Shiryaeva_Ekaterina_ml_1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Australian Credit Approval\n",
    "\n",
    "Data base: Statlog (Australian Credit Approval)\n",
    "https://archive.ics.uci.edu/ml/datasets/Statlog+%28Australian+Credit+Approval%29"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "База данных содержит информацию о заявках на кредитные карты. Все имена атрибутов и значения были изменены на бессмысленные символы для защиты конфиденциальности данных.  \n",
    "Содержит всего 15 атрибутов.  \n",
    "6 числовых атрибута:       А2, А3, А7, А10, А13, А14  \n",
    "8 категориальных атрибута: А1, А4, A5, A8, A9, A11, A12  \n",
    "И последний - атрибут класса (0 или 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Получаем данные:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/statlog/australian/australian.dat'\n",
    "#data = pd.read_csv(url, sep = ' ', engine='python')\n",
    "#data = pd.read_csv('C:/Users/Kate/Desktop/australian.csv', sep = ' ', engine='python')\n",
    "data = pd.read_csv('C:/Users/eshiryae.CCR/Desktop/australian.csv', sep = ' ', engine='python')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Добавим названия столбцов:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.columns=[u'A1', u'A2', u'A3', u'A4', u'A5', u'A6', u'A7', u'A8', u'A9', u'A10', u'A11', u'A12', u'A13', u'A14', u'class' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.67</td>\n",
       "      <td>7.000</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>29.58</td>\n",
       "      <td>1.750</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>280</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>21.67</td>\n",
       "      <td>11.500</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>20.17</td>\n",
       "      <td>8.170</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1.960</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>15.83</td>\n",
       "      <td>0.585</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>17.42</td>\n",
       "      <td>6.500</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>101</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>58.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>3.040</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>43</td>\n",
       "      <td>561</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>27.83</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>3.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>176</td>\n",
       "      <td>538</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>55.75</td>\n",
       "      <td>7.080</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>6.750</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>33.50</td>\n",
       "      <td>1.750</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>4.500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>253</td>\n",
       "      <td>858</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>41.42</td>\n",
       "      <td>5.000</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>470</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>20.67</td>\n",
       "      <td>1.250</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1.375</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>34.92</td>\n",
       "      <td>5.000</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>7.500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>58.58</td>\n",
       "      <td>2.710</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2.415</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>320</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>48.08</td>\n",
       "      <td>6.040</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2691</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>29.58</td>\n",
       "      <td>4.500</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>7.500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>330</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>18.92</td>\n",
       "      <td>9.000</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0.750</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>88</td>\n",
       "      <td>592</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>20.00</td>\n",
       "      <td>1.250</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>140</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>22.42</td>\n",
       "      <td>5.665</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>2.585</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>129</td>\n",
       "      <td>3258</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>28.17</td>\n",
       "      <td>0.585</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>260</td>\n",
       "      <td>1005</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>19.17</td>\n",
       "      <td>0.585</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0.585</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1</td>\n",
       "      <td>41.17</td>\n",
       "      <td>1.335</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>168</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1</td>\n",
       "      <td>41.58</td>\n",
       "      <td>1.750</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.210</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1</td>\n",
       "      <td>19.50</td>\n",
       "      <td>9.585</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0.790</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>351</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1</td>\n",
       "      <td>32.75</td>\n",
       "      <td>1.500</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>5.500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>1</td>\n",
       "      <td>22.50</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>71</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>1</td>\n",
       "      <td>33.17</td>\n",
       "      <td>3.040</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2.040</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>180</td>\n",
       "      <td>18028</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0</td>\n",
       "      <td>30.67</td>\n",
       "      <td>12.000</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>220</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>23.08</td>\n",
       "      <td>2.500</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1.085</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>2185</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1</td>\n",
       "      <td>27.00</td>\n",
       "      <td>0.750</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>4.250</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>312</td>\n",
       "      <td>151</td>\n",
       "      <td>1</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>1</td>\n",
       "      <td>26.67</td>\n",
       "      <td>2.710</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>5.250</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>211</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>0</td>\n",
       "      <td>22.50</td>\n",
       "      <td>0.415</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>1</td>\n",
       "      <td>39.92</td>\n",
       "      <td>0.540</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>1001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>0</td>\n",
       "      <td>26.08</td>\n",
       "      <td>8.665</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1.415</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>151</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>1</td>\n",
       "      <td>20.00</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>144</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>1</td>\n",
       "      <td>31.57</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>411</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>1</td>\n",
       "      <td>26.75</td>\n",
       "      <td>4.500</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>1211</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>0</td>\n",
       "      <td>24.92</td>\n",
       "      <td>1.250</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>0</td>\n",
       "      <td>32.25</td>\n",
       "      <td>1.500</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>372</td>\n",
       "      <td>123</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>1</td>\n",
       "      <td>17.67</td>\n",
       "      <td>4.460</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>0</td>\n",
       "      <td>37.75</td>\n",
       "      <td>5.500</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>228</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670</th>\n",
       "      <td>1</td>\n",
       "      <td>22.67</td>\n",
       "      <td>2.540</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>2.585</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>0</td>\n",
       "      <td>17.92</td>\n",
       "      <td>10.210</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>1</td>\n",
       "      <td>24.42</td>\n",
       "      <td>12.335</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>1.585</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>0</td>\n",
       "      <td>25.75</td>\n",
       "      <td>0.500</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>0.875</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>491</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>674</th>\n",
       "      <td>1</td>\n",
       "      <td>26.17</td>\n",
       "      <td>12.500</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1.250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>0</td>\n",
       "      <td>22.75</td>\n",
       "      <td>6.165</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>220</td>\n",
       "      <td>1001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>1</td>\n",
       "      <td>23.00</td>\n",
       "      <td>0.750</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>320</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>1</td>\n",
       "      <td>25.67</td>\n",
       "      <td>0.290</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>1</td>\n",
       "      <td>48.58</td>\n",
       "      <td>0.205</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>380</td>\n",
       "      <td>2733</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679</th>\n",
       "      <td>1</td>\n",
       "      <td>21.17</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680</th>\n",
       "      <td>1</td>\n",
       "      <td>35.25</td>\n",
       "      <td>16.500</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>4.000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>0</td>\n",
       "      <td>22.92</td>\n",
       "      <td>11.585</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>0.040</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>1350</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>0</td>\n",
       "      <td>48.17</td>\n",
       "      <td>1.335</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>1</td>\n",
       "      <td>43.00</td>\n",
       "      <td>0.290</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>1.750</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>376</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>1</td>\n",
       "      <td>31.57</td>\n",
       "      <td>10.500</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>6.500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>1</td>\n",
       "      <td>20.67</td>\n",
       "      <td>0.415</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>0</td>\n",
       "      <td>18.83</td>\n",
       "      <td>9.540</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0.085</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>0</td>\n",
       "      <td>27.42</td>\n",
       "      <td>14.500</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3.085</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>1</td>\n",
       "      <td>41.00</td>\n",
       "      <td>0.040</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>689 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     A1     A2      A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  A13    A14  \\\n",
       "0     0  22.67   7.000   2   8   4  0.165   0   0    0    0    2  160      1   \n",
       "1     0  29.58   1.750   1   4   4  1.250   0   0    0    1    2  280      1   \n",
       "2     0  21.67  11.500   1   5   3  0.000   1   1   11    1    2    0      1   \n",
       "3     1  20.17   8.170   2   6   4  1.960   1   1   14    0    2   60    159   \n",
       "4     0  15.83   0.585   2   8   8  1.500   1   1    2    0    2  100      1   \n",
       "5     1  17.42   6.500   2   3   4  0.125   0   0    0    0    2   60    101   \n",
       "6     0  58.67   4.460   2  11   8  3.040   1   1    6    0    2   43    561   \n",
       "7     1  27.83   1.000   1   2   8  3.000   0   0    0    0    2  176    538   \n",
       "8     0  55.75   7.080   2   4   8  6.750   1   1    3    1    2  100     51   \n",
       "9     1  33.50   1.750   2  14   8  4.500   1   1    4    1    2  253    858   \n",
       "10    1  41.42   5.000   2  11   8  5.000   1   1    6    1    2  470      1   \n",
       "11    1  20.67   1.250   1   8   8  1.375   1   1    3    1    2  140    211   \n",
       "12    1  34.92   5.000   2  14   8  7.500   1   1    6    1    2    0   1001   \n",
       "13    1  58.58   2.710   2   8   4  2.415   0   0    0    1    2  320      1   \n",
       "14    1  48.08   6.040   2   4   4  0.040   0   0    0    0    2    0   2691   \n",
       "15    1  29.58   4.500   2   9   4  7.500   1   1    2    1    2  330      1   \n",
       "16    0  18.92   9.000   2   6   4  0.750   1   1    2    0    2   88    592   \n",
       "17    1  20.00   1.250   1   4   4  0.125   0   0    0    0    2  140      5   \n",
       "18    0  22.42   5.665   2  11   4  2.585   1   1    7    0    2  129   3258   \n",
       "19    0  28.17   0.585   2   6   4  0.040   0   0    0    0    2  260   1005   \n",
       "20    0  19.17   0.585   1   6   4  0.585   1   0    0    1    2  160      1   \n",
       "21    1  41.17   1.335   2   2   4  0.165   0   0    0    0    2  168      1   \n",
       "22    1  41.58   1.750   2   4   4  0.210   1   0    0    0    2  160      1   \n",
       "23    1  19.50   9.585   2   6   4  0.790   0   0    0    0    2   80    351   \n",
       "24    1  32.75   1.500   2  13   8  5.500   1   1    3    1    2    0      1   \n",
       "25    1  22.50   0.125   1   4   4  0.125   0   0    0    0    2  200     71   \n",
       "26    1  33.17   3.040   1   8   8  2.040   1   1    1    1    2  180  18028   \n",
       "27    0  30.67  12.000   2   8   4  2.000   1   1    1    0    2  220     20   \n",
       "28    1  23.08   2.500   2   8   4  1.085   1   1   11    1    2   60   2185   \n",
       "29    1  27.00   0.750   2   8   8  4.250   1   1    3    1    2  312    151   \n",
       "..   ..    ...     ...  ..  ..  ..    ...  ..  ..  ...  ...  ...  ...    ...   \n",
       "659   1  26.67   2.710   1  13   4  5.250   1   1    1    0    2  211      1   \n",
       "660   0  22.50   0.415   2   3   4  0.335   0   0    0    1    1  144      1   \n",
       "661   1  39.92   0.540   1   6   4  0.500   1   1    3    0    2  200   1001   \n",
       "662   0  26.08   8.665   2   6   4  1.415   1   0    0    0    2  160    151   \n",
       "663   1  20.00   0.000   2   2   4  0.500   0   0    0    0    2  144      1   \n",
       "664   1  31.57   4.000   1   3   4  0.085   0   0    0    1    2  411      1   \n",
       "665   1  26.75   4.500   1   8   5  2.500   0   0    0    0    2  200   1211   \n",
       "666   0  24.92   1.250   2   1   1  0.000   1   0    0    0    2   80      1   \n",
       "667   0  32.25   1.500   2   8   4  0.250   0   0    0    1    2  372    123   \n",
       "668   1  17.67   4.460   2   8   4  0.250   0   0    0    0    1   80      1   \n",
       "669   0  37.75   5.500   2  11   4  0.125   1   0    0    1    2  228      1   \n",
       "670   1  22.67   2.540   1   8   8  2.585   1   0    0    0    2    0      1   \n",
       "671   0  17.92  10.210   2   1   1  0.000   0   0    0    0    2    0     51   \n",
       "672   1  24.42  12.335   2  11   8  1.585   1   0    0    1    2  120      1   \n",
       "673   0  25.75   0.500   2   8   8  0.875   1   0    0    1    2  491      1   \n",
       "674   1  26.17  12.500   1   4   8  1.250   0   0    0    1    2    0     18   \n",
       "675   0  22.75   6.165   2   6   4  0.165   0   0    0    0    2  220   1001   \n",
       "676   1  23.00   0.750   2   7   4  0.500   1   0    0    1    1  320      1   \n",
       "677   1  25.67   0.290   1   8   4  1.500   0   0    0    1    2  160      1   \n",
       "678   1  48.58   0.205   1   4   4  0.250   1   1   11    0    2  380   2733   \n",
       "679   1  21.17   0.000   2   8   4  0.500   0   0    0    1    1    0      1   \n",
       "680   1  35.25  16.500   1   8   4  4.000   1   0    0    0    2   80      1   \n",
       "681   0  22.92  11.585   2  13   4  0.040   1   0    0    0    2   80   1350   \n",
       "682   0  48.17   1.335   2   3   7  0.335   0   0    0    0    2    0    121   \n",
       "683   1  43.00   0.290   1  13   8  1.750   1   1    8    0    2  100    376   \n",
       "684   1  31.57  10.500   2  14   4  6.500   1   0    0    0    2    0      1   \n",
       "685   1  20.67   0.415   2   8   4  0.125   0   0    0    0    2    0     45   \n",
       "686   0  18.83   9.540   2   6   4  0.085   1   0    0    0    2  100      1   \n",
       "687   0  27.42  14.500   2  14   8  3.085   1   1    1    0    2  120     12   \n",
       "688   1  41.00   0.040   2  10   4  0.040   0   1    1    0    1  560      1   \n",
       "\n",
       "     class  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  \n",
       "5        0  \n",
       "6        1  \n",
       "7        0  \n",
       "8        0  \n",
       "9        1  \n",
       "10       1  \n",
       "11       0  \n",
       "12       1  \n",
       "13       0  \n",
       "14       1  \n",
       "15       1  \n",
       "16       1  \n",
       "17       0  \n",
       "18       1  \n",
       "19       0  \n",
       "20       0  \n",
       "21       0  \n",
       "22       0  \n",
       "23       0  \n",
       "24       1  \n",
       "25       0  \n",
       "26       1  \n",
       "27       1  \n",
       "28       1  \n",
       "29       1  \n",
       "..     ...  \n",
       "659      1  \n",
       "660      0  \n",
       "661      1  \n",
       "662      1  \n",
       "663      0  \n",
       "664      0  \n",
       "665      0  \n",
       "666      0  \n",
       "667      0  \n",
       "668      0  \n",
       "669      1  \n",
       "670      1  \n",
       "671      0  \n",
       "672      1  \n",
       "673      1  \n",
       "674      0  \n",
       "675      0  \n",
       "676      0  \n",
       "677      0  \n",
       "678      1  \n",
       "679      0  \n",
       "680      0  \n",
       "681      1  \n",
       "682      0  \n",
       "683      1  \n",
       "684      1  \n",
       "685      0  \n",
       "686      1  \n",
       "687      1  \n",
       "688      1  \n",
       "\n",
       "[689 rows x 15 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Выведем данные стандартными средствами python:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A1     A2      A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  A13    A14  \\\n",
      "0     0  22.67   7.000   2   8   4  0.165   0   0    0    0    2  160      1   \n",
      "1     0  29.58   1.750   1   4   4  1.250   0   0    0    1    2  280      1   \n",
      "2     0  21.67  11.500   1   5   3  0.000   1   1   11    1    2    0      1   \n",
      "3     1  20.17   8.170   2   6   4  1.960   1   1   14    0    2   60    159   \n",
      "4     0  15.83   0.585   2   8   8  1.500   1   1    2    0    2  100      1   \n",
      "5     1  17.42   6.500   2   3   4  0.125   0   0    0    0    2   60    101   \n",
      "6     0  58.67   4.460   2  11   8  3.040   1   1    6    0    2   43    561   \n",
      "7     1  27.83   1.000   1   2   8  3.000   0   0    0    0    2  176    538   \n",
      "8     0  55.75   7.080   2   4   8  6.750   1   1    3    1    2  100     51   \n",
      "9     1  33.50   1.750   2  14   8  4.500   1   1    4    1    2  253    858   \n",
      "10    1  41.42   5.000   2  11   8  5.000   1   1    6    1    2  470      1   \n",
      "11    1  20.67   1.250   1   8   8  1.375   1   1    3    1    2  140    211   \n",
      "12    1  34.92   5.000   2  14   8  7.500   1   1    6    1    2    0   1001   \n",
      "13    1  58.58   2.710   2   8   4  2.415   0   0    0    1    2  320      1   \n",
      "14    1  48.08   6.040   2   4   4  0.040   0   0    0    0    2    0   2691   \n",
      "15    1  29.58   4.500   2   9   4  7.500   1   1    2    1    2  330      1   \n",
      "16    0  18.92   9.000   2   6   4  0.750   1   1    2    0    2   88    592   \n",
      "17    1  20.00   1.250   1   4   4  0.125   0   0    0    0    2  140      5   \n",
      "18    0  22.42   5.665   2  11   4  2.585   1   1    7    0    2  129   3258   \n",
      "19    0  28.17   0.585   2   6   4  0.040   0   0    0    0    2  260   1005   \n",
      "20    0  19.17   0.585   1   6   4  0.585   1   0    0    1    2  160      1   \n",
      "21    1  41.17   1.335   2   2   4  0.165   0   0    0    0    2  168      1   \n",
      "22    1  41.58   1.750   2   4   4  0.210   1   0    0    0    2  160      1   \n",
      "23    1  19.50   9.585   2   6   4  0.790   0   0    0    0    2   80    351   \n",
      "24    1  32.75   1.500   2  13   8  5.500   1   1    3    1    2    0      1   \n",
      "25    1  22.50   0.125   1   4   4  0.125   0   0    0    0    2  200     71   \n",
      "26    1  33.17   3.040   1   8   8  2.040   1   1    1    1    2  180  18028   \n",
      "27    0  30.67  12.000   2   8   4  2.000   1   1    1    0    2  220     20   \n",
      "28    1  23.08   2.500   2   8   4  1.085   1   1   11    1    2   60   2185   \n",
      "29    1  27.00   0.750   2   8   8  4.250   1   1    3    1    2  312    151   \n",
      "..   ..    ...     ...  ..  ..  ..    ...  ..  ..  ...  ...  ...  ...    ...   \n",
      "659   1  26.67   2.710   1  13   4  5.250   1   1    1    0    2  211      1   \n",
      "660   0  22.50   0.415   2   3   4  0.335   0   0    0    1    1  144      1   \n",
      "661   1  39.92   0.540   1   6   4  0.500   1   1    3    0    2  200   1001   \n",
      "662   0  26.08   8.665   2   6   4  1.415   1   0    0    0    2  160    151   \n",
      "663   1  20.00   0.000   2   2   4  0.500   0   0    0    0    2  144      1   \n",
      "664   1  31.57   4.000   1   3   4  0.085   0   0    0    1    2  411      1   \n",
      "665   1  26.75   4.500   1   8   5  2.500   0   0    0    0    2  200   1211   \n",
      "666   0  24.92   1.250   2   1   1  0.000   1   0    0    0    2   80      1   \n",
      "667   0  32.25   1.500   2   8   4  0.250   0   0    0    1    2  372    123   \n",
      "668   1  17.67   4.460   2   8   4  0.250   0   0    0    0    1   80      1   \n",
      "669   0  37.75   5.500   2  11   4  0.125   1   0    0    1    2  228      1   \n",
      "670   1  22.67   2.540   1   8   8  2.585   1   0    0    0    2    0      1   \n",
      "671   0  17.92  10.210   2   1   1  0.000   0   0    0    0    2    0     51   \n",
      "672   1  24.42  12.335   2  11   8  1.585   1   0    0    1    2  120      1   \n",
      "673   0  25.75   0.500   2   8   8  0.875   1   0    0    1    2  491      1   \n",
      "674   1  26.17  12.500   1   4   8  1.250   0   0    0    1    2    0     18   \n",
      "675   0  22.75   6.165   2   6   4  0.165   0   0    0    0    2  220   1001   \n",
      "676   1  23.00   0.750   2   7   4  0.500   1   0    0    1    1  320      1   \n",
      "677   1  25.67   0.290   1   8   4  1.500   0   0    0    1    2  160      1   \n",
      "678   1  48.58   0.205   1   4   4  0.250   1   1   11    0    2  380   2733   \n",
      "679   1  21.17   0.000   2   8   4  0.500   0   0    0    1    1    0      1   \n",
      "680   1  35.25  16.500   1   8   4  4.000   1   0    0    0    2   80      1   \n",
      "681   0  22.92  11.585   2  13   4  0.040   1   0    0    0    2   80   1350   \n",
      "682   0  48.17   1.335   2   3   7  0.335   0   0    0    0    2    0    121   \n",
      "683   1  43.00   0.290   1  13   8  1.750   1   1    8    0    2  100    376   \n",
      "684   1  31.57  10.500   2  14   4  6.500   1   0    0    0    2    0      1   \n",
      "685   1  20.67   0.415   2   8   4  0.125   0   0    0    0    2    0     45   \n",
      "686   0  18.83   9.540   2   6   4  0.085   1   0    0    0    2  100      1   \n",
      "687   0  27.42  14.500   2  14   8  3.085   1   1    1    0    2  120     12   \n",
      "688   1  41.00   0.040   2  10   4  0.040   0   1    1    0    1  560      1   \n",
      "\n",
      "     class  \n",
      "0        0  \n",
      "1        0  \n",
      "2        1  \n",
      "3        1  \n",
      "4        1  \n",
      "5        0  \n",
      "6        1  \n",
      "7        0  \n",
      "8        0  \n",
      "9        1  \n",
      "10       1  \n",
      "11       0  \n",
      "12       1  \n",
      "13       0  \n",
      "14       1  \n",
      "15       1  \n",
      "16       1  \n",
      "17       0  \n",
      "18       1  \n",
      "19       0  \n",
      "20       0  \n",
      "21       0  \n",
      "22       0  \n",
      "23       0  \n",
      "24       1  \n",
      "25       0  \n",
      "26       1  \n",
      "27       1  \n",
      "28       1  \n",
      "29       1  \n",
      "..     ...  \n",
      "659      1  \n",
      "660      0  \n",
      "661      1  \n",
      "662      1  \n",
      "663      0  \n",
      "664      0  \n",
      "665      0  \n",
      "666      0  \n",
      "667      0  \n",
      "668      0  \n",
      "669      1  \n",
      "670      1  \n",
      "671      0  \n",
      "672      1  \n",
      "673      1  \n",
      "674      0  \n",
      "675      0  \n",
      "676      0  \n",
      "677      0  \n",
      "678      1  \n",
      "679      0  \n",
      "680      0  \n",
      "681      1  \n",
      "682      0  \n",
      "683      1  \n",
      "684      1  \n",
      "685      0  \n",
      "686      1  \n",
      "687      1  \n",
      "688      1  \n",
      "\n",
      "[689 rows x 15 columns]\n"
     ]
    }
   ],
   "source": [
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Выведем первые 5 строк базы данных(БД):*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>22.67</td>\n",
       "      <td>7.000</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>29.58</td>\n",
       "      <td>1.750</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1.250</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>280</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>21.67</td>\n",
       "      <td>11.500</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>20.17</td>\n",
       "      <td>8.170</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>1.960</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>60</td>\n",
       "      <td>159</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>15.83</td>\n",
       "      <td>0.585</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1.500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A1     A2      A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  A13  A14  \\\n",
       "0   0  22.67   7.000   2   8   4  0.165   0   0    0    0    2  160    1   \n",
       "1   0  29.58   1.750   1   4   4  1.250   0   0    0    1    2  280    1   \n",
       "2   0  21.67  11.500   1   5   3  0.000   1   1   11    1    2    0    1   \n",
       "3   1  20.17   8.170   2   6   4  1.960   1   1   14    0    2   60  159   \n",
       "4   0  15.83   0.585   2   8   8  1.500   1   1    2    0    2  100    1   \n",
       "\n",
       "   class  \n",
       "0      0  \n",
       "1      0  \n",
       "2      1  \n",
       "3      1  \n",
       "4      1  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Выведем последние 5 строк БД:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>1</td>\n",
       "      <td>31.57</td>\n",
       "      <td>10.500</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "      <td>6.500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>1</td>\n",
       "      <td>20.67</td>\n",
       "      <td>0.415</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>686</th>\n",
       "      <td>0</td>\n",
       "      <td>18.83</td>\n",
       "      <td>9.540</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0.085</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>687</th>\n",
       "      <td>0</td>\n",
       "      <td>27.42</td>\n",
       "      <td>14.500</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>3.085</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>120</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>688</th>\n",
       "      <td>1</td>\n",
       "      <td>41.00</td>\n",
       "      <td>0.040</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>0.040</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A1     A2      A3  A4  A5  A6     A7  A8  A9  A10  A11  A12  A13  A14  \\\n",
       "684   1  31.57  10.500   2  14   4  6.500   1   0    0    0    2    0    1   \n",
       "685   1  20.67   0.415   2   8   4  0.125   0   0    0    0    2    0   45   \n",
       "686   0  18.83   9.540   2   6   4  0.085   1   0    0    0    2  100    1   \n",
       "687   0  27.42  14.500   2  14   8  3.085   1   1    1    0    2  120   12   \n",
       "688   1  41.00   0.040   2  10   4  0.040   0   1    1    0    1  560    1   \n",
       "\n",
       "     class  \n",
       "684      1  \n",
       "685      0  \n",
       "686      1  \n",
       "687      1  \n",
       "688      1  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(689, 15)\n",
      "Index(['A1', 'A2', 'A3', 'A4', 'A5', 'A6', 'A7', 'A8', 'A9', 'A10', 'A11',\n",
      "       'A12', 'A13', 'A14', 'class'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)\n",
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=689, step=1)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.at[3, 'A4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       7.000\n",
       "1       1.750\n",
       "2      11.500\n",
       "3       8.170\n",
       "4       0.585\n",
       "5       6.500\n",
       "6       4.460\n",
       "7       1.000\n",
       "8       7.080\n",
       "9       1.750\n",
       "10      5.000\n",
       "11      1.250\n",
       "12      5.000\n",
       "13      2.710\n",
       "14      6.040\n",
       "15      4.500\n",
       "16      9.000\n",
       "17      1.250\n",
       "18      5.665\n",
       "19      0.585\n",
       "20      0.585\n",
       "21      1.335\n",
       "22      1.750\n",
       "23      9.585\n",
       "24      1.500\n",
       "25      0.125\n",
       "26      3.040\n",
       "27     12.000\n",
       "28      2.500\n",
       "29      0.750\n",
       "        ...  \n",
       "659     2.710\n",
       "660     0.415\n",
       "661     0.540\n",
       "662     8.665\n",
       "663     0.000\n",
       "664     4.000\n",
       "665     4.500\n",
       "666     1.250\n",
       "667     1.500\n",
       "668     4.460\n",
       "669     5.500\n",
       "670     2.540\n",
       "671    10.210\n",
       "672    12.335\n",
       "673     0.500\n",
       "674    12.500\n",
       "675     6.165\n",
       "676     0.750\n",
       "677     0.290\n",
       "678     0.205\n",
       "679     0.000\n",
       "680    16.500\n",
       "681    11.585\n",
       "682     1.335\n",
       "683     0.290\n",
       "684    10.500\n",
       "685     0.415\n",
       "686     9.540\n",
       "687    14.500\n",
       "688     0.040\n",
       "Name: A3, Length: 689, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['A3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.170000000000002"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['A2'][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'float64'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['A2'].dtype.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data['A2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>7.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1.750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>11.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>8.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0.585</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A1      A3\n",
       "0   0   7.000\n",
       "1   0   1.750\n",
       "2   0  11.500\n",
       "3   1   8.170\n",
       "4   0   0.585"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['A1', 'A3']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.677794</td>\n",
       "      <td>31.581974</td>\n",
       "      <td>4.748999</td>\n",
       "      <td>1.766328</td>\n",
       "      <td>7.377358</td>\n",
       "      <td>4.693759</td>\n",
       "      <td>2.224332</td>\n",
       "      <td>0.523948</td>\n",
       "      <td>0.428157</td>\n",
       "      <td>2.403483</td>\n",
       "      <td>0.457184</td>\n",
       "      <td>1.928882</td>\n",
       "      <td>184.136430</td>\n",
       "      <td>1018.103048</td>\n",
       "      <td>0.445573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.467661</td>\n",
       "      <td>11.856359</td>\n",
       "      <td>4.975215</td>\n",
       "      <td>0.430283</td>\n",
       "      <td>3.683694</td>\n",
       "      <td>1.993588</td>\n",
       "      <td>3.348856</td>\n",
       "      <td>0.499789</td>\n",
       "      <td>0.495171</td>\n",
       "      <td>4.865611</td>\n",
       "      <td>0.498525</td>\n",
       "      <td>0.299018</td>\n",
       "      <td>172.254524</td>\n",
       "      <td>5213.882348</td>\n",
       "      <td>0.497390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>22.670000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>28.670000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>37.750000</td>\n",
       "      <td>7.080000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>272.000000</td>\n",
       "      <td>395.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>80.250000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>100001.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A1          A2          A3          A4          A5          A6  \\\n",
       "count  689.000000  689.000000  689.000000  689.000000  689.000000  689.000000   \n",
       "mean     0.677794   31.581974    4.748999    1.766328    7.377358    4.693759   \n",
       "std      0.467661   11.856359    4.975215    0.430283    3.683694    1.993588   \n",
       "min      0.000000   13.750000    0.000000    1.000000    1.000000    1.000000   \n",
       "25%      0.000000   22.670000    1.000000    2.000000    4.000000    4.000000   \n",
       "50%      1.000000   28.670000    2.750000    2.000000    8.000000    4.000000   \n",
       "75%      1.000000   37.750000    7.080000    2.000000   10.000000    5.000000   \n",
       "max      1.000000   80.250000   28.000000    3.000000   14.000000    9.000000   \n",
       "\n",
       "               A7          A8          A9         A10         A11         A12  \\\n",
       "count  689.000000  689.000000  689.000000  689.000000  689.000000  689.000000   \n",
       "mean     2.224332    0.523948    0.428157    2.403483    0.457184    1.928882   \n",
       "std      3.348856    0.499789    0.495171    4.865611    0.498525    0.299018   \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000    1.000000   \n",
       "25%      0.165000    0.000000    0.000000    0.000000    0.000000    2.000000   \n",
       "50%      1.000000    1.000000    0.000000    0.000000    0.000000    2.000000   \n",
       "75%      2.625000    1.000000    1.000000    3.000000    1.000000    2.000000   \n",
       "max     28.500000    1.000000    1.000000   67.000000    1.000000    3.000000   \n",
       "\n",
       "               A13            A14       class  \n",
       "count   689.000000     689.000000  689.000000  \n",
       "mean    184.136430    1018.103048    0.445573  \n",
       "std     172.254524    5213.882348    0.497390  \n",
       "min       0.000000       1.000000    0.000000  \n",
       "25%      80.000000       1.000000    0.000000  \n",
       "50%     160.000000       6.000000    0.000000  \n",
       "75%     272.000000     395.000000    1.000000  \n",
       "max    2000.000000  100001.000000    1.000000  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA4kAAAN0CAYAAAAZIm2SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c62af4ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "scatter_matrix(data, alpha = .01, figsize = (15, 15))\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A1</th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A10</th>\n",
       "      <th>A11</th>\n",
       "      <th>A12</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.037490</td>\n",
       "      <td>-0.029562</td>\n",
       "      <td>-0.064107</td>\n",
       "      <td>-0.004410</td>\n",
       "      <td>0.053028</td>\n",
       "      <td>0.097236</td>\n",
       "      <td>0.001968</td>\n",
       "      <td>-0.062447</td>\n",
       "      <td>-0.007938</td>\n",
       "      <td>0.052961</td>\n",
       "      <td>-0.060162</td>\n",
       "      <td>0.059114</td>\n",
       "      <td>0.003580</td>\n",
       "      <td>-0.013014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2</th>\n",
       "      <td>0.037490</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203244</td>\n",
       "      <td>0.090196</td>\n",
       "      <td>-0.095044</td>\n",
       "      <td>0.028121</td>\n",
       "      <td>0.392759</td>\n",
       "      <td>0.203381</td>\n",
       "      <td>0.082758</td>\n",
       "      <td>0.185119</td>\n",
       "      <td>0.056118</td>\n",
       "      <td>0.020990</td>\n",
       "      <td>-0.077776</td>\n",
       "      <td>0.018591</td>\n",
       "      <td>0.160753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3</th>\n",
       "      <td>-0.029562</td>\n",
       "      <td>0.203244</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.090603</td>\n",
       "      <td>0.029513</td>\n",
       "      <td>0.095319</td>\n",
       "      <td>0.299678</td>\n",
       "      <td>0.246887</td>\n",
       "      <td>0.176864</td>\n",
       "      <td>0.272580</td>\n",
       "      <td>-0.015184</td>\n",
       "      <td>0.093414</td>\n",
       "      <td>-0.221722</td>\n",
       "      <td>0.123211</td>\n",
       "      <td>0.208440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A4</th>\n",
       "      <td>-0.064107</td>\n",
       "      <td>0.090196</td>\n",
       "      <td>0.090603</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.089642</td>\n",
       "      <td>0.046927</td>\n",
       "      <td>0.080794</td>\n",
       "      <td>0.130821</td>\n",
       "      <td>0.163269</td>\n",
       "      <td>0.106888</td>\n",
       "      <td>0.004111</td>\n",
       "      <td>0.006214</td>\n",
       "      <td>-0.001373</td>\n",
       "      <td>0.120061</td>\n",
       "      <td>0.195167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A5</th>\n",
       "      <td>-0.004410</td>\n",
       "      <td>-0.095044</td>\n",
       "      <td>0.029513</td>\n",
       "      <td>0.089642</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.402101</td>\n",
       "      <td>0.144595</td>\n",
       "      <td>0.310876</td>\n",
       "      <td>0.222064</td>\n",
       "      <td>0.149627</td>\n",
       "      <td>0.026223</td>\n",
       "      <td>0.070584</td>\n",
       "      <td>0.087559</td>\n",
       "      <td>0.030804</td>\n",
       "      <td>0.372965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A6</th>\n",
       "      <td>0.053028</td>\n",
       "      <td>0.028121</td>\n",
       "      <td>0.095319</td>\n",
       "      <td>0.046927</td>\n",
       "      <td>0.402101</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.240331</td>\n",
       "      <td>0.245881</td>\n",
       "      <td>0.080011</td>\n",
       "      <td>0.098617</td>\n",
       "      <td>0.094280</td>\n",
       "      <td>0.036560</td>\n",
       "      <td>0.070434</td>\n",
       "      <td>0.064865</td>\n",
       "      <td>0.246280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A7</th>\n",
       "      <td>0.097236</td>\n",
       "      <td>0.392759</td>\n",
       "      <td>0.299678</td>\n",
       "      <td>0.080794</td>\n",
       "      <td>0.144595</td>\n",
       "      <td>0.240331</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.345683</td>\n",
       "      <td>0.222869</td>\n",
       "      <td>0.322258</td>\n",
       "      <td>0.138564</td>\n",
       "      <td>-0.016232</td>\n",
       "      <td>-0.076539</td>\n",
       "      <td>0.051357</td>\n",
       "      <td>0.322423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A8</th>\n",
       "      <td>0.001968</td>\n",
       "      <td>0.203381</td>\n",
       "      <td>0.246887</td>\n",
       "      <td>0.130821</td>\n",
       "      <td>0.310876</td>\n",
       "      <td>0.245881</td>\n",
       "      <td>0.345683</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.431296</td>\n",
       "      <td>0.379150</td>\n",
       "      <td>0.093084</td>\n",
       "      <td>0.045453</td>\n",
       "      <td>-0.068195</td>\n",
       "      <td>0.090141</td>\n",
       "      <td>0.720038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A9</th>\n",
       "      <td>-0.062447</td>\n",
       "      <td>0.082758</td>\n",
       "      <td>0.176864</td>\n",
       "      <td>0.163269</td>\n",
       "      <td>0.222064</td>\n",
       "      <td>0.080011</td>\n",
       "      <td>0.222869</td>\n",
       "      <td>0.431296</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.571289</td>\n",
       "      <td>0.018433</td>\n",
       "      <td>0.176499</td>\n",
       "      <td>-0.053972</td>\n",
       "      <td>0.077741</td>\n",
       "      <td>0.457693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A10</th>\n",
       "      <td>-0.007938</td>\n",
       "      <td>0.185119</td>\n",
       "      <td>0.272580</td>\n",
       "      <td>0.106888</td>\n",
       "      <td>0.149627</td>\n",
       "      <td>0.098617</td>\n",
       "      <td>0.322258</td>\n",
       "      <td>0.379150</td>\n",
       "      <td>0.571289</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.007732</td>\n",
       "      <td>0.101672</td>\n",
       "      <td>-0.120200</td>\n",
       "      <td>0.063731</td>\n",
       "      <td>0.406076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A11</th>\n",
       "      <td>0.052961</td>\n",
       "      <td>0.056118</td>\n",
       "      <td>-0.015184</td>\n",
       "      <td>0.004111</td>\n",
       "      <td>0.026223</td>\n",
       "      <td>0.094280</td>\n",
       "      <td>0.138564</td>\n",
       "      <td>0.093084</td>\n",
       "      <td>0.018433</td>\n",
       "      <td>0.007732</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.044833</td>\n",
       "      <td>0.145174</td>\n",
       "      <td>0.019159</td>\n",
       "      <td>0.033086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A12</th>\n",
       "      <td>-0.060162</td>\n",
       "      <td>0.020990</td>\n",
       "      <td>0.093414</td>\n",
       "      <td>0.006214</td>\n",
       "      <td>0.070584</td>\n",
       "      <td>0.036560</td>\n",
       "      <td>-0.016232</td>\n",
       "      <td>0.045453</td>\n",
       "      <td>0.176499</td>\n",
       "      <td>0.101672</td>\n",
       "      <td>-0.044833</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.079135</td>\n",
       "      <td>0.139667</td>\n",
       "      <td>0.115642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A13</th>\n",
       "      <td>0.059114</td>\n",
       "      <td>-0.077776</td>\n",
       "      <td>-0.221722</td>\n",
       "      <td>-0.001373</td>\n",
       "      <td>0.087559</td>\n",
       "      <td>0.070434</td>\n",
       "      <td>-0.076539</td>\n",
       "      <td>-0.068195</td>\n",
       "      <td>-0.053972</td>\n",
       "      <td>-0.120200</td>\n",
       "      <td>0.145174</td>\n",
       "      <td>-0.079135</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.065647</td>\n",
       "      <td>-0.100683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A14</th>\n",
       "      <td>0.003580</td>\n",
       "      <td>0.018591</td>\n",
       "      <td>0.123211</td>\n",
       "      <td>0.120061</td>\n",
       "      <td>0.030804</td>\n",
       "      <td>0.064865</td>\n",
       "      <td>0.051357</td>\n",
       "      <td>0.090141</td>\n",
       "      <td>0.077741</td>\n",
       "      <td>0.063731</td>\n",
       "      <td>0.019159</td>\n",
       "      <td>0.139667</td>\n",
       "      <td>0.065647</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.175808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <td>-0.013014</td>\n",
       "      <td>0.160753</td>\n",
       "      <td>0.208440</td>\n",
       "      <td>0.195167</td>\n",
       "      <td>0.372965</td>\n",
       "      <td>0.246280</td>\n",
       "      <td>0.322423</td>\n",
       "      <td>0.720038</td>\n",
       "      <td>0.457693</td>\n",
       "      <td>0.406076</td>\n",
       "      <td>0.033086</td>\n",
       "      <td>0.115642</td>\n",
       "      <td>-0.100683</td>\n",
       "      <td>0.175808</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             A1        A2        A3        A4        A5        A6        A7  \\\n",
       "A1     1.000000  0.037490 -0.029562 -0.064107 -0.004410  0.053028  0.097236   \n",
       "A2     0.037490  1.000000  0.203244  0.090196 -0.095044  0.028121  0.392759   \n",
       "A3    -0.029562  0.203244  1.000000  0.090603  0.029513  0.095319  0.299678   \n",
       "A4    -0.064107  0.090196  0.090603  1.000000  0.089642  0.046927  0.080794   \n",
       "A5    -0.004410 -0.095044  0.029513  0.089642  1.000000  0.402101  0.144595   \n",
       "A6     0.053028  0.028121  0.095319  0.046927  0.402101  1.000000  0.240331   \n",
       "A7     0.097236  0.392759  0.299678  0.080794  0.144595  0.240331  1.000000   \n",
       "A8     0.001968  0.203381  0.246887  0.130821  0.310876  0.245881  0.345683   \n",
       "A9    -0.062447  0.082758  0.176864  0.163269  0.222064  0.080011  0.222869   \n",
       "A10   -0.007938  0.185119  0.272580  0.106888  0.149627  0.098617  0.322258   \n",
       "A11    0.052961  0.056118 -0.015184  0.004111  0.026223  0.094280  0.138564   \n",
       "A12   -0.060162  0.020990  0.093414  0.006214  0.070584  0.036560 -0.016232   \n",
       "A13    0.059114 -0.077776 -0.221722 -0.001373  0.087559  0.070434 -0.076539   \n",
       "A14    0.003580  0.018591  0.123211  0.120061  0.030804  0.064865  0.051357   \n",
       "class -0.013014  0.160753  0.208440  0.195167  0.372965  0.246280  0.322423   \n",
       "\n",
       "             A8        A9       A10       A11       A12       A13       A14  \\\n",
       "A1     0.001968 -0.062447 -0.007938  0.052961 -0.060162  0.059114  0.003580   \n",
       "A2     0.203381  0.082758  0.185119  0.056118  0.020990 -0.077776  0.018591   \n",
       "A3     0.246887  0.176864  0.272580 -0.015184  0.093414 -0.221722  0.123211   \n",
       "A4     0.130821  0.163269  0.106888  0.004111  0.006214 -0.001373  0.120061   \n",
       "A5     0.310876  0.222064  0.149627  0.026223  0.070584  0.087559  0.030804   \n",
       "A6     0.245881  0.080011  0.098617  0.094280  0.036560  0.070434  0.064865   \n",
       "A7     0.345683  0.222869  0.322258  0.138564 -0.016232 -0.076539  0.051357   \n",
       "A8     1.000000  0.431296  0.379150  0.093084  0.045453 -0.068195  0.090141   \n",
       "A9     0.431296  1.000000  0.571289  0.018433  0.176499 -0.053972  0.077741   \n",
       "A10    0.379150  0.571289  1.000000  0.007732  0.101672 -0.120200  0.063731   \n",
       "A11    0.093084  0.018433  0.007732  1.000000 -0.044833  0.145174  0.019159   \n",
       "A12    0.045453  0.176499  0.101672 -0.044833  1.000000 -0.079135  0.139667   \n",
       "A13   -0.068195 -0.053972 -0.120200  0.145174 -0.079135  1.000000  0.065647   \n",
       "A14    0.090141  0.077741  0.063731  0.019159  0.139667  0.065647  1.000000   \n",
       "class  0.720038  0.457693  0.406076  0.033086  0.115642 -0.100683  0.175808   \n",
       "\n",
       "          class  \n",
       "A1    -0.013014  \n",
       "A2     0.160753  \n",
       "A3     0.208440  \n",
       "A4     0.195167  \n",
       "A5     0.372965  \n",
       "A6     0.246280  \n",
       "A7     0.322423  \n",
       "A8     0.720038  \n",
       "A9     0.457693  \n",
       "A10    0.406076  \n",
       "A11    0.033086  \n",
       "A12    0.115642  \n",
       "A13   -0.100683  \n",
       "A14    0.175808  \n",
       "class  1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nagAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c6cba1e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['class'], data['A2'], '*', alpha = 0.05)\n",
    "plt.xlabel('class')\n",
    "#plt.xlim(0, 1)\n",
    "plt.ylabel('A2')\n",
    "plt.title('class vs A2')\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      False\n",
       "1      False\n",
       "2       True\n",
       "3       True\n",
       "4       True\n",
       "5      False\n",
       "6       True\n",
       "7      False\n",
       "8      False\n",
       "9       True\n",
       "10      True\n",
       "11     False\n",
       "12      True\n",
       "13     False\n",
       "14      True\n",
       "15      True\n",
       "16      True\n",
       "17     False\n",
       "18      True\n",
       "19     False\n",
       "20     False\n",
       "21     False\n",
       "22     False\n",
       "23     False\n",
       "24      True\n",
       "25     False\n",
       "26      True\n",
       "27      True\n",
       "28      True\n",
       "29      True\n",
       "       ...  \n",
       "659     True\n",
       "660    False\n",
       "661     True\n",
       "662     True\n",
       "663    False\n",
       "664    False\n",
       "665    False\n",
       "666    False\n",
       "667    False\n",
       "668    False\n",
       "669     True\n",
       "670     True\n",
       "671    False\n",
       "672     True\n",
       "673     True\n",
       "674    False\n",
       "675    False\n",
       "676    False\n",
       "677    False\n",
       "678     True\n",
       "679    False\n",
       "680    False\n",
       "681     True\n",
       "682    False\n",
       "683     True\n",
       "684     True\n",
       "685    False\n",
       "686     True\n",
       "687     True\n",
       "688     True\n",
       "Name: class, Length: 689, dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['class'] == 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAF3CAYAAADtkpxQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3X94XNV16P3vntHo12gkjWwsWRZiZBuDwJbtxmADHhBx\nqUnTOEEhbZxeBwotbWiavMlLuW24eeObX70EP2lyH0IpNG0TlQhSohCnaaAprsiQYIwpWLZRwNge\nD7IsG0sjaTT6NZrZ7x97xhob/5Ls0dmy1+d5/BydgywvjmbmrLPP2msrrTVCCCGEEGJ6uZwOQAgh\nhBDiYiRJmBBCCCGEAyQJE0IIIYRwgCRhQgghhBAOkCRMCCGEEMIBkoQJIYQQQjhAkjAhhBBCCAdI\nEiaEEEII4QBJwoQQQgghHCBJmBBCCCGEA/KcDuBszJ49WwcCAafDsEI8Hsfr9Todxowi52zy5JxN\nnpyzyZNzNnlyzibPiXP26quvHtVaX3Km75sRSVggEGD79u1Oh2GFtrY2GhsbnQ5jRpFzNnlyziZP\nztnkyTmbPDlnk+fEOVNKHTib75PHkUIIIYQQDpAkTAghhBDCAZKECSGEEEI4YEbUhAkhhBDCOYlE\ngs7OTkZGRpwOZdLKysro6OjIyc8uLCykpqYGj8czpb8vSZgQQgghTquzsxOfz0cgEEAp5XQ4kxKL\nxfD5fOf952qt6enpobOzk7q6uin9DHkcKYQQQojTGhkZYdasWTMuAcslpRSzZs06p9FBScKEEEII\ncUaSgL3XuZ4TScKEEEIIMSNt3LiRTZs25eRnv/rqqyxZsoSFCxfymc98Bq31ef83JAkTQgghhDjB\npz71KR577DH27NnDnj17ePbZZ8/7vyFJmBBCCCHOq/Z22LgR7rrLbNvbz/1nfv/736ehoYGlS5ey\nYcOG9/z3xx9/nGuuuYalS5fy0Y9+lKGhIQB+/OMfs3jxYpYuXcqNN94IwO7du7n22mtZtmwZDQ0N\n7Nmz57ifdejQIQYGBrjuuutQSvHJT36SZ5555tz/J06Q0yRMKfU5pdRupdQupVSLUqpQKVWnlHpZ\nKbVHKfWUUio/lzEIIUQuZS42Bw6cv4uNEDNZezts2gTRKNTUmO2mTef23ti9ezdf+9rX2LJlCzt2\n7ODb3/72e76nqamJV155hR07dlBfX893v/tdAB588EGee+45duzYwebNmwF49NFH+exnP8vrr7/O\n9u3bqampOe5nHTx48LhjNTU1HDx4cOr/A6eQsyRMKTUP+AywQmu9GHADHwceBP5Wa305EAXuzlUM\nQgiRS9kXG4/n/FxshJjpWlvB7zd/XK6Jr1tbp/4zt2zZwu23387s2bMBqKioeM/37Nq1i2AwyJIl\nS3jiiSfYvXs3AKtWreLOO+/k8ccfJ5lMAnDdddfx9a9/nQcffJADBw5QVFR03M86Wf1XLiYm5Ppx\nZB5QpJTKA4qBQ8D7gafT//17wEdyHIMQQuRE9sVGqfNzsRFipotEoKzs+GNlZeb4VGmtz5gE3Xnn\nnTz88MPs3LmTL33pS8daR3zrW9/iq1/9Ku+88w7Lli2jp6eHT3ziE2zevJmioiLWrl3Lli1bjvtZ\nNTU1dHZ2Htvv7Oykurp66v8Dp5CzJExrfRDYBEQwyVc/8CrQp7UeT39bJzAvVzEIIUQu5eJiI8RM\nV1sL/f3HH+vvN8enas2aNfzwhz+kp6cHgN7e3vd8TywWY+7cuSQSCZ544oljx/ft28fKlSv58pe/\nzOzZs3nnnXfYt28f8+fP5zOf+Qzr1q2j/YTh67lz5+Lz+di6dStaa77//e/z4Q9/eOr/A6eQs475\nSik/8GGgDugD/hX4wEm+9aRzPpVS9wD3AFRWVtLW1pabQGeYwcFBOReTJOds8uScnZ1bboHxccjL\ng4KCQa64oo3xcaivBzl9Zyavs8lz6pyVlZURi8XO6nvXrnXxf/+vh7ExTWkpDAxAX5/i4x9PEIul\npvTv19bW8vnPf55gMIjb7aahoYFHH32U0dFRPB4PsViMBx54gGuvvZZLL72Uq666isHBQWKxGP/r\nf/0v9u3bh9aam266ifnz5/PNb36Tp556Co/Hw5w5c/jc5z73nv+/TZs2cddddzE8PMwtt9zC6tWr\nT3oORkZGpvw7UbnoewGglPoYcKvW+u70/ieB64CPAVVa63Gl1HXARq312tP9rBUrVujt27fnJM6Z\npq2tjcbGRqfDmFHknE2enLOzk6kJ8/th5co2Xn65kWgU7rsPGhqcjs5+8jqbPKfOWUdHB/X19Wf9\n/e3t5rF8JGJGwJqanHtP5GrZooyTnRul1Kta6xVn+ru5XDsyAqxSShUDw8AaYDvwX8DtwJPAHcBP\nchiDEELkTEODSbhaWyGRMMnY3XdLAiZEQ4O8D85GzpIwrfXLSqmngf8GxoHXgMeAnwFPKqW+mj72\n3VzFIIQQuZa52LS1wR13OB2NEGImyeVIGFrrLwFfOuHwPuDaXP67QgghhBC2k475QgghhBAOkCRM\nCCGEEMIBkoQJIYQQQjhAkjAhhBBCzEgbN25k06ZNOfnZDzzwAJdeeiklJSU5+fkgSZgQQgghxHt8\n6EMfYtu2bTn9NyQJE0IIIcT5FQ5DczM89JDZhsPn/CO///3v09DQwNKlS9mwYcN7/vvjjz/ONddc\nw9KlS/noRz/K0NAQAD/+8Y9ZvHgxS5cu5cYbbwRg9+7dXHvttSxbtoyGhgb27Nnznp+3atUq5s6d\ne85xn44kYUIIIaZX5gJ9+PB5u0ALi4TD0NIC8ThUV5ttS8s5/Z53797N1772NbZs2cKOHTv49re/\n/Z7vaWpq4pVXXmHHjh3U19fz3e+aNqQPPvggzz33HDt27GDz5s0APProo3z2s5/l9ddfZ/v27dTU\n1Ew5tnMhSZgQQojpk32B9njOywVaWCYUMstHlJWBy2W2fr85PkVbtmzh9ttvZ/bs2QBUVFS853t2\n7dpFMBhkyZIlPPHEE+zevRswI1p33nknjz/+OMlkEoDrrruOr3/96zz44IMcOHCAoqKiKcd2LiQJ\nE0IIMX2yL9BwXi7QwjLd3XDiWo0+nzk+RVprlFKn/Z4777yThx9+mJ07d/KlL32JkZERAL71rW/x\n1a9+lXfeeYdly5bR09PDJz7xCTZv3kxRURFr165ly5YtU47tXEgSJoQQYvrk4AItLFNVBbHY8cdi\nMXN8itasWcMPf/hDenp6AOjt7X3P98RiMebOnUsikeCJJ544dnzfvn2sXLmSL3/5y8yePZt33nmH\nffv2MX/+fD7zmc+wbt062tvbpxzbuZAkTAghxPTJwQVaWCYYhGgU+vshlTLbaNQcn6Krr76aBx54\ngJtuuomlS5fy+c9//j3f85WvfIWVK1dyyy23cOWVVx47/sUvfpElS5awePFibrzxRpYuXcpTTz3F\n4sWLWbZsGb/5zW/45Cc/+Z6fd//991NTU8PQ0BA1NTVs3LhxyvGfitJan/cfer6tWLFCb9++3ekw\nrNDW1kZjY6PTYcwocs4mT87Z5Mk5O0uZmjC/n7Y5c2g8csRcoNevh0DA6eis59TrrKOjg/r6+rP/\nC+GwecTc3W0S7GDQsd9vLBbDd+Lo63l0snOjlHpVa73iTH83pwt4CyGEEMcJBEzCFQpBIgFeL9x6\nqyRgF5pAQH6nZ0GSMCGEENMrc4FuawMZPRQXMUnCsrS3Q2srRCJQWwtNTdDQ4HRUQgghhLgQSWF+\nWns7bNpkShNqasx20yZzXAghhLjYzYQa8ul2rudEkrC01lbTqsbvN73lMl+3tjodmRBCCOGswsJC\nenp6JBHLorWmp6eHwsLCKf8MeRyZFomYEbBsZWXmuBBCCHExq6mpobOzk3fffdfpUCZtZGTknBKl\n0yksLDynJY8kCUurrTWPIP3+iWP9/ea4EEIIcTHzeDzU1dU5HcaUtLW1sXz5cqfDOCl5HJnW1GSS\nsGjU9JbLfN3U5HRkQgghhLgQSRKW1tAA991nRsI6O832vvtkdqQQQgghckMeR2ZpKA3TsCAE3nSH\n39IgEHA6LCGEEEJcgGQkLCOzlEY8DtXVZtvSYo4LIYQQQpxnkoRlhELmGWRZmelRUVZm9kMhpyMT\nQgghxAVIkrCM7m44cYFPn88cF0IIIYQ4zyQJy6iqgljs+GOxmDkuhBBCCHGeSRKWEQyanhT9/aZH\nRX+/2Q8GnY5MCCGEEBcgScIyAgFYvx68XujqMtv1681xIYQQQojzTFpUZAsEJOkSQgghxLSQkTAh\nhDgX4TA0N8Phw2YrbW2EEGdJkjAhhJiq7P6CHo/0FxRCTIokYUIIMVXZ/QVB+gsKISZFkjAhhJgq\n6S8ohDgHkoQJIcRUSX9BIcQ5kCRMCCGmKru/IEh/QSHEpOQsCVNKXaGUej3rz4BS6v9RSlUopX6h\nlNqT3vpzFYMQQuRUdn/BREL6CwohJiVnSZjW+k2t9TKt9TLgfcAQ8GPgr4DntdaXA8+n94UQYmYK\nBGDDBqisNFtJwIQQZ2m6HkeuAfZqrQ8AHwa+lz7+PeAj0xSDEEIIIYQ1pqtj/seBlvTXlVrrQwBa\n60NKqTnTFMMZtbdDaytEIlBbC01N0NDgdFRCCCGEuBAprXVu/wGl8oEu4Gqt9WGlVJ/Wujzrv0e1\n1u+pC1NK3QPcA1BZWfm+J598MqdxDg/DwYMwPm7W73a5IC8P5s2DoqKc/tOTMjg4SElJidNhzChy\nziZPztnkyTmbPDlnkyfnbPKcOGc333zzq1rrFWf6vukYCfsA8N9a68Pp/cNKqbnpUbC5wJGT/SWt\n9WPAYwArVqzQjY2NOQ3yz/4MXngBSkuhsBBGRmBgAG66CR59NKf/9KS0tbWR63NxoZFzNnlyziZP\nztnkyTmbPDlnk2fzOZuOmrD1TDyKBNgM3JH++g7gJ9MQwxlt3Wp6LBYVgVJm6/OZ40IIIYQQ51tO\nkzClVDFwC9Cadfj/ALcopfak/9v/yWUMZ0upyR0XQggxRbLouRBAjpMwrfWQ1nqW1ro/61iP1nqN\n1vry9LY3lzGcrVWrTKPr4WHQ2mxjMXNcCCHEeSKLngtxjHTMT/vUp2DhQvN1pvn1woXmuBBCiPNE\nFj0X4pjpalFhvYYG+NrXpEWFEELkVHc3VFcff8zng64uZ+IRwkGShGVpaJCkSwghcqqqCvbvN0nX\nNdfAK6+YpOyyy5yOTIhpJ48jhRBCTJ+6OvPosa8P3G6zDYXMcSEuMpKECSGEmD7790MwCOXlkEya\nbTBojgtxkZHHkUIIIaZPd7cZ9VqwAAoKTAKWSklNmLgoyUiYEEKI6VNVZfr/ZIvFzHEhLjKShAkh\nhJg+wSBEoxO9gPr7zX4w6GxcQjhAkjAhhBDTJxCA9evB64VEwmzXrzfHhbjISE2YEEKI6RUImD9t\nbWDpwspCTAcZCRNCCCGEcIAkYUIIIYQQDpAkTAghxPQKh6G5GQ4fNltZvFtcpKQmLFs4bDo3d3eb\n6dLBoBSLCiHE+RQOQ0uLWbTb6zUzI1tapDhfXJRkJCwj88EQj5t1zOJxsy93aEIIcf6EQiYBKysz\n+2VlZj8UcjYuIRwgSVhG9geDyyUfDEIIkQvd3eDzHX/M5zPHhbjISBKWIR8MQgiRe9IxX4hjJAnL\nkA8GIYTIPemYL8QxUpifEQzCI4/AkSMwOmoWlp0zB+691+nIhBDiwpHpmB8KTXTMv/VWKcoX519m\nsl1+vpmFa+FkOxkJy6a12Sp1/L4QQojzJxCADRugstJsLbswigtA9mQ7j8fayXYyEpYRCsGCBfBb\nvzVxrL/fHJcPCCGEEGLmONks3Mxxi67pMhKWIYX5QogpaG+HjRvhwAGzbW93OiIhxEy5pksSliGF\n+UKISWpvh02bTF25x2O2mzZJIiaE42bINV2SsIzsGTuplMzYEUKcUWureeLh95tS0szXra1ORybE\nRW6GzMKVJCwjM2PH64WuLrOVZTSEEKcRiUyUmmSUlZnj4tTkEa7IuexremYWroXXdCnMzxYIWPcL\nEkLYq7YW3nrL3LdVVkJbm1n1bNEipyOzV+YRrt9vzlPmEe5990FDg9PRiQtK5pre1gaNjc7Gcgoy\nEiaEEFO0eDFs3Qp9fWa1s74+s794sdOR2Use4QoxQZIwIYSYol274LrroLzclJKWl5v9Xbucjsxe\n8ghXiAnyOFIIIaYoEjHtBS+/3CRgjY0mGZOE4tRqa80jSL9/4lh/vzkuxMVGRsKEEGKKamsnJl9l\nSEJxek1NJgmLRs2iJJmvm5qcjkyI6SdJmBBCTJEkFJPX0GCK8P1+M2nN75eifHHxkseRQggxRZmE\norV1IqG4+25JKM6kocH8aWuDO+5wOhohnCNJmBBiQjhs1lbLz4fmZtPYUNq2nJYkFEKIqZIkTAhh\nhMPQ0mKGc7xe81ytpcXKBodC5EzmRqS72yxxIzciIoekJkwIYYRCJgHL9A8oKzP7oZCzcQkxXTI3\nIvG46bobj5v9cNjpyMQFKqdJmFKqXCn1tFLqN0qpDqXUdUqpCqXUL5RSe9Jb/5l/khAi57q7wec7\n/pjPZ44LcTHIvhFxueRGRORcrkfCvg08q7W+ElgKdAB/BTyvtb4ceD69b4dw2NTBPPSQ2crdj7iY\nVFVBLHb8sVjMHBfiYiA3ImKa5SwJU0qVAjcC3wXQWo9prfuADwPfS3/b94CP5CqGSZFhaHGxCwZN\nHVim8VV/v9kPBp2NS4jpUlUF+/ebka+f/tRs9++XGxGRM7kszJ8PvAv8k1JqKfAq8FmgUmt9CEBr\nfUgpNSeHMZy9k9XDZI5LUaa4GAQCpgg/FDL9FrxeuPVWef3PZFJkPjl1dfAv/wKzZpklEPr6YO9e\naxd/FjOf0lrn5gcrtQLYCtygtX5ZKfVtYAD4C611edb3RbXW76kLU0rdA9wDUFlZ+b4nn3wyJ3Ee\nc/gweDzvPZ5IQGVlbv/tSRgcHKSkpMTpMGYUOWeTJ+ds8qw7Z2Nj0NsLbrf5k0yaPxUVpgWJBaw7\nZ729MDpqPveTSXPePB4oKDDnzQLWnbMZwIlzdvPNN7+qtV5xpu/L5UhYJ9CptX45vf80pv7rsFJq\nbnoUbC5w5GR/WWv9GPAYwIoVK3Rjru9EmpvNo5fslWX7+81ogEV3QW1tbeT8XFxg5JxNnpyzybPu\nnDU3m7KKsjIYHzfH+vtNgrFhg7OxpVl3zh56yJSjeDwTN+WplBkNs2QZBOvO2Qxg8znLWU2Y1rob\neEcpdUX60BrgDWAzkGlpeAfwk1zFMCnZ9TCplNTDCCFmNikynzyZnCKmWa6btf4F8IRSKh/YB/wR\nJvH7oVLqbiACfCzHMZyd7HqYri7zppN6GCHEGbS3m2WL6upg40YzYGLFskWZhCJ7dF8SitMLBuGR\nR+DIEfNYsqAA5syBe+91OjJxgcppEqa1fh042TPRNbn8d6csEJCka7Kk8FdcxNrbYdMmM6dn0SIz\neL5pkyULUgeDZoY3mBGwWMwEeOutzsZlu0wx/uAglJRYUz8nLkzSMV9MnbT1EBe51laTgPn9oNTE\n162tTkfGxOi+12tG971eWYLqTFpbzSjYokVwww1me+SIJb9QcSGStSPF1ElbDzGdLBx1jUSgpub4\nY2Vl5rgVZHR/crZtM59pxcVmv7jY1Ahv2+ZsXOKCJSNhYuqk8FdMF0tHXWtrJ3rbZvT3m+NihlLq\n9PtCnEeShGVpbzeFtXfdZbbt7U5HZDmZSSSmi6Vr+jU1mTKraBS0nvjakm4G9sosEXf4sF1LxK1c\naXqFDQ2ZEbChIbO/cqXTkYkLlCRhaZkC22jUPF7IFNhKInYa0tZDTBdLR10bGkwRvt9v2m/5/ZYU\n5dsse1TT47FmVBOA224zdWDJpEm+kkmzf9ttTkcmLlBSE5aWXWALE9vWVvlAPaVAgI5l6+l4LESi\nswtPTRX199xKvdSgiPPN4nYLDaVhGhaEaMvP544FzVAaBAJOh2Uvm2tJAwHTjsKy2kNx4ZIkLC0S\nMTdlbW1mQKesDK64wqICWwu1t8OmlgD+2gBlS8x5e6YF7psnias4z2zt35QZ1fH7zezDaNTsyyzE\nU+vuNnV92Xw+M4PTBjKZQUwjeRyZVlAAv/wlDA9DaanZ/vKX5rgVLKyhyB49dLksm55vs8zv8qGH\nrPldzgiZdW4zhdI5Wvd2Uk42qmNBrZrVpJZUiGMkCUs71ee5DZ/zttZQRCLHPx0Cy6bn28jSWX7W\nC4VgwQJYuxY+9CGzXbDA+WSnuxtGRkwc/f1mOzLieK2a1bJrSUFqScVFTZKwtLExuPFGKCqCgQGz\nvfFGc9xxlt5ty/T8KbB0lp/1LC3Mx+02NQyjo5CXZ7Ztbea4OLnsJrKJhDSRFRc1qQlLq60FwmHW\n+kN4Pd3ES6poH7WkINPSGoqmJjODFEwukbmhvftuR8OakGnumZ9vHvvZUGBr6e/SerYW5mf3kMoe\nNpfeUqeXqbtqa4PGRmdjEcJBMhKW9gcrw1y1o4VEX5yBkmoSfXGu2tHCH6wMOx2atTUU2dPzOzst\nm54fDnPob1v46ZNxjvZ5+OmTcQ79rQWP/Sz9XVrP1nYo4+MmiSgoMO0MCgrM/vi4s3EJIWYEGQlL\nqz8awvdBP1s7yujpgVmzylh1A9QcDeH4dPPshXjnzJm4AFmwEG9DgyVJ1wkiT4R4fqsfl7+M0jzo\nTZbx7FZY80SI2gcCzgUmiypPTeYRVihkRg2rqsw5c3pks6rK1PUFgyYBCwbN+3PWLGfjEkLMCJKE\nZXR3U1Nfze1XZx1LWfKYKPsClKmhsOECZLGOtm5c5dV4vWbf64W49tHR1kXtAw4GZmsyMRPY2DrA\n4hskIYT9JAnLsLXmJENqKCYlMlpFZUmMMSZ+n353jMiwBb9PG5MJMTVygySEOAdSE5Zha82JmJLB\n5UFUX5SCETN9s2CkH9UXZXC5/D7FeRYIwIYNUFlptpKAnVFmnd4DB2SdXnFxkyQsIxAwCddrr8EP\nfmC2NsymE1Oy5u4AWyrXE014cScTRBNetlSuZ83dAadDE+Kilr1Or8cj6/SKi5skYRmZdgbLl8Mn\nPmG2oZDzs+nElDQ0wD33QInXTFor8Zp9GycRiBnOwtUsbJa90oZSstKGuLhJEpYhTTQvLOEw9a+3\n0LQ2jr/SQ9PaOPWvW9CiQlxYLF3Nwmay0oYQEyQJy7C1I7eYGktXGRAXGHmdTVptLbgiYRp2NOMd\nPEzDjmZckbCstCEuSpKEZUgTzQuLJNViOsjrbNKyG2MnXR67GmMLMc2kRUVGMMihv21h+9vQNeij\nuiTGioVR5n5O+v3MSLa3HBEXBnmdTVp2Y+xkEtwVFjXGFmKayUhYWvtAgG9EzGy6yzxdRBNevhFZ\nT/tAwOnQxFRktxwBaTkickNeZ5PX3U1NvY/bb4fZs+H226GmXkYPxcVJRsLSWlshVRtgnz/AvvSx\nVNQclxl1M5A00RTTQV5nkyejh0IcIyNhaTJj5wIkTTSFsI+MHgpxjCRhabW1E58JGf39WDNjRzpM\nT4H0b5o0eZ1NkrSomLzM6KHXOzF6uH693CSJi5IkYWlNTbB3L/z85/DMM2a7d6857jTpMD0FcnGc\nNHmdTUEoRGfcz9O/KOPoUXj6F2V0xqVFxRnJKLUQgCRhx1Hq9PtOkQ7TUyD9myZNXmeT1/VaN5vb\nfMTj4HabXH9zm4+u16TIXAhxZlKYn9baatbtPnwYBgagtNTM3LGhMD8SgZqa44/ZUq/W3m7OUSRi\nHt02NTl/vgAz06q6+vhjPh90dTkTzwxg8+vMVlv3VzHLE8PtNcm+1wuFozG27q/CgkF0IYTlZCQs\n7YUX4Fe/gpERc60eGTH7L7zgdGT21qtlP76qqbHs8ZU03500W19nNnspL8jckb3Mf/s5Ckf7mf/2\nc8wd2ctLeVJkLoQ4M0nC0vbvN3UwhYXmUUxhodnfv9/pyMzoUjRq/mg98bXT9WqtrTDfFeamSDM3\nvPQQN0Wame8K2/H4SmZgTZqtrzObVVfD2KiCTOmCMvsnDsIKIRwwAyZnSRKWlkyadbsTCXMBSiTM\nfjLpdGTm8d5995n6nETCbO+7z/nHfvHdYa6PtOAZixPzVeMZi3N9pIX47rCzgYHMwJoCW19nNvvo\nnBDveOazs2otIwVl7Kxayzue+Xx0jtQeCuGoGTI5S2rC0hYsMOVCY2PmUWRhoblu23JH29Bg/rS1\nwR13OB2Ncd14iKj24y409TCjhWUMDZnjViw/EgiYP21t0NjobCwzhI2vM5vV5nez5iPVbNtubti8\nXrj2Jh+1eVJ7KISjTjY5K3PcoptxGQlL+/SnYWgIhofNSNjwsNn/9Kedjsxeq+q66UmYmWFamxuN\nnoSPVXUyM0xcJKqqqPXHjluCp9YvtYdCOK672xR4Z/PZtzyWJGFpixbB/PmQl2cexeTlmf1Fi5yO\nzF7Vy6tY1xjD64XeXjMKsK4xRvVyuQCJi4TUHgphpxkyOSunjyOVUmEgBiSBca31CqVUBfAU5nlV\nGPh9rXU0l3GcjdZWWL4c3v/+iWNRWTvy9IJBajpbuP0WzB1GLJa+AN3qdGRiijItR+rqTMd8a1qO\n2ErWjhTCTsGgqQEDmDNn4gbpVruuT9MxEnaz1nqZ1npFev+vgOe11pcDz6f3HSdrR05BdvF7V5cU\nv89w7e3wT18KM//XzfhHDzP/183805fCdrQcsZl0fxfCPjNkcpYThfkfBhrTX38PaAP+pwNxHKe2\n1iTJfv/EMemRdBYyxe82CofNCEV+vpmeHAzaG6sFnv9umPcfbgG/n3G3F78nyvsPt/D8d9fT8O2A\n0+EJIcTkzIDJWblOwjTwH0opDfy91voxoFJrfQhAa31IKTUnxzGclaYm+MIX4N13YXQUCgrgkkvg\n6193OjLLZRKd7m7zrN2WRCczPdnvN3dA0ajZt/BOyBYlr4XQ5X7GCstwY2a75peb41bMdhVCiAuM\n0lrn7ocrVa217konWr8A/gLYrLUuz/qeqNbaf5K/ew9wD0BlZeX7nnzyyZzFCWY25MGDMD5uli9y\nuUxx/rzXaMwLAAAgAElEQVR5UFSU0396UgYHBykpKXE6DGNszFTku93mTzJp/lRUmNEnJ/X2mljc\nbgZdLkpSqWP7VFQ4G5ulBvYcJoEHlwtUmQvdnyKVAg8JSi+vdDo861n13pwh5JxNnpyzyXPinN18\n882vZpVhnVJOR8K01l3p7RGl1I+Ba4HDSqm56VGwucCRU/zdx4DHAFasWKEbczyUuHEj7NljErH+\nflMPNm8eXH65+W+2aGtrI9fn4qw1N5u+FGVlJnsFc/ISCVMb46SHHjJN3lwu2goKaBwdNdl1V5fj\nLeBtXW8z8qtmnt8cxeUvo/RjBQz86yipaD9r1nmp/ZNGp8OznlXvzRlCztnkyTmbPJvPWc4K85VS\nXqWUL/M18DvALmAzkGkDeQfwk1zFMBmvvw47d5oRsdJSs9250xwXp2BzHxZLpyfbvN5m7R8GuXVV\nlAp3P8lxqHD3c+uqKLV/KO0WhBAiF3I5ElYJ/Fgplfl3fqC1flYp9QrwQ6XU3UAE+FgOYzhrfX2m\nFmxgYKJjfkGBOS5OIZPoZE8rtSDRAUxt2ne+Y4r8brrJrMR+ySXw53/uaFitraZMLTMBJLO1ohVK\nIMDcz63nQ6EQbfkJGj/uNe1GpIbu9GQCyIXF1jpXcUHK2UiY1nqf1npp+s/VWuuvpY/3aK3XaK0v\nT297cxXDZCgFR46YEbD8fLM9csQcF6eQ3agylbKvUeWJvzwLfpnWt0Kxud3Ciy/Cn/4pfPCDZvvi\ni05HNGPWpxNnKfv3WV0tv0+Rc9IxP01rM1BSVGTqzYuKzH4O5y3MfIEAHcvW0/qcl6e+1UXrc146\nllky+zAUMkserF1rspy1a81+yNmFlWtrJ5qrZ1jVCiUcNqM5hw+brS0XnxdfhK98xYy0XnaZ2X7l\nK84nYidbn87vd/x1doyNiavNsn+fLpd9v09xwZEkLK283DyCrKyEK64w28JCc1ycXHs73P9IgAe7\nNvA3ib/kwa4N3P9IwIr6Jrq7zXPlUMhkOaGQ2Xe4Xq2pyQwWRqNm8DDztcNzBQybR3Wam2HWLPPH\n7Z74urnZ2bhsrou0NXG1mc2/T3FBkiQsbdkyWLzYjIANDJjt4sXmuDi5v/s7ePtt83VmIODtt81x\nx+XlmQZ9o6Pmoj06avbznOhPPKGhAe67z9xcd3aa7X33WVAPBnaP6nR2mpGJ3/wG/vu/zdblMsed\nVFVl4vmHfzCjh//wD2bfhrpIWxNXm1k6oUdMTXu76W5w4IDZWjFAcAJnr0gWaWoys9SWLjXXnkx5\nkxUjFJbautXcJGb6qBUVmce3W7c6Gxdw/HPk7FowC54vNzRYknSdqLvb1MFk8/lMWw+nlZaa6cqZ\n5rtjY2b/qqucjauwEDZvNh8abrf54Ni8GVatcjYuMAnqZZcdf6y83FyRnGbrZIbs9Qaz18O1bL1B\ncWaZZdiWx0OUV+ejft3MP+0I8kf/O2DV56+MhKVZPUJhqVPlMxbkOaYxa2OjmeI6Pm62jY3muDg5\nm0cBFi40idfYmHmBZb5euNDZuP7zP+mrruftXj+jw5q3e/30VdfDf/6ns3GB6YFy4vTuvj5z3Ek2\nP/YOBEwi9tpr8IMfmK0tCaKYlMwybH5PnKTbg98TTy/DFnY6tOOcdiRMKVUKXKK13nvC8QattYUD\ne+fG2hEKS61aBXt+EWZ1T4hK3c1hVcWLBLn8loDToZnEIR43H6AFBWbb328ex4iTyx4FmDNnYjjY\nhlGA0lL4/d839UxHj5pVD265BRzuHN7/RifbDl9Goc/NeH4xXb4r2XcoycrUAcrO/Ndza8MGBu7/\nCvvb4d3xci7J66OutIfSb9zrbFwne+ydOe50spMZoVu+HG680dyEhEImcXU6NjEpM2UZtlOOhCml\nfh/4DfAjpdRupdQ1Wf/5n3MdmLDfZz8cZoOnhaJknHfGqylKxtngaeGzHw47Hdrx7TPAvvYZNgoE\nzNqaXq9Z9cDrtWetzaoqmDsX/viP4f77zXbuXMdH6d4aqqGCPgoKAGXy/Qr6eGvI4dEmoL10Nd8q\n+SID+KhJHmAAH98q+SLtpaudDczm4neZHXnBqC3oJpo8/nUWTfqoLbDgdZbldCNhXwDel15e6Fqg\nWSn1Ba11K+B8wyXhuPqjIXy3+9naUUZPD8yaVcaqeqg5asGdRiahCIUmEopbpfHoGQUC5k9bm3l8\nawtLa3X+Y84GPtb3FYaHAK0pHOqhKNnDv865l2vO+Ldzq7UVovWref76iaQrGrWgMbDNTZ5trou0\nmYUNbusbq+jaHCOuyijFPBhJ9cWoX2fB6yzL6ZIwt9b6EIDWeptS6mbg35RSNYANVT/Cad3d1NRX\nc/vVWcdSFn1g2ZpQiMnLTqq7uswHvQVJdWLlan5W/EVWh5txJ8cYK/Cx5Yp7SSxxeLQJ0wD4xPIv\nKxoD2/zY2+YE0VaZGj+/3ySwsZjZd3gUvfYPg9x6pIXtb8Po+Bwq3P2sWBVl7h9a8DrLcrokLKaU\nWpCpB0uPiN0MtAJXn+bviYuFfGCJ6ZRJqi3S1AT/tKOGjlmrKc/Pp2PWaiKpGv7IglnVtbUmt8ks\njQWWNAa2eZTa0hFXq9la4zdDlmE73ezIT53437XWA8CXgYO5DErMELYvW2SrTFf6hx6yqyu9mLSG\n0jD315oZWGPazMC6v7aFhtKw06HZ3RjY1uWxZHbk5Nlc42fr6yzLKZMwrfUOrfUeAKXUMqXUN5RS\nYeB/A387TfEJm2UXcnd12VXIbStZm+7CEgoxt97Ph/5HGbMvgQ/9jzLm1ttRyC1td6Yge3bkJz5h\ntqGQvD9Px+bWNjPAKR9HKqUWAR8H1gM9wFOA0lrfPE2xiZnAwkdEVrN16F5MjeWF3NJ2Z5Lk/Tl5\n8gj3nJyuJuw3QAj4kNb6bQCl1OemJSohLlSWX7TFJFVV0dkRY2tHGayFp5+DVfUxaq6QUYAZqbub\nyHg1235BesY3XLvCR21M3p+nZOmkmZnidDVhHwW6gf9SSj2ulFqDtKYQM4iV64bJ0P0FpWN2kF//\nLEqytx+3G5K9/fz6Z1E6Zktd5EwUGavi+WdixOOmH3A8Ds8/EyMyJu9PkRunqwn7sdb6D4ArgTbg\nc0ClUurvlFK/M03xCTEl7e1mLdBo1KyMEo2afccTMZnMcEF56uUAL166nrcPeRmLJ3j7kJcXL13P\nUy8HnA5NTMGPjgSZ7Y5S4e5HkaLC3c9sd5QfHZH35ylJnes5OePakVrruNb6Ca317wE1wOvAX+U8\nMiHOQWurKe3w+8363ZmvW1sdDkwmM1xQXn8d2sIBNpdtoK+gks1lG2gLB3j9dacjE1OxMxbgrfet\nJ5HvxRfrIpHv5a33rWdnLOB0aPaSVQbOyWnXjjyR1roX+Pv0HzGN2ttNAlFXZx6tNTVZUnBrYadk\nsLhRJchkhinKvAciEdPryob3QF8fzEuEWT0WoiKRz7r+Zl5UQfr6As4GJqakthYi0QCxpYFjx6JR\nC3qr2UzqXM/JGUfChPOsfbRm8TB0be3EspEZVjSqFFOS/R6oqbHnPXC5J8zaaAvukTjjyoN7JM7a\naAuXe8LOBiampKkJXJEw83/dzKoXH2L+r5txRcJ29FazldS5nhNJwmYAax+tWTwMnd2oUmvLGlWK\nSWtthfmuMDdFmrnhpYe4KdLMfFfY8ffAB0pC+C71M1ZcRioFY8Vl+C7184ES598DYvKym+8eSFRb\n1XzXWlLnek4kCZsBIpHjVwYCSx6tWdwpObtRZSIhjSpnuvjuMNdHWvCMxYn5qvGMxbk+0kJ8d9jR\nuFbVdTNa4OOyWiguhstqYbTAx6o6598DYgqymu/+6Z+5rGq+ay2pcz0nk6oJE86wdg04y9eOzDSq\nbGuDO+5wOhpxLq4bDxHVftyF5rU2WljG0JA5DgHH4qpeXsW6YtMnLJk01581K2JUS5+wmUnqm6ZG\n6lynTJKwGaCpydS/wPGP1u6+29m4CAbZ+UAL//EKHOz3Ma8sxu9cE2XJ16RTsji/VtV188y2arxx\nM+I0NATxhI+P1Dl8cQwGqels4fZboG32HBpvyTyKkffAjFRVxaGX9vPOy10ke/pwzyrn0pXVzF15\nmdORiQuUPI6cAWx9tPb09gCf+uV6eke8LCjuonfEy6d+uZ6ntwecDUxccKqXV7GuMYbXC729ZsRp\nXWOM6uUOjzhlL/jc2ysLPs9we5J1HGkN4ervQ1WU4+rv40hriD3JOqdDs1s4DM3N8NBDZmvB5KyZ\nQpKwGaKhwbSmuOwys3U6AQN4+GF4tzjAE64NfGX4L3nCtYF3iwM8/LDTkYmpsnKVAYBgEHcsSsFI\nP+gUBSP9uGMWFP9mL/hcUSELPs9wO3+yn3fnLMafOMylnS/hTxzm3TmL2fmT/U6HZi+LZ8nPBJKE\niSnbt888eUkkoKDAbKNRc1zMPNa2QgHaBwJ8I7KeaMLLZZ4uogkv34isp30g4GxgJ1vw2ZIZwoCM\nUEySZ28Hc8Y66SsP0Hnp9fSVB5gz1olnb4fTodnL4lnyM4HUhIkpc7lMjZrHY/Y9HhgbM8fFzHOy\nViiZ406PvLa2Qqo2wD5/gEyOn4paEFvWgs+ZBbytWfA5M0Lh95sRiljM7MvMtVOq8vQwNuImWVIM\nwLinGB0fpqqox+HILCaTGc6JJGFiyurqoOfVMKtHQ1S7uulKVfEiQWYtDjgdmpgCm1cZsDW2yFgV\nz2+O4fKXUeqGgfSCz2vWVeH05GVCIUgmzVBmf785YdXV5rgkYSdVu6yCzmcP4h/spMCVYDTlIaZK\nuTQohfmnZPMs+Uy5QH6+GQm2sF5TxizElK1rCPO5uaaxYSTd2PBzc1tY1xB2OjQxBTavMmBrbD86\nEuTSxF6WHHqOwtF+lhx6jksTe+1Y8PmNN2DnThgdNaNho6Nm/403nI7MWpWL57Dw0lE8HsXomMLj\nUSy8dJTKxXOcDs1etjZrDYfhO9+B558306mff97sW/ZIXpIwMWUfnROCcj+LrinjlrUuFl1TBuV+\nc9wC1haZW6qpCfbuhZ//3HyG/vznZt+GVQayV0BIpexZAaGrC/ILFKj0AWX2rXgS09sLbrfp6aGU\n2brd5rg4Oa0pq/JS99sLWfT7v0Xdby+krMpr6i7EydnarLW1FfbsMa/5vDyz3bPHgqVmjiePI8WU\n1eZ384GlA8R/8a+4+3tJllXgveV6qvJLnQ7tWJG53w+LFk0UmdvQ2sNmSp1+3ymZNi3ZC3jffbfz\nv8vrxkP061IKPYOAqSHq16WON5EFYNYss8L40BAUFsLIiHk8OWuWs3HZLJmEJUvg1782yWpFBVx/\nvTkuTs3GZq3btpkLQLGp76O42NzBbdvmbFwnkCRshmhvNxegujozqtPU5PwFiP5+ql5shbnlsHC2\nmZr84o9gjvNDJzYXmduqtRXmz4f3vc+crw98wCSvtpyzzAoINrmhooN9/72XpLeElHKTjI8yO76T\n+VcPOR0a1NdzaKA43Xg0mm48WsfceqlvOqW8PNi6FcbHoajIbLduhd/9Xacjs1um9qq729SC2VJ7\nZetdZRZJwrJkEp3MnbYViQ4Wj+rs22emRObnmxd3fr7Zt6BHha2F3Daz/ZzZ+P6szOvBc5WbPT3F\npFIKV0kxl182TEWe87PpOmYH2fmrTnR5A64aH6m+GOFfRVnywSD1Tgdna8H00aOwf//xF2utzXFx\ncrbOwl25kr7NL7C3TxG7UvPqi0MsKO+lfN1NzsV0ElITlpbdI6mmxq4eSScb1fH7LXi03d9Pb00D\nb0c8vLUjztsRD701De+toHaArYXcNrP5nFn7/qyooKI0ycolQ/hKYeWSISpKk+YxlsOeejnAG0vX\n4yn3UjrYhafcyxtL1/PUywFnA8tu7unx2NXcc8cO02NncNC8yAYHzf6OHU5HZi9L+4R1XHkbocOL\nGBtJkkeSsZEkocOL6LjyNkfjOpGMhKVlJzpg1+MrW0cojhbVsPPXMd5NXkkyCe44XPJaD0uur2G2\ns6HZu96mxWw+Z62t5snQjh3Hd1tw/P151VWmOd7rr5sLdjgMy5aZ4w6LRKCmNkC7K3DsWCoFnU6P\nbJ6swW3muNOjYQcPmto5v98UcieT5vd68KCzcdnM0j5hT70cgGvvpaE/RF5+PoeuXEN7WZBXXw6w\n8QOOhnacnI+EKaXcSqnXlFL/lt6vU0q9rJTao5R6SimVn+sYzkYkcnybE7Aj0QF7Ryj+cWwDef09\nlI73kO9JUjreQ15/D/84tsHZwDAX5nXrzEW7t9ds161zPqG2ma1rlILJcXbtguFhKC012127zHFH\n1dWZQCorzaywykqzX+f8WoO2fm7Q3W0SnVDIBBQKmf3ubocDSxsfN3EdPWq24+NOR2S3TJ+wbBb0\nCYtETIPn9qUbiJdU0r50A6nagBXX9GzT8Tjys0D2mg8PAn+rtb4ciAIW3Gdb/IHF8dPzs0conJ6e\n/4PIav655ouM5vuYO3aA0Xwf/1zzRX4QWe1sYJjHVJs3w9Kl5snQ0qVm3/HHV5ZrKA2zcUEzlxUe\nZuOCZhpKw06HBJhJfi6XqZVWymxdLnPcUfv3m3qm8nIzalJebvb3O7/WoK1tPcjLg7Y207fM7Tbb\ntjZz3GkVFeb3OD5uTtr4uNm34PGytSztE2bzNT1bTpMwpVQN8EHgH9L7Cng/8HT6W74HfCSXMZwt\naz+wsHeEQil42bOazxb8PU2en/HZgr/nZc9qKyagWFtHZzOLmxuWl5vSoT174De/Mdt43Bx3VHe3\nGfUKBs3QeTBo9i0Y1cn+3OjstOdz47ieWycWwDttzhwoKZl4FOl2m/050qz1lCztE2br4MWJcn3r\n8S3gfsCX3p8F9GmtM+O7ncC8HMdwVjKPrx5+2Dz+nzcPPv1pCz6w0jLT89va4I47nI7GuPxyePZZ\n036loMA8UejthVtvdToye+vorJZpbjhr1nubG37+846GVlU1URuduVaPjzv+xAOqqjj00n7e2dpF\n/M5r2PbPr3DpqmrmrrSjDURDaZiGBSHwplsHlAZxvH9ZMgmNjfDWW+aXWFBg9hMJZ+MCM+LldptE\nwus1x5SSkbAzsbBPWHZvwczghQ29BU+kdI7uPpRSvwf8rtb6XqVUI3Af8EfAS1rrhenvuRT4d631\nkpP8/XuAewAqKyvf9+STT+YkzozhYTh82Lz/MjdByaQp8Sgqyuk/PSmDg4OUlJQ4HQZgEpr+fnNR\n1Np8Villkh2nh3wPHTKf73l5UFAwyOhoybH9uXOdjc1a+/aZZ3xuN4NeLyXxuHkTpFKmgZiDIhEY\n7hujODVInk4wrjwMuUooKs939LU22jNI6uAhtCuPRGUpnsMDqNQ4rnlzKZjl8Pt0bGyia372h1pF\nhWkN4ZTe3mOjTIMuFyWp1MSok9PJTiRizpvWEzFl2u84/aGWZtM14JixMTOBIZEwM15LSpx9jZ3A\niXN28803v6q1XnGm78vlSNgNwDql1O8ChUApZmSsXCmVlx4NqwFOOoVCa/0Y8BjAihUrdGNjYw5D\nNQ1Qo9GJWZEwsb9xY07/6Ulpa2sj1+fibN11FwwMwPbtpg7T54MVK0zh9D/+o7OxZfdWW7myjZdf\nbiQateRxjK0efdQMa3q9tF17LY3btplnfkNDkOOboDP5k1vCXPPKd8gbfZf81Bi48lEFlxC65s95\n/BcBx+Jqva0ZT8ebzO1+jaP/3+3M/vLTHKpaTqK+kLU//j3H4gJM/6143NwVZYrL+/vNhXKDg5Nn\nsvpKtc2ZQ+ORI+bD1oJHWPz1X5ubkZISc/c9PGySi/nz4ZOfdDa2NJuuAcDxfcJ8PvO7fPNNO36f\n6X50bfn5NGbq1JyO6QQ5qwnTWv+11rpGax0APg5s0Vr/IfBfwO3pb7sD+EmuYpgMm2dH2io/H3bv\nNqOFixeb7e7ddtwAyezIKVi5kr79vbz64hCxAdPcsG9/L6xc6XRkNOxtZWFqD74yN8yqwFfmZmFq\nDw17nS3yS+7qIK/rHQ56AozkeTnoCZDX9Q7JXR1n/su51t1tLorZfD7n69Wya4gSCWtqiADTWmTJ\nEvOINBo12yVLrGg5Yq1QyIwatrfDz35mtsmk433CrO5Hl8WJ6Sj/E3hSKfVV4DXguw7E8B61tab8\n5eDBiT5E8+aZuidxcqcqwLehML+9HX71RJi78kOU5+dzV34zv3oiyKJFAUnETqHjytt4+/FDzNZH\njmtuuPDK2xzvsN4wuo1et5+ELsatYEgXk3CnaBh1dh24/MEetMvNeH4xbhTj+cV4EsPkDzrfMZ+q\nKjNLs6vr+OZql1lQr5apIWprM/VgtggGzSyGhgaTsMZiVsz0s1pHB9FX97LvSAnRET/+whHmz9mJ\nf8jhpbts7keXZVo65mut27TWv5f+ep/W+lqt9UKt9ce01qPTEcOZLF4ML71kprz7fGb70kvmuDi5\n0VG48UYzaj8wYLY33miOO+3574Z5/+EW/J44SbcHvyfO+w+38Px3w06HZq2nXg7w6rX3cujKNSTy\nizl05RpevfZe5zusYz5Ly8sV7jxIjIE7z+xnlw84YbSkgtR4ktHoEOPjMBodMvslFhRy19WZC05f\nn7kA9fWZfQt6mFnL0pl+Njv6Vg9vvOkmniqmpMRFPFXMG2+6OfqWwzcito4En8CCxix22LULVq2a\nuGksLzcj0Lt2we23n/nvX4xqa81NYvaNbDRqR+F7yWshkmNJ9GvtuG65hvHX2lHV1ZS8FsLx2WGW\nyu6wfkVJG28ubbSjwzow69aVjD31AoVlCuUtRMdH8MR6mXWrs+vADV56FXsPeZmV6OISPc5gooAD\nBXVUXmrBaFOmh1lXl0nAMh9q+/fDaud7+VnLwpl+Ntt9qIISTx+4hhinCK9rGDxJdh+qwNF3Z/ZI\n8DXXwCuv2DMSnEWSsLRIBBYuNAtkZ6RSUhN2OtnL3JSVTfTos2GZm5rYG6Te3sd4YQnalYcrMUre\nmzupWRh3OjRrZZLq7NElW5obzvv0beQdPcTB148werSXgrIC5q1ZROWnnV0HbnthkEsSnewpbKAo\nr4w9hQ0Uj0XZXhjkLkcjY6KH2YIFE8dSKceXkxEXlrfcVxGY56VssIuikSgjBWX0z6sjnLrM2SSs\nrg7+5V9Myx2329yI7N1r1+NvJAk7xuYLkK2y+7BEIuZc2dKHpWi0lwHcjLqKyQdGXcUohvGO9jod\nmrVsXjuSQIDKjfdSGQqZ5KKqyoqZTi8dCjC7dj1XR0PkpRIkCrz8uupWjh5yNi5gYjmZ7BlHFiwn\nY7uOn4fpeCxEorMbT00V9fcEqf9AwOmwrDW4PMjIK52MVDYwWuCjYNTU0Q1e43AdXfZIcGY1CwtH\ngiUJS7N5VAc4NtWW/Hwz9dyCCxBMNJG1TSxvFlWX9PHu8BBjWuN1DXHJJUkO581yOjRrWd/c0MLH\nRFrDiZ0WNXY0fycYNLPB4Pgicwu6Kbe3m9dZXZ1pAdTUZMfrrOPnYXZ+oQVd7sdVU02iL8bOL7QA\n6yURO4U1dwf4p8h6lsdDzBnt4oiritcqb+WP7g44G1j2SHBBgXk/WDgSLElYms2jOsf1YfF6zQdp\nS4sVBaOZD9PMObPlwzSxoJ7Dh4qpTHbR40kyq6aAw+46EnPtqgewjY0rMxyTuRGxaCTsurlhKre0\nEM/3M668eEajrI60cPj963G89jAQMOeoudnM+KupMf3BLPjMyPTwW7TIfJxt2mRHD7+Ox0Locj/u\nCjN66K4oI5k+LknYyTU0wA1/GODhhwMTq83c4/zvcqaMBE/L7MiZoqHB3JX94z+areMvooyTTbX1\n+x3vw5L5MI1Gzed75sPUhkWy6+8JMjzqZn9JAyMFZewvaWB41E39PTLVfEbK7vlTXW1Nz59rx0IM\nF/o5migjmYKjiTKGC/1cO+ZwjySYSFqXL4dPfMJsQyHHz5nN67omOrtxlR8/o85V7iPRadeMOpu0\nt8PmzbB0qRkXWLrU7Dt+HcheWBysWVj8RJKEzQSWTrXN/jB1uez6MK3/QIAlX1+Pp9wLYwk85V6W\nfF0eKcxY2TciLpc1NyJ0d5Mo9FFUZMIqKoJEofPvTcDac2ZzY2xPTRWpvthxx1J9MTw1do2e2MTa\n64DNTYGzyOPILE8//d4FvK1oT2HpsGokYhoRt7VN9IK84go7PkwB6uuhvgna8qGxCRzvOCqmrrvb\njIBl8/kcr+/oSlZRx35qirrIU9dwS9ErdCar6Upa8Njb0nNWWwuEwzT0h8ibl0/Djmbay4LUWnBx\nrL8nyNv/7yPM7j5CsWuUoVQBR9UcFt5/r9OhWSsSMU9CstmSVFvbFDiLjISlPf003H+/mcU6d67Z\n3n+/Oe64YJBDHVF++i/9HH0Xfvov/RzqcH5YNT8ffvlLs7xaaanZ/vKXdixbNFOWrBBnKXMjks2C\nG5GBWXUsj4coGu0jqdwUjfaxPB5iYJYFDVGrqjj00n62fTPESw/8lG3fDHHopf2On7M/WBnmqh0t\nJPriJF0eEn1xrtrRwh+sDDsaF5gbt8sv10SjcOCAIho1+/VyA3dKtbUTT/wypLPA2ZMkLO3hhyEv\nzyRfb71ltnl55rjT2gcCfCOynmjCS75KEE14+UZkPe0DAUfjUgpGRkzN75tvmu3IiB3LFtlaRyem\nKLu+I5Wypr5j5Zz99M5bwuzkYQrH48xOHqZ33hJWztnvaFwAe5J1RJ4I0bmrj13vlNO5q4/IEyH2\nJJ1NEOuPhrj+g6b4PZk0xe/Xf9BP/VHn35uRJ0K81L2Ad5etZfR3PsS7y9byUvcCIk84H5utmpom\n2tmkUhNfNzU5HdnMIElY2t695nM9kTCzWRMJs793r9ORmWfrqdoA+67fQNxXyb7rN5CqDTj+zP3Q\nIZOoZsvLM8cdZ2kdnZgiS5eTuaGig5pYB6XeBC6PotSboCbWwQ0Vzi/g/ewj+3k2HqSPcmZ7+umj\nnF4wA5UAACAASURBVGfjQZ59xOEEsbubmnoft98Os2ebko+aejvemx1tpjDf6zU3k16vKczvaHM+\nNltlOgv4/eZG3O+3Y6brTCE1YWlut8niPR6z7/HA2Jg57jRbn7n39ZkPqXnzjj/W1+dcTMdYWkdn\nPUv70QFW9gmrHA5TUtLJ4TE/Se0mz+PiivxOvMNlZ/7LOdbb0U2np46IXkAqZYqmXZ4UdDjcJ8ni\n92ZktIrKkhhjTMTmd8eIDDsfm81s7Rc5E8hIWFpdnRn9GhkxjRZHRsy+DWvd2vrMvbzcJK7Dw+ac\nDQ+b/fJyZ+MCrK2js5rU0U3ewABeXx7zA2Zm5PwAeH15ZkV7h3Umqigci5FKgXKZ92bhWIzOhMMJ\nhcWtAwaXBynu3sf8vc9x+Zubmb/3OYq79zG43PnYxIVJkrC0m26CG26AwkJzU1ZYaPZvcnZ9YOD4\nZ+7Zy8k4/cx92TJYvNhcfAYGzHbxYnPcabbW0VlN6ugmr6SEaMV83o54GB1K8XbEQ7RiPpSUOB0Z\nr/uClOkoZfSjdIoy+inTUV73OZxQWNw64NZbYWREMzoKoBgdNfsWLDIgpqC93fT8PHDAbB3vXXYS\nkoSlNTWZ4frKSjPCVFlp9p1OdOD4Z+6Z5WRseObe1GSSr+yRsIEBO85Za6vJIUq8gDLbsjILetfY\nTOroJu1gzUp+055gd6yWIYrYHavlN+0JDtasdDo0ShYH+JFnPTHtpTLZRUx7+ZFnPSWLA06HZhKu\nDRvMB60FXfwz6o+GWNa0gINXrWXr7A9x8Kq1LGtaYMWkATE57e3wzH0vsubpP6Uy9jZrnv5Tnrnv\nResSMakJy3Liem9WrP9muYEBU4w5PGxGwgoKnI7IiO8Oc0tPC6NFfsZdXjxjUa6PtPCLuAXLydjK\n4lodWz1y6DYCiUNUuo/gJgkk2ZVYxL8duo2vORzbTTfBrhi434DkKLgL4OqrYLEFo/vW6u6mpr6a\n26/OOpZyvrcaYHW9po3L12375ous3/UF8l1jvJlaTm3fDtb3dvDCN79Owz/bs4C3jISltbaadT4/\n8AH4yEfMdsECO0ZOspcH8njsWR7okUfAczDMHxc289Xyh/jjwmY8B8M88oizcQFcNx4iqv2MFpqE\nYrSwjKj2c9243NGeksW1OjDxaOGuu+x5tPDvbwT44ax72aLXEE8Vs0Wv4Yez7uXf3wg4HRrXzglz\nzd4WaiviFC+sprYizjV7W7h2Ttjp0OxlaT86m+s1bV2+7upf/h3e8X6SeYVol5tkXiHe8X6u/uXf\nORvYCSQJS7N5KQ1b11o78EKY9X3f4erDz1N58DWuPvw86/u+w4EXws4GBqyq66Yn4SMeN/vxOPQk\nfKyqk0drp2RxrY6tH/TDw/BaNMC/FmzgaF4l/1qwgdeiAYaHnY0LYOQXIWYv9JMqLWNk1EWqtIzZ\nC/2M/EJuRE7J0n50Ntdr2rps0WXx3QzmlZNym8czKXcBg3nlXBbf7WxgJ5DHkWm1tea95vdPHLNh\nBiLY26Ji9dFWKgf20KtnMaQrKE4OUzm6h9WuVuDzjsZWvbyKdcUxtnaYhpBeL6xZEaP6Cnm0djrt\nAwFa9waoq2tj4/5GmpaCDTPPW1thvitMQySEd7CbeEkV7WVBWlsDjj72KCkB9zthrh8MUZHI53d7\nmvm1O0hRScC5oNISnd148zws6Wsnf7iPsbFyRssWkeiMnfkvX6wyNyKhkHkEWVVlqvWdvhGxdAkq\nMNehVeMvsvzFZnwDncRKa3jt6g1sjTn7yM83p5BY9xiJvELA3Fe6EmP4qgodjetEMhKW1tRkGrP+\n/OfwzDNmu3evHUXmtraoWDq6jXfH/QymiklpxWCqmHfH/Swd3eZsYADBIDXeKLff0m8aQt7ST43X\ngjtai7W3wxe+YF778bjZfuELzo82ganxuz7SgmcsTsxXjWcszvWRFuK7w47GtTAvTNOYiWtMe/CM\nxWkaa2FhnrNxAai8PGbtbMOVGGWkyI8rMcqsnW2oEzssi+NlJg385V/aM2nA1sekwPWpF1n9wlfI\nH43RX3YZ+aMxVr/wFa5PvehoXL51a7jU10+xNm1ainWMS339+NatcTSuE0kSluXE5XasWH4He1tU\nJMdBo0hpjv3RKJLjzsYFQCBAx7L1tD7nJXo4QetzXjqW2fFozVaPPDKxQkSmSfHevdhX46dc1tT4\nXd4dog8/MZd5TBRzldGHn8u7nX9MhNKkUiahjkYV8bh5woaSGUczjsX1mh8ZbGZovICCgXepOryD\ngoF3GRov4CODzc4G9id/QvFvr+aygJsizziXBdwU//Zq+JM/cTauE0gSltbaCvPnH1+YP3++88+1\nwd4WFS/rlVTQSzFDKFIUM0QFvbysnZ+e394Of9MS4IXaDQyXVfJC7Qb+piVgxaiOrbZuNU84iorM\nDUhRkdnfutXpyEyN3+jACHPeCnH5m//GnLdCjA6MOF7jV9jfTX/Kh9sNLmWS1/6Uj8J+52sP4/1J\nthU1MkYB5TrKGAVsK2ok3p90OjQxWRbXa87ueYtA8RHi/QkOHPUS708QKD7C7J63nA0sEIBPfhKu\nuspMZrjqKrNvwTnLJuPSabbWXWVkloVoa4M77nA6GuOnebdRxiEqOUIFvYxRwFss4qd5t/F1h2M7\n2WSGzHGnk1dbKQVDQ3DkiFkxIhw2NU+ZpbycVF3jZl1pG3v7ZvHuiB9/4TDrStsor7nR0bi6UlUs\nyNtP5XgXPn0Nq8Zf4XBeNV2pyxyNC6ArWYUqihOpnBgtcQ3005Wc5WBU4kITG4SjR8YZo4C8PBij\ngKNHBikoB9+Z/3ruhMPw/e/z/7P37uFxVfe992fPXXPRzEiyLMmyPPINZGwZc7MhDAgIYJKGJNRu\noqYup+E0fSA5b5vmDc1JmnNoT0P7hJNe3gOkudGmTqLkDThckgAlDgKFYgzEVyx8HwtZd2k0Go3m\nuvd+//jNINlAafI+Zq3Q+TyPnqW1QPB9ZvZe+7d/63ehv18qiB86JPPWVq0MsaonrIyucVcVdKz8\nO+yN8XXHHfQ6rmOf4yJ6HdfxdccdDHtjqqVpne2qK6tWyb41Pg6mKWMiIevKMQwiYbj4InjvtTYX\nXwSRMMpjBoa97Vxe6CPCNBZOIkxzeaGPYa/6fmeJpXF8uSSZ0ymGBy0yp1P4ckkSS9UfYWlNIiF1\nuO65R0YNykDoXKLipcwayObxm2mcTgu/mYZsXtZV8o1vwC9+IZuZyyXjL34h6xpRNcLKLIy7six9\n4q5A34DphgaYCMZ4yL+N+2o+y0P+bUwEYzQ0qNUF+hvVOvJWxYm1KFpcKkFXl1QDTiZl7OqSdYVc\n4D/J8+44M44ITkxmHBGed8e5wH9SqS4A54oYD/u6mXMEaLKHmHMEeNjXjXNFTLU0fVlo7LS06GPs\naFyi4mezmzjpv4DmwinWpF+guXCKk/4L+NnsJrXCdu6Uz6nSBSQUkvnOnWp1nUXVCCuzMO5qcFCf\nuCvQN2C6qwvq6qC2VkIUamtl3tWlVhfom8ygM8eOwbJlsGiRXGeLFsn82DHVypAsMJ9PApE/8AEZ\nfT7l2WHLAyOkou3sDcZJO8PsDcZJRdtZHlAfE2YYYqvW1cl3WVcnc10SjrSkr088Jvv3w09/KqNp\nqjd2NG4plsPHmuzLTHqaORS6jElPM2uyL5NDcSmIXE66CyzE45F1jagaYb8B7NolRfBGRyGfl9Hh\nUB8wfccdcMEFcsTe3CzjBRfIumo6O2H9enjkEfm8HnlE5joY1bpi22JMx2Ji38RiMtfCE6ZpEc3w\n6ibO855kQ6aPoJliQ6aP87wnCa9WXzogOJHgk/U91DozDJot1DozfLK+h+BEQrU0LcMrAIkbOnBA\nNtpoVMYDB2RdJRqXqLg5+DMGjDZcpRxLs8dwlXIMGG3cHPyZWmEXXADT05S7scs4PS3rGlE1wsro\nWpEbpCr36KgkxRiGjKOjKK/K3dkJd98tmaQXXSTj3XfrYeg8+CDcdx9EIuD3y3jffbJe5c3ZtEn2\n9YUN2dNpWVdOLCYG15498L3vyahB77xcSzvtp/uIGtPYDidRY5r2033kWtTHhF1e6sPtMImH9/OR\n2p8QD+/H7TCVl/XQtQ0bAFNT4gb2+2Wz9ftlPjWlVpfGJSo2+I8QdWcYsxroL61mzGog6s6wwa84\nO/L22+X4MZcTb2YuJ/Pbb1er6yyq2ZFlduyQ72nfPrm+w2FYskSPbLpKYeR8XkJgZmbm11VTydrU\njXvvlePRSET20khkfn3LFrXadOWOO+CVV+DECTHABgelTIsOnk0SCYZ/2MdLgxsYmr2KlsE0l/yw\nj2bFmU4nd55kJBKnoTBErW0y44kwEFxDdudJQG3F8PfU9XPil8cxA0GygSh2JkdD5gDLL5hTqmth\n9wPXWg9XD2zXovsBAPX14i2ZmxN3cOUBXq84o3RhJf9KiQodKvkj+6vPnsXrzOCiQMnpwbBtDKNG\nrbArrxSvwPbtEpi/fr0U371Sn+bdUDXCXmfvXnj5Zbn/SiX5zo4f5/XegyoJhaBxLsHlVh+LLA8f\nnN3O8444oVBMtTQefFAMm9OnxWj91Kf0MHJOn5bPLZGYL7dQVyfrqtm/Xx5EAwOSKHDLLfoYshXv\nl2XNe8V0YOC7fezcFcURDVNXD1NzYZ7YBdd9t4+2L8SU6XJOjDARbCcxt4JLHF5e8sUJ+i1qJ9S3\nk1nsmsS9xsnRST/pWQgF/axalqXONalUV+aVBNdP9pCviVJyBHAXklwx0MNTmW4gplQbHR3i/Roa\nEk9TJALt7RIcqZpYTH56e/UIvC1zNNvKcqufks9PyeXHVcrgys9xNLuBi1SLGx6GI0dkgz1yROaa\nUT2OLHPkiBzxWZbE7lmWzI8o9qgCFI8m+B2rhyAZirgJkuF3rB6KRxNKdT34INx5pxiuzc0y3nmn\nHkd+4TCcPHnmEe7Jk28sW/FOo/Ox91/9lVzvfr8cE/n9Mv+rv1KtDPp7R3BEQgQC8n0GAuCIhOjv\nVRuYnAk24R0+ydrpPkJmirXTfXiHT5IJqo/Voa6OulqTjevmeO+1NhvXzVFXa8rbiELO6H4A2nQ/\nAOR4z+mUh/b73y+j06nFsZ+upIp+xhvXYrpq8BRmMV01jDeuJVX0qxX2wx9KGYF0Wja0dFrmP/yh\nWl1nUTXCyoyOypjLyXFfJYGisq6S88b6SBIl45RNK+MMkyTKeWNqN62FR34Oh4y1tbKumvZ2MaQr\nFQxKJZm3Kw7VWVhE1uGY/12HzgzPPitV8n3lpCafT+bPPqtWF8BAvomo88zA5KgzzUBerbEzU9/O\npmIfteY0Jk5qzWk2FfuYqVcfE8aaNbBu3ZllPdatk3WFbGofYbIYev2UIZOByWJIefcD4MzK9END\nWlWm1xXnonpyto85fz3p2hbm/DJ3LlJ8hHv//fLWXV8vb2719TJXXVbgLKrHkWUKhXKXdYf8gMwL\nBbW6ABrMEUZdLVQyyw0Dcq4Qi021Rx6nT4sHbCG1tXoc+YVCcP318NJLYnzV1MjLrOo4Op07MxQK\n8jktxOXS40hydkOcphd78BqQ94bw5tMwnWT20s1KdQXHT/KyP05kbogmTKbsCCf8awiOq48JIx5n\n+KVBXjrVydBsiJbpNJe4kzT/nlqvTsuGJm72p9nVH8Y0xc657pI0Ledp4D2E+WO/Kv8hVmxaxOSx\nPEUneB1QLIFt5lmxaZFaYcPDb9xsIxEJdtWIqidsAUa595vLJaMu9XTmQk0ErPTrpQJsGwJWmrmQ\n2k1ryZL5JIEKMzOyrpq2NtHxX/4LNDbKuGSJ+mKtOheRXb5c4pGLRZkXizJfvlytLoDrbovx88Xd\nJIsBgjNDJIsBfr64m+tuiynVFZgdYXwu+Pp+4XTC+FyQwKx6r87+mRjf6I8TObWHq4e+R+TUHr7R\nH2f/TEytsHic1kCSLdenaGiALdenaA3okelX5VdnSYtN3dIAR8xVPJO6iCPmKuqWBljSori2TSVG\nZiGV2BmNqBphZSpxMCDJMDAfF6OaxVvi1FpJgqY8vYNmiloryeItajetT31KjK7pafE2TU/L/FOf\nUioL0LdYq86dGT73ObneTVM+M9OU+ec+p1qZhOa852MxHihs47+d+iwPFLbxno+pz6bLFV3cxE/o\nsA7isfN0WAe5iZ+QK6o/ZNj5rQSrx/rYXdjAvxR+l92FDawe62PntxJqhWncjBrma5h9/OOa1TDT\nlKFBk59bXdQu8rK+LUntIi8/t7oYGlTcKP6OO+QNd3JSNrTJSZlrke49zzkzwgzD8BmGsdswjH2G\nYbxiGMZflNfbDcN4wTCMo4Zh/MAwDM/b/bfeCTo6YPHiM4OSFy+WddWUWmPsOa+bnCuAmyI5V4A9\n53VTao0p1bVlC3z5y+LhHR6W8ctf1iM7srMTbr5ZSo5MTcl4883qsxB17sywZQv8wz/A+efLkfz5\n58tch+9z/3549FHJMu/ulvHRR9U/IKP2BEsdp3FTwsKJmxJLHaeJ2hNqhQH09XF0PMq0FcbndzBt\nhTk6rkerG2IxKReweLGMGhlguibO6Mquk014a32MrY5z9LzfYmx1HG+tj10nFR8vb90qJSpCITH2\nQyGZb92qVtdZnMvXtTxwrW3bs4ZhuIFfGIbxOPCnwN/Ztv19wzD+EbgN+Oo51PEf4lOfksy+WEzi\nmmZm9PHq7N0LQ54YyYtibAr2cuiiLrJZWVfN6tWSLV0pt7B6tWpFwsKHdl3d/EN79Wr1Bo+utdVA\nDK4tWyQLXodrv8LChAaYH1XX8VvnP85gYRnNxddIkyNkzzDoWsY6/3F1osr4UiOMOVuoKb/muj0w\nVwjRkFJfPoNEQoxBj0fqOGlQeBf0vc505nlXnA/k7iN4bByXKXXCZv2LeMz1SZQ7+LdulZ/eXvjk\nJ1WreVPOmSfMFmbLU3f5xwauBSpFDL4NfOhcafhV0NmrMz0tnomaGok7qamR+dnH3e80Or817tgh\nGZGV4rv79slchyzEKr86AwNvLC+iQ0LDsvpZmoqv4aSEjYGTEk3F11hWP/v2f3yOyYWbcOXSjAzD\n0GkYGQZXLk0urNhDsbBJttutT5Ns9L3OdKalBQp5AwywscGQeUuLamUat8dawDkNXDAMwwm8DKwE\n7gOOA9O2bZcLBzAIaBDGLVS8ALoRiciR2sJ2MpY1XwVeFTq/Ne7dK+3eMhnJ+jt6VDLO59QWC6/y\na9LWJt/h6dNndrRYtUqtrnTGSTMTzFCLhQMHFg1MMJxZoVYYMH5+nI7Dd7Oy2E/ATJNxhjjm7qD/\n/M+rFdbXJ5tFxdqpjH19yr1hbW3yMlnZy0CfxBld+e3GPna6l3M6ugG/X/ZYK5nitxv7UFl8t+Ik\niEblBKTiJNAl/KOCYb8D3XkNw4gAPwL+B/BPtm2vLK8vBX5q2/a6N/mbTwCfAFi8ePHF3//+98+5\nTl0ZHpa6ZcUiRKOzJJNB3G6p46Qy0ePUKXmRXZhFatuiU3WB6f5+sHMFQsYsgRaDzJBN2g5i+Dxa\nxPnpzuzsLMFgULWM10kmxQBzOCQD0TTlRWTJkjMfmO806T3HcFkFDGwKrY14BsewMSg5PIQ2rFQn\nDBg9PkswdfrMDuyGwWx4CYtXKPxuR0cp2G6pExZ2QMoiEACPUZQYMYVU+vQ6nfPXmWmKrLPLt6hC\nt3tz4fdpmvK56fB9qn5uXnPNNS/btn3J2/1770gKj23b04Zh9AKbgIhhGK6yN6wVeNMABdu2vw58\nHeCSSy6xuzRq0/BOs38/fOELMDYGt97ay7e/3UVjI3zpS2ot+rvueuNbY2V+663KZAHwF3+Q4JrR\nHk65o6z9y0YO/s8xAsXDPL24m6dPxtSK05lyrE6vx0NXpUGwBrE6d90l1fuHhuY9YS0t8oZ7113q\ndD10wz+SLZrUO6Yxv/zbuO98mEkrgtPtpKvwX9UJAx7p/iPSo2kmqce2y/UqmaRmcYiu4a8p0zXw\npe3sfDSJIxqmdquXmR/mmU6muO7mAG0f6VKmq4LObcUAent70ep5uH07ZJJnnuNOp8QSU6jzQx+S\nPrg1NfDxj/fywAMSS718OTz8sDJZb+BcZkcuKnvAMKST53uBfuBpoHLodyvwyLnS8G7ibIflO+DA\nfFt0LrdwhdmHHYmS9YTlCNcTxo5EucLUIDNMVzSP1Vm5Uvb0D35QxpUr1cfqvOzcSIQULisPgMvK\nEyHFy86NaoUBNZODJO0ILpd8nS4XJO0INZNqi1U+NBanwZmkzikld+qcKRqcSR4a06NOWGenGPYP\nPCCjTgaYlsTjsvGnUvIgSKVkrrjum66x1GdzLuuENQNPG4axH3gReMq27R8Dfwb8qWEYx4B64Fvn\nUMO7gh075CVj4cUUDqsPMte53MKGlhGmCqEz2hZNFUJsaFFfRFNb3ixWJ6pHSQNdi9y+GrqUBkZY\nxWF85FjFYRoY4dXQpWqFAQN2K1Fj+owiz1FjmgG79d//w3PMgXSMIxd3U/QEcFpFip4ARy7u5kA6\nplTX6yQS4t255x4ZNXgJ0RpNWz1FImIT6hZLfTbn7DjStu39wIY3WT8BXHau/r/vRvbunXerOhxy\nMR04wOu916q8kbZLm/D1p8kjBoVlgc9K03apJq1RdGRkhIFSC7ufAm6EB5+Eyy4J0ZZWX9Lgllsk\nqBbENqy8bN92m1pd15YeJ8AcaWop4iJNLQHmuLb0OKrbFj0c2Mbn0v+dmHUS2zIwHDYlw8XfBP4a\nlQelbW0wkIyRXh/jvGAvh9d3kUyqN6gBSCQ48IUe/vXFKKdTLSwJp7nh0h7WfUm9UaE1GrZ6uvBC\naLMSLD7SRyTv4ebUdkZXx6m7MKZa2hlUK+b/BqCrW1XnEhXPOeKc35yk3pXCtqHeleL85iTPOfQ4\n8tCRgUITj30vzb59kM9LWY/HvpdmoKDecO3shP/eneDqge1c8Pg9XD2wnf/enVDudb18didpaing\nwwAK+EhTy+WzO9UKA9quaGWQpdTZkyzjJHX2JIMspe0KtZ4wXbtZAOz+Sh9P/NSkdXI/m62f0Dq5\nnyd+arL7K+q9wVV+NT6yMcENx+7jWmMnta45rjV2csOx+/jIxoRqaWdQNcJ+A9DVrbqwRIXDMf+7\n6mNSgGdOxfh5Ps5Fzj00GFNc5NzDz/NxnjkVU6xMX752KE5+JEmglMLhgEApRX4kydcOaWC4JhJ0\n7O3hlhszfORPWrjlxgwde9XHqwXMGeqZwmWYWDhwGSb1TBEwZ97+j88xX1jzIxqDczzviPOwcQvP\nO+I0Buf4wpofKdW10KCuSY1qY1ADHH64n7XWAYKePGlPlKAnz1rrAIcf7lctrcqvSEf/Dq5cfBSP\nz0nJduHxObly8VE6+jV4QC1AfYOzKm/LhRdKratDhyTldnQU1qyRdZUMDEjAb2/vfMbaeeepD5YG\n8A4nWJ/u45f2Bs6z6zhsbmB9uo8Xh1tRWbtGZ356KMbS5m42Fvpw2UVMX4De2s28dijGl1SL07S2\nVMYdJphPUXCIl9rhALdZZModfvs/Psf4D76A1yhwGbvw2xnmCDBlLMN/8AW1wsoGdceNUXobA3Td\nmIS9PdCh/sjPmZrE6XUy55CmwTmHn5AnizM1qVSX9lQ6IIyMQFOTHlnVu3cTiUW5OOgnXQsXX+mH\nWQt271ar6yyqnrDfANauhVdfFc+Xzyfjq6/Kukq8Xnj2WfHM1dbK+Oyzsq6azpk+huaiTJbCGA6Y\nLIUZmovSOVM9Vngr3irjVodMXEZGeKE/xF/+L/j0p+Ev/xe80B+STV8hqfYLyTpDUivMtnBZBbLO\nEKl2xW9IwPD+CVamXqLOmsBLjjpL5sP7Ffe11DgBxAzXkc+YZMbmGBuxyYzNkc+YmOE61dL0ZWFW\ndUuLVlnVzM7KwzKTkXFWfSeLs6l6whaga32Ygwdh0yZJPKkcQ65ZI+sqK/zr/NCuK4wwWduCxxQ9\nHg9QE6KuoD7IXFfetyZB4LEexgpRlhQCjJ1MstHTw9oPdKPae7h7oIkfbU8zbcuDO3safvTtNMa2\nJqVZPit/7wp2f91PW3IvtuFkMriMgeiFXPZ76o0wc2ISLzlytp+S4cJll/CRxZxQ7NUZGeENPW1C\nIdngFONYu4ZdTwRY6hiizkiStMIczrazZq3i6tM609cHExPws59Ja5e6OjmmUd0BYdUq+N73xD1d\nKolRaFnwu7+rTtObUDXCyiwsiJrPwyuvwMsvqy+ICvM1klavFgOsq0uuJdXHfoUCXHUVHD48fxxZ\nOTpVTS7cRDSXphAM43aLNk9Wg755GnOl3cfjuSgzhhg603YYKwc32WrbjwD85dNxLs33YHsg6wpR\nU0pTk0/yl09v5scKdbV9LI57bJCXjm0lH2ikGN/Ke1Ymaf6Y+jg6VylHmlqcDhO3XcJ0GKStWlyl\nnFphTU2QTp9Z3DOdlnXFPDoVZ0N4kMP5TpKlEFF3msXeJI9OxfmYanG68vzz8ItfQDAIDQ3idXrq\nKTka2bZNnS7LEgNsYY0Wh0PWNaJ6HFnmq1+FY8fk98recOyYrKtG1xpJbW2ydy4knVavC4B4nNZg\nEiuZolCQXmatQfUFBHXm1O4RPPUhwmHZq8Jh8NSHOLVbfW21fxuK8eNQN3l3gMXWEHl3gB+Huvm3\noZhaYbEYzVvjfKB1Dw3OKT7QuofmrRrEwyAFilOEsW2nNBi3naQIk/UojldbWNwTtCnuCfDLqRin\nlsbpiuzhE7Xfoyuyh1NL4/xyKqZamr7098tRQygkG0coJPN+xckMx4+LV6C9XYKX29tlfvy4Wl1n\nUfWEldm1S3pejY5K8LvPJ4b9rl2qlcmx6Oc/D+Pj0NgIjz8OixbB3Xer1bV2LXzzm9KbC+Q04dgx\nPVLNl8Zj/P2Obq4O9OF3FSEQ4Ftzm/mTeEy1NG05PttEcyjNnEe8hw2LwF9Iczyt3kPh8cCAHkA5\n8QAAIABJREFUHWM8GHt9LZstHzOrJJFg7JuPMLgnT2adk1/uytOaf4TG1lblhlhy+SV0Hv5/mbWD\nTBPBzxyLHePsX36NUl2vF/fs65PNIxCAzZuVf14AF9UlOH+gj2OhDexzXkXATHP+RB9TbdWEnn+X\nTEaauxaLYvBEImKM6YIOMTJvQdUTViablVCFYlECy4tFmWezqpUJC5tkv9lcBTt3zrdDMQxeb4+y\nU32JJA4ehGVXxXhu+TYmXIt5bvk2ll0V4+BB1cr05bVYHHdGSlSAlKhwZ5K8FlPvoYjH5eWoUp4l\nm5W5aufJ6Xt3cOyJo7w25CRvunhtyMmxJ45y+l71afDnv28FI7WrweMm4kyDx81I7WrOf98K1dLE\n4Nq2TRo8b9umhQEG8JnL+hgvRhnPh7FtB+P5MOPFKJ+5TH3SgLYEgxITZtviybBtmatuMr5qlTwI\nMhnRlcnIfNUqtbrOouoJKxMMihdsIaap/joCSRZYvhwuvliSiG66Sbz3O3aojVfbtQvWhRJsLPRR\nVxxhyt3EC544u3bF1IkqMzDwxu8uGFQfRwf6JoBs/WyMv/90N1fn+mizikzlAjzj2syffDamWhpf\n/KLcn8ePS4JTTQ2sXy/rKjn98G6GslEsnx+PAXmHn6Gshevh3Sz532q1LVlcYuxDW9j75BEc6Wms\nUITYjatZsrioVhjz90B7u/Rn1OUeuKxtBPtjLTz5FCSnIFoHN34oxGWt6pMGtMXhIG87mSnWkrW8\n1Djy1BozeB2KfTzRKCxdKm9rpilegqVLZV0jqkZYmdZWCU2Ym5PAfKdTYgxb1RaXBuRhfbaOcFi9\nQdGUS3BjuoecP8q4p4WAmebGqR5+HFKfTefxwJNPSpJAoQBHj8KpU3DjjUplvd5lIBo9s8uADj03\nt2wB/xjs+X/AtMDvh8//X/A+hRm4FTo74d579TNeJyfA7TIolXdSpwtcLoNJxVUgQDog7D2UwdEZ\nx++XvW3voRT1K+tRGba58B5YvVqve4CmJjbmT7Kxa2g+22hRCzRVsyPfitRUiVd9V9CaPUzQSpN1\nBNjru4LzpypN4xRhmvC+98GRI3K8tXatXHBF9S8hC6kaYWUuvFAeOkML7r2WFvnOVNPWJhvVQgNe\nh8D8rc19nEpGsawwbidMW2EcJVlXbYRNTcHkpHynDofcj5OTsq6ShV0GYH5U7dUEIJFgw+EezBVR\n8r4AnSuSbDjcAwn1RTRBPh/ln9FZHKjZyPUT32Hx1BD9xRV0DD3IqKuFpxp+j5sUa3toLM5KZw84\nIU+IOmcanEkeGtvMpxXqWngPGIZm90B7O3znO1BfL3FN09Pifu3qUixMX47MteK0hxmyWnCVMpQc\nAZx2iSNzrShtY9/UJEeQ8bgYYfG4PDjr61WqegPVmLAyt9wi3sr16+EDH5DR5dIjyFzXXmubLxwh\n0CTBl3NzshZoCrH5QvXZdEePShJDLicvPrmczI8eVatrYODMzHzQw6sJMPDdPp7YFWXKDON0wZQZ\n5oldUQa+q0k8TCIB27fDPffIqEExSHfQQ6x0lCBpDGyCpImVjuIOqs4YgAPpGEcu7qboCRBKD1H0\nBDhycTcH0jGlunS+Bzh5Uh7WFQMsEpH5yZOqlWnLz3gv9eP9+AtJCi4//kKS+vF+fsZ71QrTOAt3\nIVVPWJnOTrj5ZjnyOH0aliyBT31Kgzezs7StWiWNlXXQ1rKhid/1p9nVH2ZyUl4wNnWkaTlPfTZd\nNit7aCAgyQKBgMxdiq94Xb2aAP29I2ScLUwNwIo5ODUAdZEQ/b1DtH1BsbhKVe5oVFzU6bTMu9V6\n6bpOf4cCXtwUsYESLoq46Tr9HeCTynSBXFMvH4nxWDIm3v0itNSo9+7rfA8wMsKAs53doytkT8vD\nZUst2kaqMWFvhZXJ8UzoZtaae4mUJkm763jRdzVWRnE9Oo2zcBdS9YSV2b8fHn1UPGDd3TI++qis\nq2ahtro6jbTF47QGkmy5PsUf/aHFlutTtAb0eNMIBiUWLJ2WYsnptMxVJ1os9Gpalj5eTYBDk014\nX9lD17FvUlcYpevYN/G+sodDk+qN6jNa3VSKmGnQ6mZJ7jhu8ngoYGDjoYCbPEty6msRrV0ryTPT\n01ItYHpa5qrbnenq2QeJo9v5cJpMRvbaTAZ2PpxmoKDBPaApLc4RMoYfs1wD1bQgY/hpcao/EeHF\nF+GBByRb84EHZK4ZVSOszI4d8rDetw8ee0zGUknWVfNmMRTRqAbaKm8agYAE0wUCyj0TFUIh2eBN\nU+ZmuX2R6tI1nZ0SgByNwuCgjFoEJAOnJ310zT5C0E5hGk6Cdoqu2Uc4PelTLU3qxZz95YXU9450\n2QX8ZHBRwgBclGRuq28bcfAgdHSI8XXwoIwdHSgv07LwHigW9boHHhqL0+BMUudMYWBR50zR4Ezy\n0Jj6F0tdiUVT3Fx4CD9ZJmnAT5abCw8Ri6be/o/PJT/8oRTYTKflOCSdlvkPf6hW11lUjyPL7N0L\nhw7Jm0+pJO2LhobmY51Uomt2JCAGlwZG19nYNjQ3i/fL6ZTith6PHjX7dAwwB7ho+mcMWG00WaN4\n7DzeUo4B2rho+mfAVrXimpoY7H/j0Xer4qNvEwdOwMRGLi27PFf/frt3rySGFYty7ReLMne7VSub\nvwd6e+HWW1WrmedAOkahPs75L21nUWaQ6UArr16yjcOK4+h0Zn3tCdION96AB5/XwM57sDJu1tee\nUCvs/vvlQVlfL96LSkD+/ffDVsX72QKqRliZwUF5qbZt+TEMMZxra1UrK8dKJBJ0pvpwLfHQuW87\n+8Nx2jQwfnSteRWJSCZkNCqJMYsXS5xYJKJamb7ECkdwkGWcBnL4GKeBABlihSOqpdHfEOfA3/Zg\nR6AuGsKcSvNvP0my7j2b6VCoyzS8ZG03Xoo4sDGwyOLGNLwKVQmDg3IK4/fLPVAqyXxwULUyfeuE\nNecTtD37L7Ra/QStNMHZGeae/RdmfqtaMf+tqDNS8J5Oiv0jWJkMjoCf0IWd1KE4FX14+I3ei0hE\njxtgAepf1zRhYkIy6CxLPCeWJfMJDer9fGRjgjX7eihOZzAdborTGdbs6+EjGxNKdVXq/SSTZ9a8\nUh6rhpQcWbdOinpalozr1sl6lTfHMMBBiQJiQBTw4qCkRXeGH7wQ47m2bo4NB3jthSGODQd4rq2b\nH7wQU6pr2hHFwkWGICZOMgSxcDHtUF8QcmZG9rKFOJ2yrpKF+4bbrde+cfmhb3Ll7JO0ZY/QmHuN\ntuwRrpx9kssPfVO1NH1pbaUubLLy/eez+ncuYuX7z6cubKovstncLGfwC5melnWNqBphZXI5CTFx\nuyV+yO2WeU5xggdAx0Qf8feYtKf348mmaE/vJ/4ek44JtUHJC2PVHA6NYtWQN2unU5IYwmEZnU49\ngn915YjRgZcCAaQre4A0XgocMVT6moS9e+HpkzEeDW/jkVWf5dHwNp4+GWPvXrW6jrGKAm7Afv2n\ngJtjqG+NEgiIB9jtlgLUbrfMAwG1urSNcQXWH99BnWMaHA5yhhQZrHNMs/64BuJ0Zds2ZhKT7Ht6\nkp89ZbLv6UlmEpPSjkold9whabeTk3K8NTkp8zvuUKvrLKpGWJlwWDaEQECOjgMBmZ9dz0YJhw7R\nPH6Ay9bnCYRdXLY+T/P4AQliU4jO9X50Dv7Vld2uy3nGcz0FRw1uShQcNTzjuZ7drstVS2NanovU\n1Mh9WVMj87NfdN9phhxLSFNHDh82DnL4SFPHkGOJWmHApk0S01opEF4synzTJrW6dN43osVRTIcb\nd42bmoCBu8aN6XATLY6+/R//J2V/7ZX8ffCLzBCi1TzFDCH+PvhF9tdeqVbY1q1w993iTSkWZbz7\nbq3iwaAaE/Y6114rSRPj43J85XBIPNi116pWhgQ3OZ0S3AEyZrPKy7+3tUmgr45dBkC09fZK+6ne\nXknNrxphb80r0TiLxwZ5wreVta5GDga3EigmeSWqPjOsEuOXzYLPNx86oDrGz+812V+8gABZCng5\nyHoy1OD3mmqFAdddB9//vvTarMS5BoOyrhKd64S5g14KE2k8xTkctoVlODBtJ+4GxWnVGrNjByQ7\nrmTnFfNGlw69jQExuLZulQfAJ9XW7Xsrqp6wMitXylui0ymZRE6nzFeuVK0Mcc2ZpgiybRlNU3n7\nBV3rEAE8+CDceadocrtlvPNOWa/y5py/Oca/1nUz5wjgsorMOQL8a10352+OqZbGhRfKdVVTIzFN\nNTUyVx3jN27Ws4TXuITd+JnjEnazhNcYN9W3RvnBD6S/6+8b2/mscQ+/b2ynKZfgBz9Qq0vnOmH5\nlnZ8zOG2c7jI47Zz+Jgj39KuWpq2DAxAm5Wgc992Ln/uHjr3bafNSmjh2Xy9y8boqDZdNs6maoSV\neewxaWvj9crG4PXK/LHHVCtDivusWyeiTFPGdetkXSEHD8IH1iXYmtvOh47dw9bcdj6wLqG8DhFI\nd4GK8ZXPzxtj996rWpkEIN91F3z84zLqEJAMEiphtMfY4d/GqLGYHf5tGO0xLUIobrlFkpoqjdiP\nHpW56gd3ODvIBfTjIw+AjzwX0E84qz4D6+TTCe4w7udG904uc/+SG907ucO4n5NPJ5Tq0jlUYHK8\nUsPGATipPCLn16uczbpQgtUv9+AuZEiHWnAXMqx+uYd1oYRaYYkE3Hcf7NwpjoudO2WumSFWNcLK\nnDgh31MgIMZXICDzE4pLnQBSgd7plF0qHJbR6VRemT7zSoKrh3vwWRkm3C34rAxXD/eQeSWhVBfI\n95ZMyiZvGDImk+q/T50zSoE3ZELqkBkJcrSceSXB5vHtfGLmHjaPbyfzSoIjiqtnXE0vYJPDi4WD\nHF7ALq+r5YbMj1hhHsE2nMw467ANJyvMI9yQ+ZFqaXR2ygvIsmUy6mCAAQRTg2Q8dVhIWqmFk4yn\njmBKvVGtK7/d2Ec2YxIe2M+qIz8lPLCfbMbktxsV95zdsUPe1pxO6VfndMpchwyQBVSNsDIOh3jA\n3G558LjdMnfo8AktrExf6YGlQWX6jsk+nnslyt4TYU695mDviTDPvRKlY1J9w2eHQwq1VorvZjIy\nV/196pxRev/98Npr8nvF+HrtNVlXzT/fleC3ZnsIGhlGHS0EjQy/NdvDP9+VUKqrnmmyePGRw4mJ\njxxZvNSjOGMAuMr3AjnTTVNhgDX5vTQVBsiZbq7yvaBamra4zTyewjQ2NiYGNjaewjRuM69amra0\nzR7i+qYDBFx5xs0oAVee65sO0DarNnGM3bvlGOSFF+Qo5IUXZL57t1pdZ1ENzC/T3i5p8NmsnPbl\n8/LwbtclFKBSmb63F7q61Gopkzk+wshcCxhiUFgWjMyFiB1X3+y2oUESBip1kvJ5OclVHeOnc/eD\n3l4pUOz1zhur6bSsq6b5eB9TVpRZZxjDCTNWGMuUdZVFNC0MQsxRxI2NAwsnIbJk8CvTVGHd8lmm\n9yZwmSUcWNTioIFhIstjqqVpS6EIQUpYGICBgY0Lm9miamUaMzVFtN7Jxasr17wfJtUnjjE+Pt+j\ny7ZlPj6uR9DyAnTw82jB1VeLjZNKwenTMsZisl7lzdk70kTEITWlKj0aI440e0fUN7sNBiVZoFAQ\n47BQkLnqBt5tbXJtLUSXzLDpafmsFnoPLUt9GQiAReYIaUI4HGAgRmKaEItMtb0jJ2jEwMRVbuAt\no8kEjUp1ASxpMWgPTeDzWJQcHnwei/bQBEtaNDlj1hGriFV+LBpIR2oLB1hVK+wtqa+XTWzfPnj5\nZRlTKeWJY0xNSUyRw1GuRO2QuWrj8CyqRliZUEiOi0MhWLLkzLkWaJjl8YwZp9ZKUmun5E3bTlFr\nJXnGVF/SYHZWjIdKr0jblvnsrFpdCzPDLEuvzDCfTwyvhXF0mYysq2automAlX69EbtpQsBKM1er\n1uCfMhaRJoiYhjZgkCbIlLFIqa7XaWigJuAkUlOgJuAUF3GVt8TAII2fPH6KeMnjJ40fg6rh+pYs\nWjRfjK5CsSjrKsnlxK2fSomeVErmOlRgX0DVCCvz2GNS4yoQEK9JICBzLbIjEwno6ZEnotstY0+P\nckNsyBPje3QzS4BmhpglwPfoZsgTU6oL5KOxrDONMMtS/pHR2Qkf3ZSg8cnt+O+/h8Ynt/PRTQkt\nApMbGmSPcjrl83I6Za7Dc9t/Q5wWf5IwKaySRZgULf4k/hvUGvw5h5cBYhzhfPJ4OcL5DBAj51Df\nOzJZDLCv0MFkTSuFcAOTNa3sK3SQLCouma8x447FGDiYJcQEi5glhIGDccdi1dL0xbalduWqVXDR\nRTL6/fObryoMY75VhMMx3zpCl2yjMtWYsDKnT0tLqYWB25Yl68rp65Po7UqZ6crY16c0OD8choFs\njO+cFZPTpEGXgcqRX+V+MwzZE84+Cnyn6X88wew3eljZHMXR0YI1nWb2Gz30t3fTcVNMqbbWVgiM\nJ+ic6aMJD7c6t7O/Nk60Va0ugI9+LsbfjXTTdqqPZdkhUjVNHFu2mU9/Tq22E+4OLjJ/QYoQBbyk\nCBFilhPuDrqUKoNd1kZaXM8wF25jxuXDVcrhT02yy7qCmxRr05Xpqz9MzdPfxcccNRQo4SRFlOmr\nP6xamrZNzzFNTq/q4vTTRzCTSZzRMEuu6WKJqfgI1+s9s71GTY0cR3rVvyAtpGqElVmyRI6rFlbg\nnpmRdeWMjIhbbiGhkESeKyQchtqpBBsLfSxmhFGaeMETxwjHlOqC+czWihFWyX5V/XLW//U+7EgU\nZ51Yqs66MGZ5XbURdvWyBCsO34crP07JuJqbvM9wvX8/x5d9EpXB7yAPm0//fYwdO2IcHJAYuk9r\n8BB60XU5BYq8h+fIkSXCNM/xHva5LufjaqXx8/CH2RJ+lcWTh3Dn0xS9IUbr1/Dz8IerRthbcMUD\nf8iej4yT39uPrzhLzh3Ee2EHVzzwh0p1VUrbRKPSkaRS2kaH+moDhSZ2PpvB0RDH3yZ2zivPprju\n5nqUhrpGo/MP9krLiCVLzmzVoAFVI6zMpz4Ff/zHYu9UcDrhz/9cnabXaWqCkyfF6Lr0UnjxRTHK\nli1TKmulK8HF9DDtiTJhtxA10nyUHl52daP6oR0MihFdMcQMQzybqmP8ioMjOFrPNKgdkRDFQfUZ\npb/j3sFw+ijFunqmvC6idU7cM0e52L0D+FPV8ujsVP/AOZtXc+18kO+wnwtxE2GMC4kxwA9y6tOq\nW1qgeEjeOioxTcWC/Yb3ORVo69WJxdjwg8/LKcPIiOy98bjyckBv1vS8sq76c3toLM5KZw84IU+I\nOmcanEkeGtvMp1UKW7VKHgSVkgeVlhvNzSpVvYFqTFiZ1aulfIHTKcfGTqfMteiD2N4um8L0tAib\nnpa54voZl5tSNmCyFKZQcjBZCjNlRbncVF8n7IYb5r3OFe+X1yvrKnG3NpF6Lc2hfkkkOtQPqdfS\nuFvVZ5QuGdzNijVuFuUGcBUyLMoNsGKNmyWDetXV0Yml5kmeJc40EVyYTBPhWeIsNU+qlsbvuHfg\nS40xHFzNwNL3MBxcjS81xu+41Ral278fvvAFePxxCW99/HGZ61KwWEd0bnp+IB1jeGWcppE9rD3w\nPZpG9jC8Ms6BdEytsG3b5GG+aJHEqC1aJPNt29TqOouqJ6zMjh3Sh+6aa+bXtGlCevKkvI0NDUla\nWCQCa9bI+pXqOtUH0iPMOVvwuOabns/ZIQJp9V6dP/9zSSQ9dkzs1sZGMapVezZ918eZ/EIPjgD4\nAiEcM2kmh5O0fHyzWmEAc3NEJ08TbQsx6Heysq0IkyfAp8OZvJ40M0IDo1xFLyOczyp6eYrrMFEf\nd7JkcDe+S6IcG/KTnoVQ0E/7aot6xUb1V78q92Vt7Xwdv2PHZP2rX1UqDRIJJj5zN7Mv9ksqdTBI\n8NI+Gr7yeaXeMJ2bnq8LJWh+sY+Rpg2cWnYV3nya5mN9rLu0FaUnIldeCV/8olQTqNQouuMOpc/M\nN6NqhJXRuYgmIyPi9VqxQtw58bhYPYpjwobMJiLONFlPGKdT7MOaQpohU71Xp7MT/vFzCfq/3ofh\n9fB/LttOxyfidHTGlOraPRYjeVU3jYf7CM0Mka1tYujizWTHYrxPqTLEdV8qnblWKqkvrqYxS+wE\n29iOhwLjlFjOCf6AQbbberxt19cZ1C9dsJAxYE6ZHAB27YJV7gQbU33UFT3cnNrOC544u3bF1AoD\nRr70DUqPP41h2tiWgZFJk3t8hJG6RTR940vKdN1yi8SAwZlNz2+7TZmk1/ntxj52mlEcZhg/MGVK\nIWVpWxRTqm1/7ZXsaL6SdncvdzX/V26pBdU+lbM5Z8eRhmEsNQzjacMw+g3DeMUwjD8ur9cZhvGU\nYRhHy6MWUXI6F9GkqYnB/jQPPggTE/DggzDYn5Z4BYUcjMZpDSaJOlKUChZRR4rWYJKDUfV1wkgk\niDwhTWVNhxt3IUPkCfVlPQYGILw+RvGj25j5xGcpfnQb4fUxPYz9Zcugrg6Gh6WWzvCwzBXHHurM\ne/lXAsziwsQAXJgEmOW9/KtqabBxo/Sd2r9fzr7375f5xo1KZTXlEtw4JT1nS4Ybn5XhxqkemnIJ\npboAcjt+giObwVOcw2vO4inO4chmyO34iVJdOjc9b/OMcN2HQgQCUgc1EIDrPhSizaO2kPLCPr1u\nt359eiucy5iwEvAZ27Y7gE3AJw3DWAN8Dthp2/YqYGd5rhydi2j2N8T5t58kMadS4nGaSvFvP0nS\n36DW2Fl2dYwfB7txRQKsaxjCFQnw42A3y66OKdUFMPDdPp7YFWXKDON0ydvZE7uiDHxXbbya1sZ+\nY6M0um1qkl2rqUnmjeqrv+tKG69h4Xy9yrpVbl3UxmuKlSFJPPm8/Nj2/O+XXqpU1tbmPsZKUaYt\nCXKatsKMlaJsbVYfS+qdHsNDDrvcB8HGwEMO7/SYamnaNj2nqYm2aJotW+CP/gi2bIG2qHonwZsl\nM+jSp3ch58wIs2172LbtX5Z/TwP9wBLgg8C3y//at4EPnSsNvwqdnXDzzdJxoadHxptv1uNC/8EL\nMQ6t78YdCeC0irgjAQ6t7+YHL8SU6rr9dvCvifFEwza+XvtZnmjYhn9NjNtvVyoLgP7eEWYdIU4N\nSMr0qQGYdYTo71X7dqazsY9ty2vsypVnjqrremiMEwsTR7l3pEERNyYOnOWWN0o5eZLhzhvYbV7E\n86Pt7DYvYrjzBoklVcjmC0cINEmaslX+mAJNITZfqPbeBChYbtwUqLWT1Nnj1NpJ3BQoWG7V0vQl\nHpdNLJWSLzSVknlcrZNA52SGhRj2O7DBGoYRA54F1gIDtm1HFvyzpG3bbziSNAzjE8AnABYvXnzx\n97///XOqMZuVQG6nk9fjm0wTFi+WrFaVnDoljgnDAK93lnw+iG2LW1r1SVE2K8mahQJ4PJIzoPrz\nAhjrn6JUMCnZTgItDjJDFi7DxOVx0thRp1Sbrp8Zo6MUiwbFdI5iQwD3RAZ3yIfbbcuNoBgdP7fi\ny/twYWIDmdZWAoODGEAJJ+6L16vVNjjKTNrAa+dwYmLiJG/4qA3ZuFsVfp9TUxRzJrNZJ4QdkLII\n1pi4fU45/lZI9uV+fGSRFlQVDHLUUHNxhypZZzA7O0tQszjN3EyB3PgsdqGI4XHjWxTEV+tRqml4\nWEJaXa7552Zl/k5Uqbjmmmtetm37krf79865EWYYRhB4BviSbds7DMOY/o8YYQu55JJL7Jdeeumc\n6rzrrjdmn1Tmd911Tv/Xb8tCbeed18vhw13aaHvwQbj3XukssGSJ1FvbskWtJoDfWpvg0uM9zHmi\nrP3LRg7+jzH8hSQvrujmxwdjquVpycCXtvPY9zOMZMOc/8deXv2HPE01KT7w0QBtX1AbaL5/P/zT\n/0ywIdNHozXCmKOJPYE4f/AXMaXe6n8w/hu/zz/hocQL//tuNv7fn6eAi3/hD/hj+/+oEwY8fsPf\nsqz/cWocBRxWEcvhJmt5ONVxEzf9q8K6b5U2bNEovY2NdI2NyQbX3a28HtfR8AaaZl6VJvHYWBjY\nwEjt+axK7VGqrUJvby9dXV2qZbyOrvfmwgK3Gzf28sIL8tx8p2LpDMP4Dxlh57ROmGEYbuAh4Lu2\nbVdOYkcNw2gu//NmQP1hO+KizOWgtxceeUTGXE4P1+XCI6yFmTGqj7AefBDuvFO8E83NMt55p6yr\n5lgpxo+83cw5ArjsInOOAD/ydnOsFFMtTVu+diiOZ/A4l00/Sa2V4rLpJ/EMHudrh9QnWuz8VoJr\nR3uIujPM1rYQdWe4drSHnd9KKNV1v/czPM21TBPBxmCaCE9zLfd7P6NUF0BhZIr67GkcVpGiK4DD\nKlKfPU1hZEqtsFhMDK5AQNz5gYAWBhiA38iTop60EWWGKGkjSop6/EZetTRt0fXe1DmZYSHnrESF\nYRgG8C2g37btv13wjx4FbgX+pjw+cq40/Cp4vfDEE3LUYZowNibHgJs1KN9UuZh27Ji/mG67Tf3F\ndO+9Uuun0uqpMt57r3pvWE0NFFtjPDYbY5Wjl8fCXQSDUFMN7XhL9uyBDc4USwvHGbUuY3HhOHmP\nhx9p4AAI7pF2TwWfBHnkfWE8EVlXmQZf44PRfBNHWUUeH0dZxShN1PiUSXqddusYY4F2GucShApD\nFD0BxgLttFvHVEsTgysWk7ddjbw6c+5aHDUGnmwKH3lytpdCTRjLrbjVhsboem/CfJeN3l649Val\nUt6Sc+kJew+wDbjWMIy95Z/3IcbX9YZhHAWuL8+VMzEhhtf0NKTTMo6NyboOHDkiF1I6LeORI6oV\nyRHkKneC60e285HX7uH6ke2scie0aHq+adN8TJ/XK6NpynqVN+ea5A4i+TFetVaTtkO8aq0mkh/j\nmqT6dKI27whJ88wHYdIM0eZVnGjBDhYzxnFWkyHIcVazmDFuQYPPrH6OQHqElGcRk/W7H6g2AAAS\nRElEQVSrSXkWEUiP0FavuFCYxhwOXUJtdggPeSzDgYc8tdkhDofe9lTpPy263pu/KZwzT5ht27+A\ncsOyN3Ldufr//rrs2yfB75YlD2unUyrA79unWpkc7/3Jn4gXzDShv1/moNbjdFFdgisGeiiFoox7\nWgiYaa4c7MHRpr535O23w+CgGNKmKWsrV6JF5qaubHLt5mQuSsHtJ2rArOWnULTYFFHftqijq4mh\nR9NkjDB+v2S8WtNpOm5Wmwa/wdxN0hElZ/upBXKGn1nDYoOp/jOLLAliJUokpmF2CoIeiEVKRJao\nD+rWtXfk0GyYDGFqmMNplzBxkiHM0Gz47f/4Pym63pu/KVR7R5aZmRGPSV2dtJiqq5P5zIxqZfA3\nfyM91lwuMQxdLpn/jWIf4mcu62O8GGU8H8a2HYznw4wXo3zmMvX1fjo74ct3JPizlu00G6P8Wct2\nvnxHQouNXle8XvB6DByG5IY5DJl71Xfgoe1jcS5ZmWTiRIrnn7OYOJHikpVJ2j6mNl7N5QSXx8Ao\nv24ahsxdTqWyABitWcbJ4lKCAYtl9bMEAxYni0sZrVGbUq1zEc3m7HFO+DuYMBaRJsSEsYgT/g6a\ns8dVS9OWto/FeX/HcdYNP0nr3sdYN/wk7+84rvze/E2h2raoTCQiKa1zcxL8bhhi8OjQcP3ECYlx\ncpfjmdxumZ84oVbXZW0j2B9r4cmnIDkF0Tq48UMhLmtV3zuSRIKOvT103BiltzFA141J2NsDHXoE\nAOvI8bqNLJ97hsSMAbZNwJgjVjvF8bqruUyxtv0zMf5prpsNsT4usIYYczTxwNxm/mAmprQNyclF\nG2k+9gxZDMDGa81Rk5viZOvVClUJz02twd0WYHFpCF8+Rc4bZtTVwqmpZajM6XmzIpqVddUvSSFj\njtrsGNOuRUw4luCyCkSyYziC1WDSf4/GRoPGDUDeBi/Q+FaHYO8wiQT09UlNm+3bpXaZZvt/1Qgr\ns26dHF9VKnbYtvysW6dWF8j1UzlSq2Casq6UpiY2htJs3LTAVZ9KQ0ADN3Rfn+zulWp9lbGvT/lN\nWDmKGRiQSvm6HMUcXPVhOPwqqznEhHExDRzhSGaNrCtmxw6w2mKciMaovHtYSfUP7h32h7meYRYx\nhhMTA5MjrOYp+8PcoU4WAM+74lxuDdI308lkPkS9N017JMnzrrhSI0znPr1GOIhztoRN+RkAOClh\nhNUf4WpLXx8sXw4bNsyvpVLq99oFpVAIBMTl2tOjTSZuhepxZJmGBrgwkuDjnu3c6biHj3u2c2Ek\nQUODamVivOdyUqwSZMzllBckhnic4f4kj30nxdf+0eKx76QY7ldfKRmQpuehszKaQiFZV8jCo5jW\nVr2OYmwbpu1ajpgryNo1HDFXMG3XalEwX9fq17vHYnzLfQfPOq8ji59nndfxLfcd7B6LqRUGzDbE\nuG+ymxkzQKtziBkzwH2T3cw2qNWmc+uumcgy8o1LcRoWvtIsTsMi37iUmUi1f+pboule+6Yv4tGo\nrGtE1RNWJjiR4E9bejg5HWUy30LMm+aaSA/PT6gPMv/iF6Wa//Hj4gFzuWD9ellXyf6ZGH93oJu2\nU32Es0O8VNPEjuxmPq34iAh4ven5rv4w3AgPPgmbOtK0nqdPPzPQ6yhm0at9DHlXcMB5EZscXnb5\nbqTelWLRq+pTzdva3lhMWYcHt2XBKSPGgCtGp9HL911d2Da4NehaZNsw5InxRG0Mn09e3GZmYJVi\no/qWW+TFo6KxUvfwttvU6gIorlzDCTuA1zGEJ5uiUBMmX9eCZ2XVCHtLmpokbX/hW1Jafe9IRkag\npeXMtVAIhjQIl1lA1Qgrc3mpj2IgyrKlYeR2C1OcknXVD6DOTqm9VXmA/+Ef6nGEdf/90HsyRqEQ\no2SCKweek+C9H/7xH9Vq62+I88KXehjJwdKuRl57OcXsK0k2/t1mVDYf0fkoxpcaoVTTQlOw3L+7\nGbKzIXwp9ZvWwgd3ODzfnk71gzsclj3dVd5JTVNapejgQS8U4Kqr4PBh+bzCYbjwQllXia51DwF8\n18c52jtIMdCJ2RjCmUnjPp1k3R0aePd1JR6XYz4QIyedlptTdZFNXY3Ds6geR5bZ1D7CZDFEJiNv\nZ5kMTBZDbGrXo9ZJZ6ekci9bJqMOG1albhmAr1ycslLHTDX/8EiM7cVuss4AHqNI1hlge7Gbf3gk\nplSXzkcxuXATfjNNsfyQLhbAb6bJhdVvWgurXw8O6lP9urkZ/H4JMK8k9Pj9eiT0tLXJfdnVBR/8\noIw+nx7Xmo77Gcjx8tBV3RjBAKH0EEYwwNBV3VocL2vLwg4IQ0P6dEBY2FgctGksfjZVT1iZlg1N\n3OyX46vJSaivh+suSdOi+PhKZ6anpZ7awqzNUknWVbNrF3jqYzzjj7HE08szS7uYm5N1lejq0QEg\nHmdVbw+ns2BZjUQcKZY0JBmOa9A2gvnq1zrR2grj4/IdGoYYOdHoG72dKtD6WtOUgQFwN8V4KhUj\nVSOf23lNeniqtabSAUEnKsZhX998e6zNm7XTWTXCKsTjtA72sOV6znSpavIA0hGdy3oYb5Eh/Vbr\n7xQLj2Iq2ZG6HMVcd1uMzz3VTctAH02FIv0DAXYu38zf3BZTLQ3QM6u0uRkmXkpws7OPZsPDrc7t\n7DHjNDfH1ApD72tNV7xe8eTX1spPNgvPPqtVZ6UqvwqatsdaSNUIqxCL0X9hN/1f76M4OIS7tYmO\nT2ymQzOrWScWlvWoGGG6lPXYtEnuu4qmbFbsah3uQx09OgBPPQU/PxGjVIrRQS//XOrCdULWVeut\nZJVWvEyVrFLVR5LRVIJbM/fR5BwHx9Xc4HyGzsx+TqY+iepY0iq/OrY9n8BQKkmsn8eDFhnCVd6d\nVGPCyuzfD3/dE+OZtm28ctNneaZtG3/dE9OidICuNDRId4FIZL6R96JFegQl3367tCmCatui/yhf\n+Yo8bGpq5Ji5pkbmX/mKamVnZpU6HPO/71DconHd0R1c4t5HQ/oEjlKehvQJLnHvY91R9b0jdS6H\noisjI2J8zczM/5RK6qstVHn3UjXCyui6yetMPg833ACrVsmxzKpVMs/nVSsT78iXvgQ33SShADfd\nJHPVHh2dmZyUuD5nueVOJd5vclKtLtC3TljH2NO4UpO4vQ5wOnF7HbhSk3SMPa1WGNU97ddhcBBm\nZ+WlsrFRxtlZWa9S5VxQPY4so3PpAF2p1G5aeMSXTOoREwbzx369vXDrrarV6I/bPV+HroJpzide\nqETXOmGh3DjTeCga0mCzaHgxyBPJjasVRnVP+3WYnZ1/CangdMp6lSrngqonrIzOpQN05ZZb5gst\nWtb877eo7IlS5dfm8svF6Mrn5Rgyn5f55ZerVqbvtZZyN1BXW8BLHmzwkqeutkDKrf5MXuc9bf9+\nKU1x6pSMuhyR1tRIGSm3W65/t1vmNTWqlVV5t1I1wsrousnrjK61m6r8enzlK3DBBeIJs20ZL7hA\nj5gwXa+10TXXkvXVE41YuJ0m0YhF1lfP6Jpr1QpD3z1tYaya261XrNqmTfLisXgxnHeejKYp61Wq\nnAuqx5Flquncvx66ZvpV+dXp7IRvf1vugfp6+Mxn9CgDUUHHay326Vs4/plh6u1xTJeXEf9yJo1F\nrPi0+rc3Xfe0hbFqhqFX667bbxcjf2xMvIZebzWhp8q5pWqELUDHTb5KlXeSahzdr0bHTTHgk/R/\nvQ/D8DB8/nV0fCJeXlePjnuazrFqlYQe3erRVXn3UjXCqlSpUuX/Bx03xei4KUZvby9dt3eplqM9\nuiZZVNDRcK3y7qUaE1alSpUqVd4xFsaq2bY+sWpVqqig6gmrUqVKlXcriYT0zhsZkTS/eFx577yF\nsWrFonjEdIhVq1JFBVUjrEqVKlXejSQS0NMjVk5Li/Tt6umRpsYaGGLV2MMqVarHkVWqVKny7qSv\nTwywcFhK5ofDMu/rU62sSpUqZapGWJUqVaq8GxkZgVDozLVQqNoIsUoVjagaYVWqVKnybqSpSY4g\nF5JOy3qVKlW0oGqEValS5f9r745C5DrLMI7/HxqxJlGixsbaxKwVSZWitvaiGiPGWCha0gheGFQK\nFnojWkVRS0HECwkoWkFQpGoqhgZJK5aApSFVvLFCG21TG22Rak0TTaQ2Gr3Q2teLOaFLupvkILvf\nOTv/HyxnzpnZmWdfdmbeOeeb82kp2rRp8rXDEycmp8w/cWKyvmlT62SSOjZhkrQUzcxMBuGvWAFH\njkyWAxiUL+k5fjtSkpaqmRmbLmnA3BMmSZLUgE2YJElSAzZhkiRJDdiESZIkNWATJkmS1IBNmCRJ\nUgM2YZIkSQ3YhEmSJDWwYE1Yku8mOZbk4VnbXpZkX5LHuuVLF+rxJUmShmwh94TtBK4+bdvngP1V\n9Tpgf7cuSZI0dRasCauqnwNPnbb5WuC27vJtwLaFenxJkqQhW+wxYWuq6ihAt7xgkR9fkiRpEFJV\nC3fnyQywt6ou7dafrqpVs67/W1XNOS4syQ3ADQBr1qx5y+7duxcs55icPHmSlStXto4xKtasP2vW\nnzXrz5r1Z836a1GzzZs3P1BVV5ztdovdhP0OeGdVHU1yIfCzqtpwDvdzHPjjggUdl9XAX1uHGBlr\n1p8168+a9WfN+rNm/bWo2fqqesXZbrRsMZLMchdwHbCjW/74XH7pXP6QaZHk/nPprvUca9afNevP\nmvVnzfqzZv0NuWYLeYqK24FfABuSHE5yPZPm66okjwFXdeuSJElTZ8H2hFXV9nmu2rJQjylJkjQW\nnjF/fL7dOsAIWbP+rFl/1qw/a9afNetvsDVb0IH5kiRJmpt7wiRJkhqwCRuBJOuS/DTJoSS/SXJj\n60xjkeS8JL9Ksrd1lrFIsirJniS/7f7n3to609Al+WT33Hw4ye1Jzm+daWicT7i/eWr25e65+VCS\nHyVZdab7mDZz1WzWdZ9OUklWt8g2F5uwcXgG+FRVvR64Evhokjc0zjQWNwKHWocYma8Dd1fVJcCb\nsH5nlOQi4OPAFd05Ec8DPtA21SDtxPmE+9rJ82u2D7i0qt4IPArctNihBm4nz68ZSdYxOSvDE4sd\n6Exswkagqo5W1YHu8j+YvCle1DbV8CVZC7wXuLV1lrFI8hLgHcB3AKrq31X1dNtUo7AMeFGSZcBy\n4EjjPIPjfML9zVWzqrqnqp7pVu8D1i56sAGb5/8M4GvAZ4BBDYS3CRuZbhaCy4Bftk0yCrcwedI9\n2zrIiFwMHAe+1x3GvTXJitahhqyqngS+wuQT9lHgRFXd0zbVaDif8P/nI8BPWocYuiRbgSer6sHW\nWU5nEzYiSVYCdwCfqKq/t84zZEmuAY5V1QOts4zMMuBy4JtVdRnwTzxEdEbdOKZrgdcArwJWJPlQ\n21Ra6pLczGSoyq7WWYYsyXLgZuDzrbPMxSZsJJK8gEkDtquq7mydZwQ2AluT/AHYDbwryQ/aRhqF\nw8Dhqjq1p3UPk6ZM83s38HhVHa+q/wB3Am9rnGks/tLNI0y3PNY4zygkuQ64BvhgeZ6ps3ktkw9I\nD3bvB2uBA0le2TRVxyZsBJKEyRidQ1X11dZ5xqCqbqqqtVU1w2SQ9L1V5d6Js6iqPwN/SrKh27QF\neKRhpDF4ArgyyfLuuboFv8xwrk7NJww95hOeZkmuBj4LbK2qf7XOM3RVdbCqLqiqme794DBwefda\n15xN2DhsBD7MZG/Or7uf97QOpSXrY8CuJA8Bbwa+1DjPoHV7DfcAB4CDTF5XB3uG7lacT7i/eWr2\nDeDFwL7uveBbTUMOzDw1GyzPmC9JktSAe8IkSZIasAmTJElqwCZMkiSpAZswSZKkBmzCJEmSGrAJ\nkzQVkrwvSSW5ZNa2/8467ctdLfNJmj6eokLSVEjyQ+BCYH9VfaHbdrKqVjYNJmlquSdM0pLXzbu6\nEbieyQwKktScTZikabANuLuqHgWeSnJqPszzk9yf5L4k2xrmkzSFlrUOIEmLYDtwS3d5d7d+AHh1\nVR1JcjFwb5KDVfX7ViElTRfHhEla0pK8nMmkvceAAs7rlutr1gtgkp3A3qra0yKnpOnj4UhJS937\nge9X1fqqmqmqdcDjwNuTvBAgyWomY8YeaZhT0pTxcKSkpW47sOO0bXcAXwRWJ3mWyQfSHVVlEyZp\n0Xg4UpIkqQEPR0qSJDVgEyZJktSATZgkSVIDNmGSJEkN2IRJkiQ1YBMmSZLUgE2YJElSAzZhkiRJ\nDfwP+3FqA7R+a58AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c6cc5e208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 6))\n",
    "\n",
    "plt.scatter(data[data['class'] == 0]['A5'],\n",
    "            data[data['class'] == 0]['A2'],\n",
    "            alpha = 0.5,\n",
    "            label = 'class 0',\n",
    "            color = 'b')\n",
    "\n",
    "plt.scatter(data[data['class'] == 1]['A5'],\n",
    "            data[data['class'] == 1]['A2'],\n",
    "            alpha = 0.35,\n",
    "            label = 'class 1',\n",
    "            color = 'r')\n",
    "\n",
    "plt.xlabel('A5')\n",
    "#plt.xticks(range(2))\n",
    "#plt.xlim(0, 1)\n",
    "plt.ylabel('A2')\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmEAAAHjCAYAAABrZcgFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSZJ6wBAmSZLUA4YwSZKkHjCESZIk9cBPFO3vQr2RYI8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c6a801e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "attribute_5_rnd = data['A5'] #+ np.random.rand(data.shape[0]) - .5\n",
    "\n",
    "plt.figure(figsize = (10, 8))\n",
    "\n",
    "plt.scatter(attribute_5_rnd[data['class'] == 0],\n",
    "            data[data['class'] == 0]['A3'],\n",
    "            alpha = 0.5,\n",
    "            label = 'class 0',\n",
    "            color = 'b')\n",
    "\n",
    "plt.scatter(attribute_5_rnd[data['class'] == 1],\n",
    "            data[data['class'] == 1]['A3'],\n",
    "            alpha = 0.35,\n",
    "            label = 'class 1',\n",
    "            color = 'r')\n",
    "\n",
    "plt.xlabel('Attribute 5')\n",
    "plt.ylabel('Attribute 3')\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#data.describe(include = ['int64'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Значения атрибута А5 и его тип:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8,  4,  5,  6,  3, 11,  2, 14,  9, 13,  7,  1, 10, 12], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['A5'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Для первого упоминания каждой категории атрибута А5 выводим соответсвующее значение атрибута A2:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 22.67\n",
      "4 29.58\n",
      "5 21.67\n",
      "6 20.17\n",
      "3 17.42\n",
      "11 58.67\n",
      "2 27.83\n",
      "14 33.5\n",
      "9 29.58\n",
      "13 32.75\n",
      "7 42.83\n",
      "1 74.83\n",
      "10 29.5\n",
      "12 33.17\n"
     ]
    }
   ],
   "source": [
    "for category in data['A5'].unique():\n",
    "    print(category, data['A2'][data['A5'] == category].iloc[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Задача\n",
    "\n",
    "Будем определять принадлежность к классу (0 или 1) по данным параметрам"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Убираем незначащие данные:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#data = data.drop('capital-gain', axis = 1)\\\n",
    "#            .drop('capital-loss', axis = 1)\\\n",
    "#           .drop('education',    axis = 1)\\\n",
    "#           .drop('native-country', axis = 1).drop('fnlwgt', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подготовка данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A1', 'A4', 'A5', 'A8', 'A9', 'A11', 'A12', 'class']\n",
      "['A2', 'A3', 'A6', 'A7', 'A10', 'A13', 'A14']\n"
     ]
    }
   ],
   "source": [
    "#Категоричные колонки\n",
    "#categorical = [c for c in data.columns if data[c].dtype.name == 'int64']\n",
    "categorical = [u'A1', u'A4', u'A5', u'A8', u'A9', u'A11', u'A12', u'class']\n",
    "#Колонки с численными данными\n",
    "numerical   = [c for c in data.columns if c not in categorical]\n",
    "print(categorical)\n",
    "print(numerical)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Обработка пропущенных значений\n",
    "Если данные содержат пропущенные значения, то имеется две простые альтернативы:  \n",
    "* удалить столбцы с такими значениями (data = data.dropna(axis = 1)),  \n",
    "* удалить строки с такими значениями (data = data.dropna(axis = 0)).\n",
    "  \n",
    "После этого данных может стать совсем мало.  \n",
    "  \n",
    "Куда более эффективным (и в то же время по-прежнему простым) методом обычно является заполнение пропущенных значений для каждой количественной переменной их средним или медианным значением. Для категориальных признаков пропущенные значения можно заполнить самым популярным значением в столбце."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A1       689\n",
       "A2       689\n",
       "A3       689\n",
       "A4       689\n",
       "A5       689\n",
       "A6       689\n",
       "A7       689\n",
       "A8       689\n",
       "A9       689\n",
       "A10      689\n",
       "A11      689\n",
       "A12      689\n",
       "A13      689\n",
       "A14      689\n",
       "class    689\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.count(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В описании данной базы данных говорится, что 37 строк (около 5% всех данных) содержали один или больше пропущенных значений.   \n",
    "\n",
    "Аттрибуты из которых были пропущены значения:  \n",
    "  \n",
    "    A1:  12\n",
    "    A2:  12\n",
    "    A4:   6\n",
    "    A5:   6\n",
    "    A6:   9\n",
    "    A7:   9\n",
    "    A14: 13\n",
    "    \n",
    "   Эти значения были заменены:\n",
    "   * на моду данного атрибута (для категориального признака)\n",
    "   * на среднее значение по данному атрибуту (для численного признака)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Векторизация\n",
    "В данной базе данных значения категориальных признаков уже были представлены в числовом виде, поэтому векторизацию проводить не нужно."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['A1', 'A8', 'A9', 'A11', 'class'] ['A4', 'A5', 'A12']\n"
     ]
    }
   ],
   "source": [
    "binary_columns    = [c for c in categorical if len(data[c].unique()) == 2]\n",
    "nonbinary_columns = [c for c in categorical if len(data[c].unique()) > 2]\n",
    "print(binary_columns, nonbinary_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Нормализация количественных признаков\n",
    "Многие алгоритмы машинного обучения чувствительны к масштабированию данных. К таким алгоритмам, например, относится метод ближайших соседей, машина опорных векторов и др.  \n",
    "  \n",
    "В этом случае количественные признаки полезно нормализовать. Это можно делать разными способами. Например, каждый количественный признак приведем к нулевому среднему и единичному среднеквадратичному отклонению:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A10</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.581974</td>\n",
       "      <td>4.748999</td>\n",
       "      <td>4.693759</td>\n",
       "      <td>2.224332</td>\n",
       "      <td>2.403483</td>\n",
       "      <td>184.136430</td>\n",
       "      <td>1018.103048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.856359</td>\n",
       "      <td>4.975215</td>\n",
       "      <td>1.993588</td>\n",
       "      <td>3.348856</td>\n",
       "      <td>4.865611</td>\n",
       "      <td>172.254524</td>\n",
       "      <td>5213.882348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>13.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.670000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.670000</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>37.750000</td>\n",
       "      <td>7.080000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>272.000000</td>\n",
       "      <td>395.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>80.250000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>28.500000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>100001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A2          A3          A6          A7         A10  \\\n",
       "count  689.000000  689.000000  689.000000  689.000000  689.000000   \n",
       "mean    31.581974    4.748999    4.693759    2.224332    2.403483   \n",
       "std     11.856359    4.975215    1.993588    3.348856    4.865611   \n",
       "min     13.750000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%     22.670000    1.000000    4.000000    0.165000    0.000000   \n",
       "50%     28.670000    2.750000    4.000000    1.000000    0.000000   \n",
       "75%     37.750000    7.080000    5.000000    2.625000    3.000000   \n",
       "max     80.250000   28.000000    9.000000   28.500000   67.000000   \n",
       "\n",
       "               A13            A14  \n",
       "count   689.000000     689.000000  \n",
       "mean    184.136430    1018.103048  \n",
       "std     172.254524    5213.882348  \n",
       "min       0.000000       1.000000  \n",
       "25%      80.000000       1.000000  \n",
       "50%     160.000000       6.000000  \n",
       "75%     272.000000     395.000000  \n",
       "max    2000.000000  100001.000000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numerical = data[numerical]\n",
    "data_numerical.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A10</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.890000e+02</td>\n",
       "      <td>6.890000e+02</td>\n",
       "      <td>6.890000e+02</td>\n",
       "      <td>6.890000e+02</td>\n",
       "      <td>6.890000e+02</td>\n",
       "      <td>6.890000e+02</td>\n",
       "      <td>6.890000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.601074e-15</td>\n",
       "      <td>4.730936e-16</td>\n",
       "      <td>1.200459e-17</td>\n",
       "      <td>5.894334e-16</td>\n",
       "      <td>3.910757e-16</td>\n",
       "      <td>3.931704e-17</td>\n",
       "      <td>-7.239009e-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.504001e+00</td>\n",
       "      <td>-9.545313e-01</td>\n",
       "      <td>-1.852819e+00</td>\n",
       "      <td>-6.642066e-01</td>\n",
       "      <td>-4.939735e-01</td>\n",
       "      <td>-1.068979e+00</td>\n",
       "      <td>-1.950759e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-7.516619e-01</td>\n",
       "      <td>-7.535350e-01</td>\n",
       "      <td>-3.479952e-01</td>\n",
       "      <td>-6.149361e-01</td>\n",
       "      <td>-4.939735e-01</td>\n",
       "      <td>-6.045498e-01</td>\n",
       "      <td>-1.950759e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.456044e-01</td>\n",
       "      <td>-4.017914e-01</td>\n",
       "      <td>-3.479952e-01</td>\n",
       "      <td>-3.655972e-01</td>\n",
       "      <td>-4.939735e-01</td>\n",
       "      <td>-1.401207e-01</td>\n",
       "      <td>-1.941170e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.202294e-01</td>\n",
       "      <td>4.685228e-01</td>\n",
       "      <td>1.536129e-01</td>\n",
       "      <td>1.196431e-01</td>\n",
       "      <td>1.225985e-01</td>\n",
       "      <td>5.100799e-01</td>\n",
       "      <td>-1.195085e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.104804e+00</td>\n",
       "      <td>4.673366e+00</td>\n",
       "      <td>2.160045e+00</td>\n",
       "      <td>7.846162e+00</td>\n",
       "      <td>1.327614e+01</td>\n",
       "      <td>1.054175e+01</td>\n",
       "      <td>1.898449e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 A2            A3            A6            A7           A10  \\\n",
       "count  6.890000e+02  6.890000e+02  6.890000e+02  6.890000e+02  6.890000e+02   \n",
       "mean   6.601074e-15  4.730936e-16  1.200459e-17  5.894334e-16  3.910757e-16   \n",
       "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "min   -1.504001e+00 -9.545313e-01 -1.852819e+00 -6.642066e-01 -4.939735e-01   \n",
       "25%   -7.516619e-01 -7.535350e-01 -3.479952e-01 -6.149361e-01 -4.939735e-01   \n",
       "50%   -2.456044e-01 -4.017914e-01 -3.479952e-01 -3.655972e-01 -4.939735e-01   \n",
       "75%    5.202294e-01  4.685228e-01  1.536129e-01  1.196431e-01  1.225985e-01   \n",
       "max    4.104804e+00  4.673366e+00  2.160045e+00  7.846162e+00  1.327614e+01   \n",
       "\n",
       "                A13           A14  \n",
       "count  6.890000e+02  6.890000e+02  \n",
       "mean   3.931704e-17 -7.239009e-17  \n",
       "std    1.000000e+00  1.000000e+00  \n",
       "min   -1.068979e+00 -1.950759e-01  \n",
       "25%   -6.045498e-01 -1.950759e-01  \n",
       "50%   -1.401207e-01 -1.941170e-01  \n",
       "75%    5.100799e-01 -1.195085e-01  \n",
       "max    1.054175e+01  1.898449e+01  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_numerical = (data_numerical - data_numerical.mean(axis = 0))/data_numerical.std(axis = 0)\n",
    "data_numerical.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Соединяем все в одну таблицу"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(689, 15)\n"
     ]
    }
   ],
   "source": [
    "data = pd.concat((data_numerical, data[nonbinary_columns], data[binary_columns]), axis = 1)\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A2</th>\n",
       "      <th>A3</th>\n",
       "      <th>A6</th>\n",
       "      <th>A7</th>\n",
       "      <th>A10</th>\n",
       "      <th>A13</th>\n",
       "      <th>A14</th>\n",
       "      <th>A4</th>\n",
       "      <th>A5</th>\n",
       "      <th>A12</th>\n",
       "      <th>A1</th>\n",
       "      <th>A8</th>\n",
       "      <th>A9</th>\n",
       "      <th>A11</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.890000e+02</td>\n",
       "      <td>6.890000e+02</td>\n",
       "      <td>6.890000e+02</td>\n",
       "      <td>6.890000e+02</td>\n",
       "      <td>6.890000e+02</td>\n",
       "      <td>6.890000e+02</td>\n",
       "      <td>6.890000e+02</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "      <td>689.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.601074e-15</td>\n",
       "      <td>4.730936e-16</td>\n",
       "      <td>1.200459e-17</td>\n",
       "      <td>5.894334e-16</td>\n",
       "      <td>3.910757e-16</td>\n",
       "      <td>3.931704e-17</td>\n",
       "      <td>-7.239009e-17</td>\n",
       "      <td>1.766328</td>\n",
       "      <td>7.377358</td>\n",
       "      <td>1.928882</td>\n",
       "      <td>0.677794</td>\n",
       "      <td>0.523948</td>\n",
       "      <td>0.428157</td>\n",
       "      <td>0.457184</td>\n",
       "      <td>0.445573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.430283</td>\n",
       "      <td>3.683694</td>\n",
       "      <td>0.299018</td>\n",
       "      <td>0.467661</td>\n",
       "      <td>0.499789</td>\n",
       "      <td>0.495171</td>\n",
       "      <td>0.498525</td>\n",
       "      <td>0.497390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.504001e+00</td>\n",
       "      <td>-9.545313e-01</td>\n",
       "      <td>-1.852819e+00</td>\n",
       "      <td>-6.642066e-01</td>\n",
       "      <td>-4.939735e-01</td>\n",
       "      <td>-1.068979e+00</td>\n",
       "      <td>-1.950759e-01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-7.516619e-01</td>\n",
       "      <td>-7.535350e-01</td>\n",
       "      <td>-3.479952e-01</td>\n",
       "      <td>-6.149361e-01</td>\n",
       "      <td>-4.939735e-01</td>\n",
       "      <td>-6.045498e-01</td>\n",
       "      <td>-1.950759e-01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-2.456044e-01</td>\n",
       "      <td>-4.017914e-01</td>\n",
       "      <td>-3.479952e-01</td>\n",
       "      <td>-3.655972e-01</td>\n",
       "      <td>-4.939735e-01</td>\n",
       "      <td>-1.401207e-01</td>\n",
       "      <td>-1.941170e-01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.202294e-01</td>\n",
       "      <td>4.685228e-01</td>\n",
       "      <td>1.536129e-01</td>\n",
       "      <td>1.196431e-01</td>\n",
       "      <td>1.225985e-01</td>\n",
       "      <td>5.100799e-01</td>\n",
       "      <td>-1.195085e-01</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.104804e+00</td>\n",
       "      <td>4.673366e+00</td>\n",
       "      <td>2.160045e+00</td>\n",
       "      <td>7.846162e+00</td>\n",
       "      <td>1.327614e+01</td>\n",
       "      <td>1.054175e+01</td>\n",
       "      <td>1.898449e+01</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 A2            A3            A6            A7           A10  \\\n",
       "count  6.890000e+02  6.890000e+02  6.890000e+02  6.890000e+02  6.890000e+02   \n",
       "mean   6.601074e-15  4.730936e-16  1.200459e-17  5.894334e-16  3.910757e-16   \n",
       "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
       "min   -1.504001e+00 -9.545313e-01 -1.852819e+00 -6.642066e-01 -4.939735e-01   \n",
       "25%   -7.516619e-01 -7.535350e-01 -3.479952e-01 -6.149361e-01 -4.939735e-01   \n",
       "50%   -2.456044e-01 -4.017914e-01 -3.479952e-01 -3.655972e-01 -4.939735e-01   \n",
       "75%    5.202294e-01  4.685228e-01  1.536129e-01  1.196431e-01  1.225985e-01   \n",
       "max    4.104804e+00  4.673366e+00  2.160045e+00  7.846162e+00  1.327614e+01   \n",
       "\n",
       "                A13           A14          A4          A5         A12  \\\n",
       "count  6.890000e+02  6.890000e+02  689.000000  689.000000  689.000000   \n",
       "mean   3.931704e-17 -7.239009e-17    1.766328    7.377358    1.928882   \n",
       "std    1.000000e+00  1.000000e+00    0.430283    3.683694    0.299018   \n",
       "min   -1.068979e+00 -1.950759e-01    1.000000    1.000000    1.000000   \n",
       "25%   -6.045498e-01 -1.950759e-01    2.000000    4.000000    2.000000   \n",
       "50%   -1.401207e-01 -1.941170e-01    2.000000    8.000000    2.000000   \n",
       "75%    5.100799e-01 -1.195085e-01    2.000000   10.000000    2.000000   \n",
       "max    1.054175e+01  1.898449e+01    3.000000   14.000000    3.000000   \n",
       "\n",
       "               A1          A8          A9         A11       class  \n",
       "count  689.000000  689.000000  689.000000  689.000000  689.000000  \n",
       "mean     0.677794    0.523948    0.428157    0.457184    0.445573  \n",
       "std      0.467661    0.499789    0.495171    0.498525    0.497390  \n",
       "min      0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "25%      0.000000    0.000000    0.000000    0.000000    0.000000  \n",
       "50%      1.000000    1.000000    0.000000    0.000000    0.000000  \n",
       "75%      1.000000    1.000000    1.000000    1.000000    1.000000  \n",
       "max      1.000000    1.000000    1.000000    1.000000    1.000000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## X и y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = data.drop(('class'), axis = 1) # выбрасываем столбец 'class'\n",
    "y = data['class']\n",
    "feature_names = X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.base.Index"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['A2', 'A3', 'A6', 'A7', 'A10', 'A13', 'A14', 'A4', 'A5', 'A12', 'A1',\n",
       "       'A8', 'A9', 'A11'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(689, 14)\n",
      "(689,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)\n",
    "N, d = X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "482 207\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 42)\n",
    "\n",
    "N_train, _ = X_train.shape \n",
    "N_test,  _ = X_test.shape \n",
    "\n",
    "print(N_train, N_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "689"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_train+N_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# $k$NN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Объявляем классификатор с параметром k = 10. Проводим обучение модели на обучающей выборке X_train, y_train:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 10)\n",
    "#knn.set_params(n_neighbors=10)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Применяем метод. Считаем средние ошибки:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.163900414938 0.188405797101\n"
     ]
    }
   ],
   "source": [
    "y_train_predict = knn.predict(X_train)\n",
    "y_test_predict = knn.predict(X_test)\n",
    "\n",
    "err_train = np.mean(y_train != y_train_predict)\n",
    "err_test  = np.mean(y_test  != y_test_predict)\n",
    "\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Другой способ подсчета ошибки на тестовой выборке:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.188405797101\n"
     ]
    }
   ],
   "source": [
    "err_test = 1 - knn.score(X_test, y_test)\n",
    "print(err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*С помощью метода confusion_matrix построим матрицу, которая позволит оценить прогнозы классификатора. Элемент матрицы (i, j) равен числу объектов, которые, принадлежат группе i, но, по прогнозам, находятся в группе j.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[111  13]\n",
      " [ 26  57]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "print(confusion_matrix(y_test, y_test_predict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Подбор параметров"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.195020746888 15\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "nnb = [1, 3, 5, 10, 15, 20, 25, 35, 45, 55]\n",
    "knn = KNeighborsClassifier()\n",
    "grid = GridSearchCV(knn, param_grid = {'n_neighbors': nnb}, cv=10)\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "best_cv_err = 1 - grid.best_score_\n",
    "best_n_neighbors = grid.best_estimator_.n_neighbors\n",
    "print(best_cv_err, best_n_neighbors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Применим алгоритм еще раз с новым параметром k:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.174273858921 0.149758454106\n"
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = best_n_neighbors).fit(X_train, y_train)\n",
    "\n",
    "err_train = np.mean(y_train != knn.predict(X_train))\n",
    "err_test  = np.mean(y_test  != knn.predict(X_test))\n",
    "\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Ошибку удалось уменьшить, но ненамного. Возможно, это из-за того, что используемая выборка достаточно маленькая по размеру и алгоритм не успевает хорошо обучиться.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Нейронные сети\n",
    "\n",
    "*Обучить на ваших данных полносвязную (неглубокую) нейронную сеть. Попробуйте 1-2 скрытых слоя. Постройте графики зависимости ошибки на обучающей выборке и тестовой выборках от параметра alpha и от количества нейронов. Наблюдается ли переобучение/недообучение? *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "*Будем предсказывать класс*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(689, 14)\n",
      "(689,)\n"
     ]
    }
   ],
   "source": [
    "X = data.drop(('class'), axis = 1)\n",
    "y = data['class']\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 42)\n",
    "N_test, _ = X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Попробуем сравнить нейронные сети (НС) с 1 и 2 слоями.*  \n",
    "*Рассмотрим НС с 1 скрытым слоем:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='logistic', alpha=0.0001, batch_size='auto',\n",
       "       beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=10, learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=42, shuffle=True,\n",
       "       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp_model = MLPClassifier(hidden_layer_sizes = (10), solver = 'lbfgs', \n",
    "                          activation = 'logistic', random_state = 42)\n",
    "mlp_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train_pred = mlp_model.predict(X_train)\n",
    "y_test_pred = mlp_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Посмотрим на ошибку на обучающей и тестовой выборках*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.100775193798 0.127167630058\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(y_train != y_train_pred), np.mean(y_test != y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Рассмотрим НС с 2 скрытыми слоями по 10 нейронов в каждом:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(activation='logistic', alpha=0.0001, batch_size='auto',\n",
       "       beta_1=0.9, beta_2=0.999, early_stopping=False, epsilon=1e-08,\n",
       "       hidden_layer_sizes=(10, 10), learning_rate='constant',\n",
       "       learning_rate_init=0.001, max_iter=200, momentum=0.9,\n",
       "       nesterovs_momentum=True, power_t=0.5, random_state=42, shuffle=True,\n",
       "       solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlp_model1 = MLPClassifier(hidden_layer_sizes = (10, 10), solver = 'lbfgs', \n",
    "                          activation = 'logistic', random_state = 42)\n",
    "mlp_model1.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train_pred = mlp_model1.predict(X_train)\n",
    "y_test_pred = mlp_model1.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.104651162791 0.104046242775\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(y_train != y_train_pred), np.mean(y_test != y_test_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Очевидно, будем использовать НС с двумя скрытыми слоями, так как в этом случае получаем ошибку меньше.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Подбор параметра alpha и числа нейронов  \n",
    "*Подберем оптимальное значение параметра регуляризации alpha*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alpha_arr = np.logspace(-3, 2, 20)\n",
    "test_err = []\n",
    "train_err = []\n",
    "for alpha_1 in alpha_arr:\n",
    "    mlp_model = MLPClassifier(alpha = alpha_1, hidden_layer_sizes = (10,10), \n",
    "                              solver = 'lbfgs', activation = 'logistic', random_state = 42)\n",
    "    mlp_model.fit(X_train, y_train)\n",
    "\n",
    "    y_train_pred = mlp_model.predict(X_train)\n",
    "    y_test_pred = mlp_model.predict(X_test)\n",
    "    train_err.append(np.mean(y_train != y_train_pred))\n",
    "    test_err.append(np.mean(y_test != y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEaCAYAAADQVmpMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c6a716518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogx(alpha_arr, train_err, 'b-o', label = 'test')\n",
    "plt.semilogx(alpha_arr, test_err, 'r-o', label = 'train')\n",
    "plt.xlim([np.min(alpha_arr), np.max(alpha_arr)])\n",
    "plt.title('Error vs. alpha')\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('error')\n",
    "plt.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  1.00000000e-03   1.83298071e-03   3.35981829e-03   6.15848211e-03\n",
      "   1.12883789e-02   2.06913808e-02   3.79269019e-02   6.95192796e-02\n",
      "   1.27427499e-01   2.33572147e-01   4.28133240e-01   7.84759970e-01\n",
      "   1.43844989e+00   2.63665090e+00   4.83293024e+00   8.85866790e+00\n",
      "   1.62377674e+01   2.97635144e+01   5.45559478e+01   1.00000000e+02]\n"
     ]
    }
   ],
   "source": [
    "print(alpha_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*При значениях alpha от 0.78 до 4.83 можем наблюдать переобучение.* \n",
    "  \n",
    "*Минимальное значение ошибки на обучающей и тестовой выборках:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0542635658915 0.0809248554913\n"
     ]
    }
   ],
   "source": [
    "min_train_err = np.min(train_err)\n",
    "min_test_err = np.min(test_err)\n",
    "print(min_train_err, min_test_err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Оптимальное значение alpha:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.784759970351\n"
     ]
    }
   ],
   "source": [
    "alpha_opt = alpha_arr[test_err == min_test_err]\n",
    "alpha_opt = alpha_opt[0]\n",
    "print(alpha_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Найдем оптимальное количество нейронов\n",
    "*Используем в качестве alpha значение значение по умолчанию (0.0001). Количество скрытых слоев = 2.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "neurons_arr = np.arange(2, 20, 2)\n",
    "test_err = []\n",
    "train_err = []\n",
    "for neurons in neurons_arr:\n",
    "    mlp_model = MLPClassifier(hidden_layer_sizes = (neurons, neurons), \n",
    "                              solver = 'lbfgs', activation = 'logistic', random_state = 42)\n",
    "    mlp_model.fit(X_train, y_train)\n",
    "\n",
    "    y_train_pred = mlp_model.predict(X_train)\n",
    "    y_test_pred = mlp_model.predict(X_test)\n",
    "    train_err.append(np.mean(y_train != y_train_pred))\n",
    "    test_err.append(np.mean(y_test != y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c6ca5bba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(neurons_arr, train_err, 'b-o', label = 'train')\n",
    "plt.plot(neurons_arr, test_err, 'r-o', label = 'test')\n",
    "plt.xlim([np.max(neurons_arr), np.min(neurons_arr)])\n",
    "plt.title('Error vs. neurons count')\n",
    "plt.xlabel('neurons count')\n",
    "plt.ylabel('error')\n",
    "plt.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Минимальное значение ошибки:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0329457364341 0.0809248554913\n"
     ]
    }
   ],
   "source": [
    "min_train_err = np.min(train_err)\n",
    "min_test_err = np.min(test_err)\n",
    "print(min_train_err, min_test_err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*при которой оптимальное число нейронов:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6]\n"
     ]
    }
   ],
   "source": [
    "neurons_opt = neurons_arr[test_err == min_test_err]\n",
    "print(neurons_opt)\n",
    "neurons_opt = neurons_opt[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Домашнее задание №5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest  \n",
    "*Проведем классификацию по классам с помощью случайного леса.*\n",
    "\n",
    "*Разобъем данные на тестовую и обучающую выборки.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Обучаем классификатор вида случайного леса. Число деревьев = 10*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import ensemble\n",
    "rf = ensemble.RandomForestClassifier(n_estimators = 10)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Посмотрим величину ошибки на тестовой и обучающей выборках:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0151843817787 0.131578947368\n"
     ]
    }
   ],
   "source": [
    "err_train = np.mean(y_train != rf.predict(X_train))\n",
    "err_test  = np.mean(y_test  != rf.predict(X_test))\n",
    "\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Наблюдаем переобучение, т.к. ошибка на тестовой выборке оказалась выше, чем на обучающей, и составила ~0.13*\n",
    "\n",
    "*Построим график зависимости ошибки на тестовой и обучающей выборках, а также oob-ошибки от количества деревьев*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trees_arr = np.arange(15, 100, 5)\n",
    "test_err = []\n",
    "train_err = []\n",
    "oob_err = []\n",
    "for trees in trees_arr:\n",
    "    rf = ensemble.RandomForestClassifier(n_estimators = trees, oob_score=True)\n",
    "    rf.fit(X_train, y_train)\n",
    "    train_err.append(np.mean(y_train != rf.predict(X_train)))\n",
    "    test_err.append(np.mean(y_test  != rf.predict(X_test)))\n",
    "    oob_err.append(1 - rf.oob_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEWCAYAAABMoxE0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c68972f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(trees_arr, train_err, 'b-o', label = 'train')\n",
    "plt.plot(trees_arr, test_err, 'r-o', label = 'test')\n",
    "plt.plot(trees_arr, oob_err, 'g-o', label = 'oob')\n",
    "plt.xlim([np.max(trees_arr), np.min(trees_arr)])\n",
    "plt.title('Error vs. number of trees')\n",
    "plt.xlabel('trees')\n",
    "plt.ylabel('error')\n",
    "plt.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Наблюдаем переобучение. Для уменьшения oob-ошибки количество деревьев не должно быть малым.*  \n",
    "*Минимум oob-ошибки наблюдается при количестве деревьев, равном:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[55]\n"
     ]
    }
   ],
   "source": [
    "min_oob_err = np.min(oob_err)\n",
    "trees_opt = trees_arr[oob_err == min_oob_err]\n",
    "print(trees_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Минимальное значение ошибки на тестовой и обучающей выборках:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.114035087719\n"
     ]
    }
   ],
   "source": [
    "min_train_err = np.min(train_err)\n",
    "min_test_err = np.min(test_err)\n",
    "print(min_train_err, min_test_err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*при котором оптимальное количество деревьев:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30\n"
     ]
    }
   ],
   "source": [
    "trees_opt = trees_arr[test_err == min_test_err]\n",
    "trees_opt = trees_opt[0]\n",
    "print(trees_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extremely Randomized Trees  \n",
    "*Проведем классификацию по классам с помощью Extremely Randomized Trees классификатора.*\n",
    "\n",
    "*Разобъем данные на тестовую и обучающую выборки*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.33, random_state = 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Обучаем классификатор вида Extremely Randomized Trees. Число деревьев = 100*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
       "           max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ert = ensemble.ExtraTreesClassifier(n_estimators = 100)\n",
    "ert.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Посмотрим величину ошибки на тестовой и обучающей выборках:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.127192982456\n"
     ]
    }
   ],
   "source": [
    "err_train = np.mean(y_train != ert.predict(X_train))\n",
    "err_test  = np.mean(y_test  != ert.predict(X_test))\n",
    "\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Наблюдаем переобучение, т.к. ошибка на тестовой выборке оказалась выше, чем на обучающей, и составила ~0.13*\n",
    "\n",
    "*Построим график зависимости ошибки на тестовой и обучающей выборках, а также oob-ошибки от количества деревьев. Указываем bootstrap=True для генерации случайной bootstrap-выборки из обучащей*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "trees_arr = np.arange(15, 100, 5)\n",
    "test_err = []\n",
    "train_err = []\n",
    "oob_err = []\n",
    "for trees in trees_arr:\n",
    "    ert = ensemble.ExtraTreesClassifier(n_estimators = trees, oob_score=True, bootstrap=True)\n",
    "    ert.fit(X_train, y_train)\n",
    "    train_err.append(np.mean(y_train != ert.predict(X_train)))\n",
    "    test_err.append(np.mean(y_test  != ert.predict(X_test)))\n",
    "    oob_err.append(1 - ert.oob_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c65d59a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(trees_arr, train_err, 'b-o', label = 'train')\n",
    "plt.plot(trees_arr, test_err, 'r-o', label = 'test')\n",
    "plt.plot(trees_arr, oob_err, 'g-o', label = 'oob')\n",
    "plt.xlim([np.max(trees_arr), np.min(trees_arr)])\n",
    "plt.title('Error vs. number of trees')\n",
    "plt.xlabel('trees')\n",
    "plt.ylabel('error')\n",
    "plt.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Наблюдаем переобучение. Как и для Random Forest видим, что малое количество деревьев негативно влияет на величину oob-ошибки.*  \n",
    "*Минимум oob-ошибки наблюдается при количестве деревьев:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[55]\n"
     ]
    }
   ],
   "source": [
    "min_oob_err = np.min(oob_err)\n",
    "trees_opt = trees_arr[oob_err == min_oob_err]\n",
    "print(trees_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "*Минимальное значение ошибки на обучающей и тестовой выборках:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.109649122807\n"
     ]
    }
   ],
   "source": [
    "min_train_err = np.min(train_err)\n",
    "min_test_err = np.min(test_err)\n",
    "print(min_train_err, min_test_err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*при котором оптимальное количество деревьев:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75\n"
     ]
    }
   ],
   "source": [
    "trees_opt = trees_arr[test_err == min_test_err]\n",
    "trees_opt = trees_opt[0]\n",
    "print(trees_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AdaBoost\n",
    "*Проведем классификацию по классам по трем сортам с помощью AdaBoost классификатора.*\n",
    "\n",
    "*Разобъем данные на тестовую и обучающую выборки*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.5, random_state = 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Обучаем классификатор вида Ada Boost. Число классификаторов (по умолчанию DecisionTreeClassifier) = 100*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
       "          learning_rate=1.0, n_estimators=100, random_state=None)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ab = ensemble.AdaBoostClassifier(n_estimators = 100)\n",
    "ab.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Посмотрим величину ошибки на тестовой и обучающей выборках:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.031976744186 0.179710144928\n"
     ]
    }
   ],
   "source": [
    "err_train = np.mean(y_train != ab.predict(X_train))\n",
    "err_test  = np.mean(y_test  != ab.predict(X_test))\n",
    "\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Наблюдаем переобучение, т.к. ошибка на тестовой выборке оказалась выше, чем на обучающей, и составила ~0.17*\n",
    "\n",
    "*Построим график зависимости ошибки на тестовой и обучающей выборках. Параметр learningrate уменьшает вклад каждого классификатора.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "estim_arr = np.arange(5, 100, 5)\n",
    "test_err = []\n",
    "train_err = []\n",
    "for estim in estim_arr:\n",
    "    ab = ensemble.AdaBoostClassifier(n_estimators = estim, learning_rate = 0.5)\n",
    "    ab.fit(X_train, y_train)\n",
    "    train_err.append(np.mean(y_train != ab.predict(X_train)))\n",
    "    test_err.append(np.mean(y_test  != ab.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nnHOuXP8P/IoLgC6TdwEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c69c7b978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(estim_arr, train_err, 'b-o', label = 'train')\n",
    "plt.plot(estim_arr, test_err, 'r-o', label = 'test')\n",
    "plt.xlim([np.max(estim_arr), np.min(estim_arr)])\n",
    "plt.title('Error vs. number of estimators')\n",
    "plt.xlabel('trees')\n",
    "plt.ylabel('error')\n",
    "plt.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95]\n"
     ]
    }
   ],
   "source": [
    "print(estim_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Наблюдаем достаточно низкую ошибку при количестве классификаторов <20. При большем количестве классификаторов имеется переобучение*\n",
    "\n",
    "*Минимальное значение ошибки:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0668604651163 0.136231884058\n"
     ]
    }
   ],
   "source": [
    "min_train_err = np.min(train_err)\n",
    "min_test_err = np.min(test_err)\n",
    "print(min_train_err, min_test_err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*при котором оптимальное количество классификаторов:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n"
     ]
    }
   ],
   "source": [
    "estim_opt = estim_arr[test_err == min_test_err]\n",
    "estim_opt = estim_opt[0]\n",
    "print(estim_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GBT\n",
    "*Проведем классификацию по классам с помощью Gradient Boosted Trees классификатора.*\n",
    "\n",
    "*Разобъем данные на тестовую и обучающую выборки*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.5, random_state = 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Обучаем классификатор вида Gradient Boosted Trees. Число деревьев = 100*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "              presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbt = ensemble.GradientBoostingClassifier(n_estimators = 100)\n",
    "gbt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Посмотрим величину ошибки на тестовой и обучающей выборках:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00581395348837 0.16231884058\n"
     ]
    }
   ],
   "source": [
    "err_train = np.mean(y_train != gbt.predict(X_train))\n",
    "err_test  = np.mean(y_test  != gbt.predict(X_test))\n",
    "\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Наблюдаем переобучение, т.к. ошибка на тестовой выборке оказалась выше, чем на обучающей, и составила ~0.16*\n",
    "\n",
    "*Построим график зависимости ошибки на тестовой и обучающей выборках. Параметр learningrate уменьшает вклад каждого дерева, по умолчанию равен 0.1*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trees_arr = np.arange(2, 20, 2)\n",
    "test_err = []\n",
    "train_err = []\n",
    "for trees in trees_arr:\n",
    "    gbt = ensemble.GradientBoostingClassifier(n_estimators = trees)\n",
    "    gbt.fit(X_train, y_train)\n",
    "    train_err.append(np.mean(y_train != gbt.predict(X_train)))\n",
    "    test_err.append(np.mean(y_test  != gbt.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c6c489b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(trees_arr, train_err, 'b-o', label = 'train')\n",
    "plt.plot(trees_arr, test_err, 'r-o', label = 'test')\n",
    "plt.xlim([np.max(trees_arr), np.min(trees_arr)])\n",
    "plt.title('Error vs. number of trees')\n",
    "plt.xlabel('trees')\n",
    "plt.ylabel('error')\n",
    "plt.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  4  6  8 10 12 14 16 18]\n"
     ]
    }
   ],
   "source": [
    "print(trees_arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Наблюдаем переобучение.*  \n",
    "  \n",
    "*Минимальное значение ошибки на обучающей и тестовой выборках:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0901162790698 0.115942028986\n"
     ]
    }
   ],
   "source": [
    "min_train_err = np.min(train_err)\n",
    "min_test_err = np.min(test_err)\n",
    "print(min_train_err, min_test_err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*при котором оптимальное количество деревьев:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    }
   ],
   "source": [
    "trees_opt = trees_arr[test_err == min_test_err]\n",
    "trees_opt = trees_opt[0]\n",
    "print(trees_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Principal component analysis (PCA)\n",
    "*Уменьшим размерность данных с помощью метода главных компонент. Далее проведем классификацию по классам с помощью классификатора Principal component analysis.*\n",
    "\n",
    "*Разобъем данные на тестовую и обучающую выборки*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.7, random_state = 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Обучаем PCA. Исходные данные содержат 15 столбцов. Уменьшим размерность до 13*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import decomposition\n",
    "pca = decomposition.PCA(n_components = 13)\n",
    "X_train_reduc = pca.fit_transform(X_train)\n",
    "X_test_reduc = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "*Обучаем классификатор вида Extremely Randomized Trees. Число деревьев = 100*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
       "           max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ert = ensemble.ExtraTreesClassifier(n_estimators = 100)\n",
    "ert.fit(X_train_reduc, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.171842650104\n"
     ]
    }
   ],
   "source": [
    "err_train = np.mean(y_train != ert.predict(X_train_reduc))\n",
    "err_test  = np.mean(y_test  != ert.predict(X_test_reduc))\n",
    "\n",
    "print(err_train, err_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Наблюдаем переобучение, т.к. ошибка на тестовой выборке оказалась выше, чем на обучающей, и составила ~0.17. Тестовая ошибка на не редуцированных данных (была получена ранее) составляла 0.14. На ошибку по обучающей выборке редукция размерности не повлияла.*\n",
    "\n",
    "*Построим график зависимости ошибки на тестовой и обучающей выборках от количества компонент в методе.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "components_arr = np.arange(1, 15, 1)\n",
    "test_err = []\n",
    "train_err = []\n",
    "oob_err = []\n",
    "for components in components_arr:\n",
    "    pca = decomposition.PCA(n_components = components)\n",
    "    X_train_reduc = pca.fit_transform(X_train)\n",
    "    X_test_reduc = pca.transform(X_test)\n",
    "    ert = ensemble.ExtraTreesClassifier(n_estimators = 100, oob_score=True, bootstrap=True)\n",
    "    ert.fit(X_train_reduc, y_train)\n",
    "    train_err.append(np.mean(y_train != ert.predict(X_train_reduc)))\n",
    "    test_err.append(np.mean(y_test  != ert.predict(X_test_reduc)))\n",
    "    oob_err.append(1 - ert.oob_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24c6a5f0f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(components_arr, train_err, 'b-o', label = 'train')\n",
    "plt.plot(components_arr, test_err, 'r-o', label = 'test')\n",
    "plt.plot(components_arr, oob_err, 'g-o', label = 'oob')\n",
    "plt.xlim([np.max(components_arr), np.min(components_arr)])\n",
    "plt.title('Error vs. number of components')\n",
    "plt.xlabel('components')\n",
    "plt.ylabel('error')\n",
    "plt.legend()\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*На графике видно, что при большей редукции размерности ошибка на тестовой выборке возрастает, т.е. наблюдаем переобучение. Величина OOB-ошибки немного больше и также увеличивается при уменьшении числа компонент.*\n",
    "\n",
    "*Минимальное значение ошибки:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0 0.144927536232\n"
     ]
    }
   ],
   "source": [
    "min_train_err = np.min(train_err)\n",
    "min_test_err = np.min(test_err)\n",
    "print(min_train_err, min_test_err)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*при которой оптимальное количество компонент:*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14\n"
     ]
    }
   ],
   "source": [
    "components_opt = components_arr[test_err == min_test_err]\n",
    "components_opt = components_opt[0]\n",
    "print(components_opt)"
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}