys1R6+NLbF9SEYukvWkzl+43tDqMrA+cWxETgKRnAjsA95L0gbGnNqC1PFQabw0dtV/8eU0oUyTtzDA7ADjT9smV8dDhkE6obx8YZ/vlw99PrI5lFrsBzwdeavtaSUuo39h1Aq3N4WtMbTrrwT00fcvLEto8FmjzhiZuaCN/DbDE9svVZv5tUXxc+BDwPFqifVvaDcz7F8YDHa3CXsX7cfWsRmhblT45zKoBuI76rU8fo81jOxzAuMvN+wAAF9RJREFU9uVqm3YrEzVX05LaPSVq9gWOl/QT2k2AjWnH+HlzmydqRj3vktajrZ1eNnw+7wN34g+n6QMeV1Ccse/yhFXS6bafDGD7BzMfm7A/YWr2yvPpY/aK5/i40gtoLUabA/toajtWFy1GnRrdNbhR0sbA/wIldxs1feB5WVvDmKNpSb93AuPbU5YWzzf5Ce3myc5MXyG5FHh1SUQDd7iJStI/0lp+Pz08tK+kxxTPPelmSGfv1IYZHwMcZ/v7HcSzJm2O3lNGjw1JiMpFAwDrVrd/zOG1tC0v36e9F28KvHK4nqiqljyCduwczaD4MS1BUprAtf09SWsO11hHSKpOQPS0CrvX9+ORJ9N+nu88fH4D8HBJa7hthKqwru0LNH1TbPXNnB47czYFHkpr9d2FNuh4Xq9r5nM99+m0FZejGTXrAF9h6mAXHbK9PoCkt9J6g4+ivWHuwdS2lSpdnbBKWpvWCrKRpLsyVTmyAS3LOnHDm/apwKljs1fOlFQ5e2UrtU0cYsWtHCVJEdvVK3cXopOHFr9305IjprVATZzbNo7LOpp/tCZtE8/fzHxC0t2qjlG2LwMuk/R/wCm2yysRNccGqpHik7AdgK1HX6eh8u4SWptYlZtpv3NvYmxIJ51UJ3ZmZ9rdzeMk3UobAHtc1THC9jJJN0ra0LUbXWY6WdIOtr9UHcg4218aKlYeQDs/uMpTA4TfXxTWZrZ3k7T7EONNmnE1W+BGSXekXcQeTNtcu15lQO5oMP3wu/ZLYHdJ2wP3t32EpI0kbWp7trlDk7Tt8OdE2s/584F/B/5a0vG2Dy6I6eeSNmN4j5H0HOrXv59If505+9s+fjgXfiqt3fYwYLv5esH5HCacYUkLmKRv2N5uVY9NOKZupsoP8ewL7EdLylzDVKLmetoGhQ8VxTVz9sqJwD/bvqYinrj9GX7G1q68+FBHA8/HhtHCiu2G5YMnJX2K1hb5OeAI298ujGWlJd6VJ/xDmfwTRok1tW1QZ05yMO4sMXU3pHMhGC749wf2qJz9Jek42l3XrzL9OFWWkBwqp9ej3fC6heJKUklPsv31GXOrlrNd1mI7VKo8GTh3GAq9Ga1K+RGFMW0C/IzWFvJqYEPgw7a/VxjT/WkVLA9k+rKBykHsB9ASIlvY3nyoBD7e9mOqYhriOo22sXJ8c9BnaRUaF9l+YEFM9wM+SiumuA74T9qxs3pD1jq0tsPvVMYxIukS2w+V9E7gCttHjx6br9ecz4qaiQ/cidvUMkl70MqITbvor+5lfg3wp72csNo+FDhU0qsKq1WmyeyVmC9Dr/5raW+aL5O0RNJjC3v1uxl43uPAyXG295S0Ae04foQk00r6P+NhvfkEY+nmzuss3glcMsxZEG1WTfW65x6HdHZL0n2B59Iqa5YBr6uMh9Z+fEpxDNOMKqc78njg60zNrRpPepvaIf8H0qqU7yPp08BjgNK2zbGL55vo533wCOAA4H20hQMvoX5G4i60NpWLAWz/RFIPP/tLmD5z6RZgk6Faq2oeyzW07+EZwN1oN5xfBJRs9QSQtBNwCG1g9qaStqZtZq3cPnyNpMNpHUPvGm5azmuF/nxW1GxLKzudNnDH9kUr/YfRheFk51Dam5JpAzH3G81gKYrpROB5trs6aZW0K3Cq7aWS3kzbcPR2F6wEHcq9R3ftxn+5M3sl/iCSjqX16r/QbSvAOsB5qZKcMpTE7wFsavttwzDMP7FduR1rOUkb0bbP7EdbpfynwAcmmWiW9H7b+0k6iVlaoIpPwpB0T1qlloBv2L62OJ4vAA+inUD30qffJUnfoFUZHA8ca/vq4pC6NbRr35/p1Q9n10W0vJ38L5m+jcq2yy4WASTdnVYVJeD86puFkp4FvA3YhPZ1Kj+/k3SR7YdJusL2Q4bH/tX2Y1f1b+cxpgtsP0LSxUM11Hq0c5ayCskhrv1pSaQThod2olW+vwf4qO09CmI6ldatcDFjN+Vtz7ZJb1IxXUTb2HfmqGJl/OerKKZ1aRsPr7D93eF84SG2vzJfrzmfFTUTH7gTt50hIVO+gWOGbqbKzzDqWdweeDotAzyvPYtzyeyVmEdd9eqrzxWlH6ZtpHsS7UR6Ka3d6OGFMY3uTO0FbEabO/YI2z8bTjq+Tfs6TspRw9+HTPA1fxcPZ2rr063ASYWxQF9DOnv3IttXVQcxbkZb5HLFLSF/Rdtecm/gUtr5+XnUrjGH9nM+ulgczaYpvW7Q1GKIU2Z5rMr7gb+gXSz2cl31a7Uh/9+V9Le0Co0/Ko7puKH64S5q8y33om03KjXcxPkSsD0tyfbXtkcbkyeepBnc2/Yzil57Lr+1/csZp5mlP+9DocDnxz7/KfM8y2c+EzUTH7gTf7jOBz32esI6yj7vCBxm+wRJBxbGEzEfbh6qaEbD5jajdm1ijytKtxvu3F0CYPu6YehjtV2B9828Y277Rkl7TTKQUVWt7bMm+bqrQytufdpH0qNduPWp81axLkja0/angB0k7TDzedvvLQhrZNuxj9em/S7erSiWkX1pP+fn236ipAfQRwtNNxeL6nBZxJj/Aq7sIUkj6SjbL6BVh6wL7EO7SfEkildO2z5E0lNpbTxbAG+x/dXKmEaG98GeOkz+TdJDbF9RHciYKyU9H1hzmIG0D32sV5+o+UzUjF+8fiQXrwvGKKv7GNpQsGOHz3el+KDS8QnrxHsWIyZpqJz5CCv26r+4Mi73t6L0FrWVvKNk1j1oVRmlbL9wJc+dPslYRnocPkmHW586/Tr1ZrTxZrb5E9V3YP93xkPvl3QO8JaKeAa/tv1rSUi6k+2rJG1RGM9ITxeLr2BqWcRFTF8W8U9VQQ1eB3xJ0llMry6vSEg+bBhuvAetWuVG2iy7XvwHrS3sa5LWlbT+pOey9UzSFbRj5FrASyRdTfuZGrXTVbaJvYq27fA3wGeA02hJwEVlPhM1uXhdgEbJEEkvBp5o+5bh84/Q1quX6fiE9bm0nsVDbP9i6Fn8u+KYIm4ztq225expTPXq71vcq9/dilLgA8AXgD+S9A7gOcCbq4JR2+6ysgrJyjaxHodPAtwFGK1T37AykEGvX6du2D58+PBrts8df05S9YaXbcY+XYNWYVM90PTHQ7X7F4GvSrqONk+yRI8Xiz0uixjzDuAG2nlwdcXm6AbO/ZhKaHns78oWv5cBL6dVsG0G3IsWb2XbWm+eVR3AXIY2ozcBbxpugK1n+9er+Ge3O/M5THjiA3fitiPpO8CjPLWm9K60Mtmyuy7DXajRCetODCestg8oimdt4K9pAzmvAD5h+7cVsUTMN0n/BPyL7X+vjgX6XFE6xPUA2omggNNduAp7RNJbgWtp82FGA4/Xt31wYUw9Dp/cHfhH2uDe5VufbB9TGFN3X6dejYaGruqxCcd0BlPJ0t8CP6Dd1PmPqpjGSXo87dh5qu2bV/Xfz1MMm6zsedevCH400wccY/uThfFcaHvbVf+XkyPpMNt7V8cxTtKlwCNoQ+G7GEYbq0/S0bRrrGW0JOCGwHttv7s0sAmbt0RNLGySXkJbS3jG8NDjgQMr2496O2FV24JzC/CvwDOBH9retyKWiPkm6VvA5sAPaZvFeiiN7YqkQ2nbZqpbsKaR9A3b263qsQnHdC7wWOCztNW81wD/WHkzYIirt61PXX6deiLpUcCjaa0q7xt7agNgF9tblQRGf5uMhoGvl9t+cMXrLzSSjqJVY1zK1EgHV85rHGZpfT03vldu9B4n6RLbD5W0FnBxzlkWBkmX2t5a0h7Aw4DXAxcttu/ffLY+xQJm+whJpwEvoG0FOZXC0thBb1PlHziWMPoE0MUK3oh58szqAMapw20qtG0lb5a0Oa0F6tixbQ6Vlg0nO8fQvma7M7aCc5J6Hj45WAP4Oe38aHNJm88cwjwJC+Dr1JM7Anemfc/G24qup7UfVpptk1EZ27dKukzSEts/qo5nAdiWdq7X013tvwFeJ+lm2s1CKF7P3amzJL0RWGcYKvxK6rf4xeq7g6Q7AM8GPmT7Fkk9/R5ORCpqYlaaY32j7YmvbxydsEp6HW397V1oJ6wbAgfbPn/SMQ1xTSupri6xjlhMJN197NPl21RsVw7pBEDS3Wh30Z8HLLFduo1K0n2BQ2kDoA2cC+xn+wcFsXyLlvQ7EXgCM+atjNptK0h6F7Ab8E2mhkDb9s4FsXT7deqVpE2q22RmknRlb9Urkr5Oqxq7gFYdCUDFz3nvJB0P7OO2hjcWkOHG7ktps/VEG0b78c6SbjEHSfvQqmguoy0mWgJ8arG1/SZRE7MahruN1jduPcxdOMj2bgWxdHnCKmkZUyc5AtahTbwftYTk7kbEBEk6x/b2HcTxCNoF/7OBb9neqTikbgwnX3vThkxew4zhk5UVUcNsti1tV66dH8XS7depV5K+Cuxq+xfD53cFjrH99MKYPgp8sJNNRsDyuTQrsH3WpGPp3TBjaGtaUmt8w1JpUkvSzrQZWgBn2j65Mp7eDMNnj7S9Z3UscduRtNZimwWa1qeYS0/rG7ucKm97zYrXjYg+t6kMFRl/AXwfOA542+iisZKkI5i9TWyvScdi+wPAB3ocPglcTRtOXZ6o6fzr1KuNxn/fbF8nqbI9GmB74MVDq2YXa2+TkPmdHFgdwEzDjJqHA58eHtpX0va2/74wrK7YXibpHpLuWDUkO/4wQ9X0AbRjqIFzgLcC/1sZ16QlURNz6WZ9Y05YI2IW72HFbSq7lkXT/CdtW17l2vLZjN9tXRvYheKZYz0dyyV9kPazdCNt3fvpTL97XjY4tKev0wJw6/jslaHlr7psvKvZXgCSlrLi1+WXwIXAa21fPfmo+tRpUmsHYGvbtwJIOhK4BEiiZrofAOdKOpHpLX7vLYsofhfHAGfT2sihbas8FnhKWUQF0voUq9TD+saIiHGSXstUZR3Dx7+kbQW4tDCuuwL3pyVEWmAFw2hXZujd/1rFzLEeSVrpcN7KbYex+iQ9A/goMLq4fhzwctun1UXVH0kH0RK1R9OOn88D/gT4DrC37SfURdeHURvtLEmt8tZ2SZcDTxi1/Q8z0c5cbNtw5jI21/IXTN8CB4DtgwrCit/RaNPvjMe6W00/35KoiYiIBUfS0bR2pxNpJ887Av8OPAA43vbBBTF1M4R9ZYY21lNs/2l1LD2RtB6t7XfZ8PmawJ1s31gbWayuodXp5bTfv7WBn/WWKK02Wls847HzbT9S0mWV68xj5SSJto31bcAZtPe+xwFvsH1MZWy9GJtreRJtruU0GcS+MEg6hFbld9zw0HOAB9k+oC6qyUuiJiIiFhxJpwF/afuG4fM7A5+ltfVcZPuBBTF1M4R9Rlwz7wpfSzux/1xRSF2SdD7wlBk/U1+x/ejayGJ1LJREaTVJ59EqDT47PPQc4DVDouZS21vXRRerIuki4Fm09xoB37B9bW1U/RgbxL4p01t8M4h9ARg7XxGwHrBseGpN4IbFtqglM2oiImIhWgKMt2LeAmxi+yZJVcNgexrCvpzt0iHLC8jaoyQNgO0bJK1bGVD8TvZlKlH6xFGitDimHu0BHAp8mHZBdD6wp6R1gL+tDCxWy/nAvW2fWB1IjzLXcmEbP18Z2vqmtZIvNknURETEQnQ0cL6kE4bPdwI+M7SvfKsopm6GsI+TdLrtJ6/qseBXkraxfTGApG2Bm4pjitXXZaK0N8Ow4J3mePqcScYSv5cnAq+Q9EPakNzyTWI9SpJmYZujQvLfgEV13pLWp4iIWJAkPYy2ulHAObYvLA5puR6GsEtaG1iXNsvgCUwNXt4A+LLtP6uIq1dDYuZYWnLNwMbAbrYvKg0sVoukLwAvAfYDngRcB9zB9g6lgUXchiRtMtvjtn846Vgi5kuvreSTloqaiIhYkIYL6C4uoodNSpfbfjB0s9b1FbSL1o1pXyfREhBLgQ8VxtWrTYGH0trqdqHdwcvdrAXC9i7DhwdKOoMhUVoYUsRtLgmZWCRSIQmsUR1ARETEQmf7VuAySUuqYxmxfajtTYF3AFsPHx8BXA2cVxpcn/a3fT1wF+CptFXPh9WGFL8P22fZPrGqmi0iIv4gM1vJT6CDVvJJS+tTRETEbUDS12mluhfQZgcAYHvnsqAASZfb3lLS9sA/AO8B3jhzRe9iJ+kS2w+V9E7gCttHjx6rji1ivkj6c+Ba29+ojiUiYqYeWsmrpPUpIiLitnFn2trUEQHvKopl3Gi95Y7AR2yfIOnAwnh6dY2kw4GnAO+SdCdSeRy3f9sBD5G0lu1nVgcTETGuk1byEqmoiYiIuA1Iutj2NjMeu7x6G4ekk4FraAmIh9E2GV1ge6vKuHozrOJ+Bq2a5ruS7gk8xPZXikOLiIiIRSaJmoiIiD+ApL2BVwL3A74/9tT6wLm29ywJbJAERESMDMeD1wJLbL9M0v2BLWyfXBxaRESMSaImIiLiDyBpQ+CuwDuBvx97aqnt/6uJKiJiRZKOpW2Be6HtB0taBzjP9tbFoUVExJgkaiIiIiIiFgFJF9rednxQtqTL0goZEdGXDMmLiIiIiFgcbh6qaAwgaTPgN7UhRUTETNn6FBERERGxOBwAnArcR9KngccALy6NKCIiVpDWp4iIiIiIRULS3YFHAgLOt/3z4pAiImKGJGoiIiIiIhYJSVsC92Wsst7258sCioiIFaT1KSIiIiJiEZD0z8CWwDeBW4eHDSRRExHRkVTUREREREQsApK+ZfuB1XFERMTKZetTRERERMTicJ6kJGoiIjqXipqIiIiIiEVA0uOAk4BraWu5Bdj2lqWBRUTENEnUREREREQsApK+B7wGuIKpGTXY/mFZUBERsYIME46IiIiIWBx+ZPvE6iAiImLlUlETEREREbEISPowcBda+9NvRo9nPXdERF9SURMRERERsTisQ0vQPG3ssaznjojoTCpqIiIiIiIiIiI6kYqaiIiIiIjbMUmvs32wpA/SKmimsb1PQVgRETGHJGoiIiIiIm7fvj38fWFpFBERsVqSqImIiIiIuB2zfdLw4Y22jx9/TtKuBSFFRMRKZEZNRERERMQiIOli29us6rGIiKiVipqIiIiIiNsxSc8EdgDuJekDY09tAPy2JqqIiJhLEjUREREREbdvP6HNp9kZuGjs8aXAq0siioiIOaX1KSIiIiJiEZC0E3CK7VurY4mIiLmtUR1ARERERERMxG7AdyUdLOnPqoOJiIjZpaImIiIiImKRkLQBsDvwEsDAEcBnbC8tDSwiIpZLRU1ERERExCJh+3rgc8AxwD2BXYCLJb2qNLCIiFguFTUREREREYvAMKNmL2Az4CjgSNs/k7Qu8G3bm5QGGBERQLY+RUREREQsFrsC77N99viDtm+UtFdRTBERMUMqaiIiIiIiIiIiOpGKmoiIiIiI2zFJS2mDg2dle4MJhhMREauQRE1ERERExO2Y7fUBJL0VuJY2n0bAHsD6haFFRMQs0voUEREREbEISPqG7e1W9VhERNTKeu6IiIiIiMVhmaQ9JK0paQ1JewDLqoOKiIjpkqiJiIiIiFgcng88F/jv4c+uw2MREdGRtD5FRERERERERHQiw4QjIiIiIhYBSUcwy/Yn23sVhBMREXNIoiYiIiIiYnE4eezjtYFdgJ8UxRIREXNI61NERERExCIkaQ3ga7afVB1LRERMyTDhiIiIiIjF6f7AkuogIiJiurQ+RUREREQsApKWMn1GzbXA64vCiYiIOaT1KSIiIiIiIiKiE2l9ioiIiIhYBCSdvjqPRURErbQ+RURERETcjklaG1gX2EjSXQENT20AbFwWWEREzCqJmoiIiIiI27dXAPvRkjIXMZWouR74p6qgIiJidplRExERERGxCEh6le0PVscRERErlxk1ERERERGLw7WS1geQ9GZJn5e0TXVQERExXRI1ERERERGLw/62l0raHng6cCRwWHFMERExQxI1ERERERGLw7Lh7x2Bw2yfANyxMJ6IiJhFEjUREREREYvDNZIOB54LfEnSncj1QEREdzJMOCIiIiJiEZC0LvAM4Arb35V0T+Ahtr9SHFpERIxJoiYiIiIiIiIiohMpdYyIiIiIiIiI6EQSNRERERERERERnUiiJiIiIiIiIiKiE0nURERERERERER04v8BQvMrN8y6814AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(20, 12))\n",
    "sns_heatmap = sns.heatmap(\n",
    "    math.isnull(), yticklabels=False, cbar=False, cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "school                  0\n",
       "sex                     0\n",
       "age                     0\n",
       "address                17\n",
       "famsize                27\n",
       "Pstatus                45\n",
       "Medu                    3\n",
       "Fedu                   24\n",
       "Mjob                   19\n",
       "Fjob                   36\n",
       "reason                 17\n",
       "guardian               31\n",
       "traveltime             28\n",
       "studytime               7\n",
       "failures               22\n",
       "schoolsup               9\n",
       "famsup                 39\n",
       "paid                   40\n",
       "activities             14\n",
       "nursery                16\n",
       "studytime, granular     7\n",
       "higher                 20\n",
       "internet               34\n",
       "romantic               31\n",
       "famrel                 27\n",
       "freetime               11\n",
       "goout                   8\n",
       "health                 15\n",
       "absences               12\n",
       "score                   6\n",
       "dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Поиск выбросов\n",
    "\n",
    "Далее будут показаны признаки у которых наблюдаются выбрасы значений"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe9cce92e10>"
      ]
     },
     "execution_count": 134,
     "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": [
    "math.Fedu.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe9e85ddc10>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "math.famrel.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe9e85be2d0>"
      ]
     },
     "execution_count": 137,
     "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": [
    "math.absences.plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Убираем выбрасы для признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "math['Fedu'] = math['Fedu'].apply(lambda w: np.log(w + 1))\n",
    "math['famrel'] = math['famrel'].apply(lambda w: np.log(w + 1))\n",
    "math['absences'] = math['absences'].apply(lambda w: np.log(w + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "supplied range of [-inf, 1.791759469228055] is not finite",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-142-294cf5732d24>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpairplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'reg'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mpairplot\u001b[0;34m(data, hue, hue_order, palette, vars, x_vars, y_vars, kind, diag_kind, markers, height, aspect, corner, dropna, plot_kws, diag_kws, grid_kws, size)\u001b[0m\n\u001b[1;32m   2107\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msquare_grid\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2108\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"hist\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2109\u001b[0;31m             \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_diag\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mdiag_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2110\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mdiag_kind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"kde\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2111\u001b[0m             \u001b[0mdiag_kws\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"shade\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mmap_diag\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m   1480\u001b[0m                     \u001b[0mdata_k\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove_na\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_k\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1481\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1482\u001b[0;31m                 \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel_k\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1484\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clean_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, normed, data, **kwargs)\u001b[0m\n\u001b[1;32m   2641\u001b[0m         \u001b[0malign\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0malign\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morientation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morientation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrwidth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlog\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2642\u001b[0m         \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstacked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstacked\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnormed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnormed\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2643\u001b[0;31m         **({\"data\": data} if data is not None else {}), **kwargs)\n\u001b[0m\u001b[1;32m   2644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2645\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1597\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1598\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1599\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msanitize_sequence\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1600\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1601\u001b[0m         \u001b[0mbound\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_sig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, normed, **kwargs)\u001b[0m\n\u001b[1;32m   6773\u001b[0m             \u001b[0;31m# this will automatically overwrite bins,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6774\u001b[0m             \u001b[0;31m# so that each histogram uses the same bins\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6775\u001b[0;31m             \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhist_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6776\u001b[0m             \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# causes problems later if it's an int\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6777\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmlast\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mhistogram\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36mhistogram\u001b[0;34m(a, bins, range, normed, weights, density)\u001b[0m\n\u001b[1;32m    793\u001b[0m     \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ravel_and_check_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    794\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 795\u001b[0;31m     \u001b[0mbin_edges\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muniform_bins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_bin_edges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    796\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    797\u001b[0m     \u001b[0;31m# Histogram is an integer or a float array depending on the weights.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36m_get_bin_edges\u001b[0;34m(a, bins, range, weights)\u001b[0m\n\u001b[1;32m    427\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'`bins` must be positive, when an integer'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    428\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 429\u001b[0;31m         \u001b[0mfirst_edge\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast_edge\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_outer_edges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    431\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/numpy/lib/histograms.py\u001b[0m in \u001b[0;36m_get_outer_edges\u001b[0;34m(a, range)\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfirst_edge\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfinite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlast_edge\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m             raise ValueError(\n\u001b[0;32m--> 319\u001b[0;31m                 \"supplied range of [{}, {}] is not finite\".format(first_edge, last_edge))\n\u001b[0m\u001b[1;32m    320\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0;31m# handle empty arrays. Can't determine range, so use 0-1.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: supplied range of [-inf, 1.791759469228055] is not finite"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2340x2340 with 182 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(math, kind = 'reg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Корреляция признаков"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "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>age</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>traveltime</th>\n",
       "      <th>studytime</th>\n",
       "      <th>failures</th>\n",
       "      <th>studytime, granular</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.161704</td>\n",
       "      <td>-0.172711</td>\n",
       "      <td>0.081508</td>\n",
       "      <td>-0.015556</td>\n",
       "      <td>0.228047</td>\n",
       "      <td>0.015556</td>\n",
       "      <td>0.062650</td>\n",
       "      <td>0.029023</td>\n",
       "      <td>0.135107</td>\n",
       "      <td>-0.065946</td>\n",
       "      <td>0.149995</td>\n",
       "      <td>-0.155726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Medu</th>\n",
       "      <td>-0.161704</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.564380</td>\n",
       "      <td>-0.173515</td>\n",
       "      <td>0.078589</td>\n",
       "      <td>-0.245463</td>\n",
       "      <td>-0.078589</td>\n",
       "      <td>-0.037503</td>\n",
       "      <td>0.025335</td>\n",
       "      <td>0.064164</td>\n",
       "      <td>-0.045051</td>\n",
       "      <td>0.077649</td>\n",
       "      <td>0.213349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fedu</th>\n",
       "      <td>-0.172711</td>\n",
       "      <td>0.564380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.114598</td>\n",
       "      <td>0.035312</td>\n",
       "      <td>-0.271115</td>\n",
       "      <td>-0.035312</td>\n",
       "      <td>-0.008562</td>\n",
       "      <td>-0.048061</td>\n",
       "      <td>0.021740</td>\n",
       "      <td>0.021055</td>\n",
       "      <td>0.017527</td>\n",
       "      <td>0.125383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>traveltime</th>\n",
       "      <td>0.081508</td>\n",
       "      <td>-0.173515</td>\n",
       "      <td>-0.114598</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.102206</td>\n",
       "      <td>0.064699</td>\n",
       "      <td>0.102206</td>\n",
       "      <td>-0.029700</td>\n",
       "      <td>-0.021937</td>\n",
       "      <td>0.037636</td>\n",
       "      <td>-0.021531</td>\n",
       "      <td>-0.029822</td>\n",
       "      <td>-0.070559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>studytime</th>\n",
       "      <td>-0.015556</td>\n",
       "      <td>0.078589</td>\n",
       "      <td>0.035312</td>\n",
       "      <td>-0.102206</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.178992</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.017882</td>\n",
       "      <td>-0.121969</td>\n",
       "      <td>-0.051280</td>\n",
       "      <td>-0.080016</td>\n",
       "      <td>-0.047275</td>\n",
       "      <td>0.115741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>failures</th>\n",
       "      <td>0.228047</td>\n",
       "      <td>-0.245463</td>\n",
       "      <td>-0.271115</td>\n",
       "      <td>0.064699</td>\n",
       "      <td>-0.178992</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.178992</td>\n",
       "      <td>0.009944</td>\n",
       "      <td>0.098382</td>\n",
       "      <td>0.120914</td>\n",
       "      <td>0.081284</td>\n",
       "      <td>0.068134</td>\n",
       "      <td>-0.344338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>studytime, granular</th>\n",
       "      <td>0.015556</td>\n",
       "      <td>-0.078589</td>\n",
       "      <td>-0.035312</td>\n",
       "      <td>0.102206</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.178992</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.017882</td>\n",
       "      <td>0.121969</td>\n",
       "      <td>0.051280</td>\n",
       "      <td>0.080016</td>\n",
       "      <td>0.047275</td>\n",
       "      <td>-0.115741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>famrel</th>\n",
       "      <td>0.062650</td>\n",
       "      <td>-0.037503</td>\n",
       "      <td>-0.008562</td>\n",
       "      <td>-0.029700</td>\n",
       "      <td>0.017882</td>\n",
       "      <td>0.009944</td>\n",
       "      <td>-0.017882</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.163342</td>\n",
       "      <td>0.068154</td>\n",
       "      <td>0.111806</td>\n",
       "      <td>-0.097491</td>\n",
       "      <td>0.042237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freetime</th>\n",
       "      <td>0.029023</td>\n",
       "      <td>0.025335</td>\n",
       "      <td>-0.048061</td>\n",
       "      <td>-0.021937</td>\n",
       "      <td>-0.121969</td>\n",
       "      <td>0.098382</td>\n",
       "      <td>0.121969</td>\n",
       "      <td>0.163342</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.296437</td>\n",
       "      <td>0.061575</td>\n",
       "      <td>0.003439</td>\n",
       "      <td>0.019026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>goout</th>\n",
       "      <td>0.135107</td>\n",
       "      <td>0.064164</td>\n",
       "      <td>0.021740</td>\n",
       "      <td>0.037636</td>\n",
       "      <td>-0.051280</td>\n",
       "      <td>0.120914</td>\n",
       "      <td>0.051280</td>\n",
       "      <td>0.068154</td>\n",
       "      <td>0.296437</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015608</td>\n",
       "      <td>0.102736</td>\n",
       "      <td>-0.129685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>health</th>\n",
       "      <td>-0.065946</td>\n",
       "      <td>-0.045051</td>\n",
       "      <td>0.021055</td>\n",
       "      <td>-0.021531</td>\n",
       "      <td>-0.080016</td>\n",
       "      <td>0.081284</td>\n",
       "      <td>0.080016</td>\n",
       "      <td>0.111806</td>\n",
       "      <td>0.061575</td>\n",
       "      <td>-0.015608</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.048090</td>\n",
       "      <td>-0.070460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>absences</th>\n",
       "      <td>0.149995</td>\n",
       "      <td>0.077649</td>\n",
       "      <td>0.017527</td>\n",
       "      <td>-0.029822</td>\n",
       "      <td>-0.047275</td>\n",
       "      <td>0.068134</td>\n",
       "      <td>0.047275</td>\n",
       "      <td>-0.097491</td>\n",
       "      <td>0.003439</td>\n",
       "      <td>0.102736</td>\n",
       "      <td>-0.048090</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.168414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>-0.155726</td>\n",
       "      <td>0.213349</td>\n",
       "      <td>0.125383</td>\n",
       "      <td>-0.070559</td>\n",
       "      <td>0.115741</td>\n",
       "      <td>-0.344338</td>\n",
       "      <td>-0.115741</td>\n",
       "      <td>0.042237</td>\n",
       "      <td>0.019026</td>\n",
       "      <td>-0.129685</td>\n",
       "      <td>-0.070460</td>\n",
       "      <td>0.168414</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          age      Medu      Fedu  traveltime  studytime  \\\n",
       "age                  1.000000 -0.161704 -0.172711    0.081508  -0.015556   \n",
       "Medu                -0.161704  1.000000  0.564380   -0.173515   0.078589   \n",
       "Fedu                -0.172711  0.564380  1.000000   -0.114598   0.035312   \n",
       "traveltime           0.081508 -0.173515 -0.114598    1.000000  -0.102206   \n",
       "studytime           -0.015556  0.078589  0.035312   -0.102206   1.000000   \n",
       "failures             0.228047 -0.245463 -0.271115    0.064699  -0.178992   \n",
       "studytime, granular  0.015556 -0.078589 -0.035312    0.102206  -1.000000   \n",
       "famrel               0.062650 -0.037503 -0.008562   -0.029700   0.017882   \n",
       "freetime             0.029023  0.025335 -0.048061   -0.021937  -0.121969   \n",
       "goout                0.135107  0.064164  0.021740    0.037636  -0.051280   \n",
       "health              -0.065946 -0.045051  0.021055   -0.021531  -0.080016   \n",
       "absences             0.149995  0.077649  0.017527   -0.029822  -0.047275   \n",
       "score               -0.155726  0.213349  0.125383   -0.070559   0.115741   \n",
       "\n",
       "                     failures  studytime, granular    famrel  freetime  \\\n",
       "age                  0.228047             0.015556  0.062650  0.029023   \n",
       "Medu                -0.245463            -0.078589 -0.037503  0.025335   \n",
       "Fedu                -0.271115            -0.035312 -0.008562 -0.048061   \n",
       "traveltime           0.064699             0.102206 -0.029700 -0.021937   \n",
       "studytime           -0.178992            -1.000000  0.017882 -0.121969   \n",
       "failures             1.000000             0.178992  0.009944  0.098382   \n",
       "studytime, granular  0.178992             1.000000 -0.017882  0.121969   \n",
       "famrel               0.009944            -0.017882  1.000000  0.163342   \n",
       "freetime             0.098382             0.121969  0.163342  1.000000   \n",
       "goout                0.120914             0.051280  0.068154  0.296437   \n",
       "health               0.081284             0.080016  0.111806  0.061575   \n",
       "absences             0.068134             0.047275 -0.097491  0.003439   \n",
       "score               -0.344338            -0.115741  0.042237  0.019026   \n",
       "\n",
       "                        goout    health  absences     score  \n",
       "age                  0.135107 -0.065946  0.149995 -0.155726  \n",
       "Medu                 0.064164 -0.045051  0.077649  0.213349  \n",
       "Fedu                 0.021740  0.021055  0.017527  0.125383  \n",
       "traveltime           0.037636 -0.021531 -0.029822 -0.070559  \n",
       "studytime           -0.051280 -0.080016 -0.047275  0.115741  \n",
       "failures             0.120914  0.081284  0.068134 -0.344338  \n",
       "studytime, granular  0.051280  0.080016  0.047275 -0.115741  \n",
       "famrel               0.068154  0.111806 -0.097491  0.042237  \n",
       "freetime             0.296437  0.061575  0.003439  0.019026  \n",
       "goout                1.000000 -0.015608  0.102736 -0.129685  \n",
       "health              -0.015608  1.000000 -0.048090 -0.070460  \n",
       "absences             0.102736 -0.048090  1.000000  0.168414  \n",
       "score               -0.129685 -0.070460  0.168414  1.000000  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Краткий вывод\n",
    "\n",
    "У прогнозируемого признака \"score\" больше всего корреляция есть у признаков \"Medu\", \"studytime\", \"Fedu\", \"absences\". На основе этих признаков можно попытаться обучить модель для прогнозирования \"score\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}