Borrower Reliability Study.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Исследование надёжности заёмщиков\n",
    "\n",
    "Заказчик — кредитный отдел банка. Нужно разобраться, влияет ли семейное положение и количество детей клиента на факт погашения кредита в срок. Входные данные от банка — статистика о платёжеспособности клиентов.\n",
    "\n",
    "Результаты исследования будут учтены при построении модели **кредитного скоринга** — специальной системы, которая оценивает способность потенциального заёмщика вернуть кредит банку."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 1. Откройте файл с данными и изучите общую информацию"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21525 entries, 0 to 21524\n",
      "Data columns (total 12 columns):\n",
      "children            21525 non-null int64\n",
      "days_employed       19351 non-null float64\n",
      "dob_years           21525 non-null int64\n",
      "education           21525 non-null object\n",
      "education_id        21525 non-null int64\n",
      "family_status       21525 non-null object\n",
      "family_status_id    21525 non-null int64\n",
      "gender              21525 non-null object\n",
      "income_type         21525 non-null object\n",
      "debt                21525 non-null int64\n",
      "total_income        19351 non-null float64\n",
      "purpose             21525 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from pymystem3 import Mystem\n",
    "from collections import Counter\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.read_csv('/datasets/data.csv')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "После вывода метода `.info()`  видим, что в двух столбцах: *days_employed*  и *total_income* есть отсутствующие значения. В остальном типы данных логически соответствуют столбцам. Стоит посмотреть сам датасет. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>-5623.422610</td>\n",
       "      <td>33</td>\n",
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       "      <td>3</td>\n",
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       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>340266.072047</td>\n",
       "      <td>53</td>\n",
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       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>158616.077870</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>-926.185831</td>\n",
       "      <td>27</td>\n",
       "      <td>высшее</td>\n",
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       "      <td>1</td>\n",
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       "      <td>255763.565419</td>\n",
       "      <td>покупка жилья</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>-2879.202052</td>\n",
       "      <td>43</td>\n",
       "      <td>высшее</td>\n",
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       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
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       "      <td>240525.971920</td>\n",
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       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>-152.779569</td>\n",
       "      <td>50</td>\n",
       "      <td>СРЕДНЕЕ</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
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       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>-6929.865299</td>\n",
       "      <td>35</td>\n",
       "      <td>ВЫСШЕЕ</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
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       "      <td>95856.832424</td>\n",
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       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>-2188.756445</td>\n",
       "      <td>41</td>\n",
       "      <td>среднее</td>\n",
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       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>144425.938277</td>\n",
       "      <td>покупка жилья для семьи</td>\n",
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       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
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       "      <td>113943.491460</td>\n",
       "      <td>покупка недвижимости</td>\n",
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       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>-792.701887</td>\n",
       "      <td>40</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>77069.234271</td>\n",
       "      <td>покупка коммерческой недвижимости</td>\n",
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       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>среднее</td>\n",
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       "      <td>1</td>\n",
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       "      <td>пенсионер</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>-1846.641941</td>\n",
       "      <td>54</td>\n",
       "      <td>неоконченное высшее</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>F</td>\n",
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       "      <td>0</td>\n",
       "      <td>130458.228857</td>\n",
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       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>-1844.956182</td>\n",
       "      <td>56</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>1</td>\n",
       "      <td>165127.911772</td>\n",
       "      <td>покупка жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>-972.364419</td>\n",
       "      <td>26</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>116820.904450</td>\n",
       "      <td>строительство собственной недвижимости</td>\n",
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       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>-1719.934226</td>\n",
       "      <td>35</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>289202.704229</td>\n",
       "      <td>недвижимость</td>\n",
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       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>-2369.999720</td>\n",
       "      <td>33</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>90410.586745</td>\n",
       "      <td>строительство недвижимости</td>\n",
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       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>400281.136913</td>\n",
       "      <td>53</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>вдовец / вдова</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>56823.777243</td>\n",
       "      <td>на покупку подержанного автомобиля</td>\n",
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       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>-10038.818549</td>\n",
       "      <td>48</td>\n",
       "      <td>СРЕДНЕЕ</td>\n",
       "      <td>1</td>\n",
       "      <td>в разводе</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>242831.107982</td>\n",
       "      <td>на покупку своего автомобиля</td>\n",
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       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>-1311.604166</td>\n",
       "      <td>36</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>209552.644578</td>\n",
       "      <td>недвижимость</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>-253.685166</td>\n",
       "      <td>33</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>131812.203909</td>\n",
       "      <td>приобретение автомобиля</td>\n",
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       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>-1766.644138</td>\n",
       "      <td>24</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>149681.140731</td>\n",
       "      <td>на покупку подержанного автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>-272.981385</td>\n",
       "      <td>21</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>128265.720871</td>\n",
       "      <td>сыграть свадьбу</td>\n",
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       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>338551.952911</td>\n",
       "      <td>57</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>290547.235997</td>\n",
       "      <td>операции с коммерческой недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>363548.489348</td>\n",
       "      <td>67</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>55112.757732</td>\n",
       "      <td>покупка недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>госслужащий</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>-529.191635</td>\n",
       "      <td>28</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>308848.983691</td>\n",
       "      <td>строительство собственной недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>-717.274324</td>\n",
       "      <td>26</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>187863.237306</td>\n",
       "      <td>строительство собственной недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>63</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>строительство жилой недвижимости</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    children  days_employed  dob_years            education  education_id  \\\n",
       "0          1   -8437.673028         42               высшее             0   \n",
       "1          1   -4024.803754         36              среднее             1   \n",
       "2          0   -5623.422610         33              Среднее             1   \n",
       "3          3   -4124.747207         32              среднее             1   \n",
       "4          0  340266.072047         53              среднее             1   \n",
       "5          0    -926.185831         27               высшее             0   \n",
       "6          0   -2879.202052         43               высшее             0   \n",
       "7          0    -152.779569         50              СРЕДНЕЕ             1   \n",
       "8          2   -6929.865299         35               ВЫСШЕЕ             0   \n",
       "9          0   -2188.756445         41              среднее             1   \n",
       "10         2   -4171.483647         36               высшее             0   \n",
       "11         0    -792.701887         40              среднее             1   \n",
       "12         0            NaN         65              среднее             1   \n",
       "13         0   -1846.641941         54  неоконченное высшее             2   \n",
       "14         0   -1844.956182         56               высшее             0   \n",
       "15         1    -972.364419         26              среднее             1   \n",
       "16         0   -1719.934226         35              среднее             1   \n",
       "17         0   -2369.999720         33               высшее             0   \n",
       "18         0  400281.136913         53              среднее             1   \n",
       "19         0  -10038.818549         48              СРЕДНЕЕ             1   \n",
       "20         1   -1311.604166         36              среднее             1   \n",
       "21         1    -253.685166         33              среднее             1   \n",
       "22         1   -1766.644138         24              среднее             1   \n",
       "23         0    -272.981385         21               высшее             0   \n",
       "24         1  338551.952911         57              среднее             1   \n",
       "25         0  363548.489348         67              среднее             1   \n",
       "26         0            NaN         41              среднее             1   \n",
       "27         0    -529.191635         28               высшее             0   \n",
       "28         1    -717.274324         26               высшее             0   \n",
       "29         0            NaN         63              среднее             1   \n",
       "\n",
       "            family_status  family_status_id gender  income_type  debt  \\\n",
       "0         женат / замужем                 0      F    сотрудник     0   \n",
       "1         женат / замужем                 0      F    сотрудник     0   \n",
       "2         женат / замужем                 0      M    сотрудник     0   \n",
       "3         женат / замужем                 0      M    сотрудник     0   \n",
       "4        гражданский брак                 1      F    пенсионер     0   \n",
       "5        гражданский брак                 1      M    компаньон     0   \n",
       "6         женат / замужем                 0      F    компаньон     0   \n",
       "7         женат / замужем                 0      M    сотрудник     0   \n",
       "8        гражданский брак                 1      F    сотрудник     0   \n",
       "9         женат / замужем                 0      M    сотрудник     0   \n",
       "10        женат / замужем                 0      M    компаньон     0   \n",
       "11        женат / замужем                 0      F    сотрудник     0   \n",
       "12       гражданский брак                 1      M    пенсионер     0   \n",
       "13        женат / замужем                 0      F    сотрудник     0   \n",
       "14       гражданский брак                 1      F    компаньон     1   \n",
       "15        женат / замужем                 0      F    сотрудник     0   \n",
       "16        женат / замужем                 0      F    сотрудник     0   \n",
       "17       гражданский брак                 1      M    сотрудник     0   \n",
       "18         вдовец / вдова                 2      F    пенсионер     0   \n",
       "19              в разводе                 3      F    сотрудник     0   \n",
       "20        женат / замужем                 0      M    сотрудник     0   \n",
       "21       гражданский брак                 1      F    сотрудник     0   \n",
       "22       гражданский брак                 1      F    сотрудник     0   \n",
       "23       гражданский брак                 1      M    сотрудник     0   \n",
       "24  Не женат / не замужем                 4      F    пенсионер     0   \n",
       "25        женат / замужем                 0      M    пенсионер     0   \n",
       "26        женат / замужем                 0      M  госслужащий     0   \n",
       "27        женат / замужем                 0      M    сотрудник     0   \n",
       "28        женат / замужем                 0      F    сотрудник     0   \n",
       "29  Не женат / не замужем                 4      F    пенсионер     0   \n",
       "\n",
       "     total_income                                 purpose  \n",
       "0   253875.639453                           покупка жилья  \n",
       "1   112080.014102                 приобретение автомобиля  \n",
       "2   145885.952297                           покупка жилья  \n",
       "3   267628.550329              дополнительное образование  \n",
       "4   158616.077870                         сыграть свадьбу  \n",
       "5   255763.565419                           покупка жилья  \n",
       "6   240525.971920                       операции с жильем  \n",
       "7   135823.934197                             образование  \n",
       "8    95856.832424                   на проведение свадьбы  \n",
       "9   144425.938277                 покупка жилья для семьи  \n",
       "10  113943.491460                    покупка недвижимости  \n",
       "11   77069.234271       покупка коммерческой недвижимости  \n",
       "12            NaN                         сыграть свадьбу  \n",
       "13  130458.228857                 приобретение автомобиля  \n",
       "14  165127.911772              покупка жилой недвижимости  \n",
       "15  116820.904450  строительство собственной недвижимости  \n",
       "16  289202.704229                            недвижимость  \n",
       "17   90410.586745              строительство недвижимости  \n",
       "18   56823.777243      на покупку подержанного автомобиля  \n",
       "19  242831.107982            на покупку своего автомобиля  \n",
       "20  209552.644578                            недвижимость  \n",
       "21  131812.203909                 приобретение автомобиля  \n",
       "22  149681.140731      на покупку подержанного автомобиля  \n",
       "23  128265.720871                         сыграть свадьбу  \n",
       "24  290547.235997   операции с коммерческой недвижимостью  \n",
       "25   55112.757732                    покупка недвижимости  \n",
       "26            NaN                             образование  \n",
       "27  308848.983691  строительство собственной недвижимости  \n",
       "28  187863.237306  строительство собственной недвижимости  \n",
       "29            NaN        строительство жилой недвижимости  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- В датасете встречаются пропущенные значения в различных стобцах, что может негативно повлиять на репрезентативность выборки;\n",
    "- В датасете в столбце education используются как заглавные,так и прописные методы записи;\n",
    "- Названия столбцов записаны правильно,в редактуре не нуждаются.\n",
    "- В столбце *days_employed* встречаются отрицательные и непропорционально большие значения.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 2. Предобработка данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Обработка пропусков"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для начала определим количество пропусков в датафрейме:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "children               0\n",
       "days_employed       2174\n",
       "dob_years              0\n",
       "education              0\n",
       "education_id           0\n",
       "family_status          0\n",
       "family_status_id       0\n",
       "gender                 0\n",
       "income_type            0\n",
       "debt                   0\n",
       "total_income        2174\n",
       "purpose                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что количество пропусков в стобцах  *days_employed* и *total_income* совпадают. Рассмотрим датафрейм с пропущенными значениями используя фильтрацию."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
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       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
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       "      <td>29</td>\n",
       "      <td>0</td>\n",
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       "      <td>63</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
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       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>строительство жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>сделка с подержанным автомобилем</td>\n",
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       "    <tr>\n",
       "      <td>55</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>операции с коммерческой недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>52</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>покупка жилья для семьи</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>госслужащий</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>операции с коммерческой недвижимостью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50</td>\n",
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       "      <td>женат / замужем</td>\n",
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       "    <tr>\n",
       "      <td>83</td>\n",
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       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
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       "      <td>35</td>\n",
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       "      <td>94</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>на проведение свадьбы</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44</td>\n",
       "      <td>СРЕДНЕЕ</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>покупка жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47</td>\n",
       "      <td>высшее</td>\n",
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       "      <td>женат / замужем</td>\n",
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       "      <td>120</td>\n",
       "      <td>0</td>\n",
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       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>высшее образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>автомобиль</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>жилье</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>свадьба</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>62</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>строительство недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>55</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>вдовец / вдова</td>\n",
       "      <td>2</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>свой автомобиль</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     children  days_employed  dob_years education  education_id  \\\n",
       "12          0            NaN         65   среднее             1   \n",
       "26          0            NaN         41   среднее             1   \n",
       "29          0            NaN         63   среднее             1   \n",
       "41          0            NaN         50   среднее             1   \n",
       "55          0            NaN         54   среднее             1   \n",
       "65          0            NaN         21   среднее             1   \n",
       "67          0            NaN         52    высшее             0   \n",
       "72          1            NaN         32    высшее             0   \n",
       "82          2            NaN         50    высшее             0   \n",
       "83          0            NaN         52   среднее             1   \n",
       "90          2            NaN         35    высшее             0   \n",
       "94          1            NaN         34    высшее             0   \n",
       "96          0            NaN         44   СРЕДНЕЕ             1   \n",
       "97          0            NaN         47    высшее             0   \n",
       "120         0            NaN         46   среднее             1   \n",
       "121         0            NaN         29    высшее             0   \n",
       "135         0            NaN         27   среднее             1   \n",
       "141         0            NaN         39   среднее             1   \n",
       "145         0            NaN         62   среднее             1   \n",
       "174         0            NaN         55    высшее             0   \n",
       "\n",
       "             family_status  family_status_id gender  income_type  debt  \\\n",
       "12        гражданский брак                 1      M    пенсионер     0   \n",
       "26         женат / замужем                 0      M  госслужащий     0   \n",
       "29   Не женат / не замужем                 4      F    пенсионер     0   \n",
       "41         женат / замужем                 0      F  госслужащий     0   \n",
       "55        гражданский брак                 1      F    пенсионер     1   \n",
       "65   Не женат / не замужем                 4      M    компаньон     0   \n",
       "67         женат / замужем                 0      F    пенсионер     0   \n",
       "72         женат / замужем                 0      M  госслужащий     0   \n",
       "82         женат / замужем                 0      F    сотрудник     0   \n",
       "83         женат / замужем                 0      M    сотрудник     0   \n",
       "90         женат / замужем                 0      F    сотрудник     0   \n",
       "94        гражданский брак                 1      F    компаньон     0   \n",
       "96         женат / замужем                 0      F    сотрудник     0   \n",
       "97         женат / замужем                 0      F    сотрудник     0   \n",
       "120        женат / замужем                 0      F    сотрудник     0   \n",
       "121        женат / замужем                 0      F    компаньон     0   \n",
       "135        женат / замужем                 0      M    компаньон     0   \n",
       "141       гражданский брак                 1      M    сотрудник     0   \n",
       "145        женат / замужем                 0      M    пенсионер     0   \n",
       "174         вдовец / вдова                 2      F    компаньон     0   \n",
       "\n",
       "     total_income                                purpose  \n",
       "12            NaN                        сыграть свадьбу  \n",
       "26            NaN                            образование  \n",
       "29            NaN       строительство жилой недвижимости  \n",
       "41            NaN       сделка с подержанным автомобилем  \n",
       "55            NaN                        сыграть свадьбу  \n",
       "65            NaN  операции с коммерческой недвижимостью  \n",
       "67            NaN                покупка жилья для семьи  \n",
       "72            NaN  операции с коммерческой недвижимостью  \n",
       "82            NaN                                  жилье  \n",
       "83            NaN                                  жилье  \n",
       "90            NaN                      операции с жильем  \n",
       "94            NaN                  на проведение свадьбы  \n",
       "96            NaN             покупка жилой недвижимости  \n",
       "97            NaN                 профильное образование  \n",
       "120           NaN                     высшее образование  \n",
       "121           NaN                             автомобиль  \n",
       "135           NaN                                  жилье  \n",
       "141           NaN                                свадьба  \n",
       "145           NaN             строительство недвижимости  \n",
       "174           NaN                        свой автомобиль  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['days_employed'].isna()].head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Учёт трудового стажа"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В исследовании нет задач, требующих учёта трудового стажа. Но мы не знаем, пригодятся ли они при дальнейших исследованиях, так что оставим их и данные в  столбце 'days_employed' заполним по медиане. Остальные данные по каждому из клиентов являются существенными. Так же в глаза бросаются очень большие либо отрицательные значения трудового стажа, попробуем разобраться,в чём дело. Можно пропробовать посмотреть на графике распределение значений по группам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['сотрудник' 'пенсионер' 'компаньон' 'госслужащий' 'безработный'\n",
      " 'предприниматель' 'студент' 'в декрете']\n"
     ]
    }
   ],
   "source": [
    "#сначала посмотрим уникальные значения для столбца 'income_type'\n",
    "income_type = df['income_type'].unique()\n",
    "print(income_type)\n",
    "\n",
    "#Зададим параметры для графика\n",
    "sns.set(rc={'figure.figsize':(15, 5)})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Таким образом хорошей идеей будет использовать именно столбец с типом занятости для группировки и анализа."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fec64441710>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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C3YWFIMh2qVUKBPm6Isi36XWP1Vo9ikobFswpr9Lhem4pzqYWoLCkCnqh7rpHpUIGb/dbI42ezrcSR3VN4ujjroaXuwoKOdc93lat1eOT70/jalYJXn4oAt3a+kgdEhERWREmhuSwrHG0sLZZKw/gs3kjWNCD7JqTUg5/b5cGa9wCAjyQk1MCANDrBRSVVTUomHO7aM6VzGKcKNFAq6u37lEGeN9OGN1rJY219390V8NJaf//xnR6PT776QySrxZg2v3d0LtzgNQhERGRlWFiSGTFZizdh40LYjlljhyaXC6rGQ1siiAIKK2obrDHY0FJJQpLNMjMK0PS1fxG9yz1cHWqlTg610oc76x9dFbZ7q9LQRDwxY7zOHEhB0+M6oTBPYOlDomIiKyQ7f6mIzLBmh9OSx1Ci01bvJd7HBI1QyaTwcNVBQ9XFcJaeTR5XoVGWytxvDnyWHgricwv0SAlsxilFQ3XPbqoFXW366iTON583c1ZaXUPcQRBwJY9l3DwdBYmDG6He/u1kTokIiKyUkwMySEdP58jdQgGmbpoD5NDIhG4qJVwUSvR+i6VOKuqdSgsvTPqWFhSew1kJTJySlFUWgWh3nVOSnmdSqu3t+uovX2Hp6sKcrnlksfth6/ht2NpuKdvKB4c0t5i/RIRke1hYkgOJz2nVOoQjMLkkMgyVE4KBPq4ItCn6aI5Wp0exWVV9RLHO2sfL6UXoaBEA52+bvqokMvg7a66lTg619qm485/3u5qUdYW7zmZjh/+uIyBPYIw+Z5OVjeaSURE1oWJITmcdzYelToEozE5JLIOSoUcvp7O8PV0bvIcvSCgpLz6VuJYq2BOsQaFpRqkZZciMSUXVdX1iuYA8HRT1dvf8c4+j7dHJNVOTe/3eOTMdXz12wVEhfvjufu6Qs6kkIiImsHEkByKTq9v/iQrx+SQyDbIZTJ4uang5aZC26DG1z0KgoDyWusebyaOlSi8tYVHdmEFzqcWolyjbXCtm7OyJlmsnTym3SjF7yfTEdbKHS9N7MHKxkRE1CJMDMmhPL9kn9QhiGLlNwmY/VgvqcMgIhPJZDK4OTvBzdkJoQHuTZ6nqdKhoFSDguLKm9NXb+/9WHwzmbx2owTFZVV1rkm9UYpZKw82mKrqWy+ZdHd14ogiERExMSSyRQkpefj1yDXcN6Ct1KEQkQWoVQoE+boiyLfpdY8pGUV478sTAIAn7+2Maq3+zr6PpRqcSy1AYUkV9ELddY9KhezOfo+NFMzx8VDDy10FhZwjj0RE9oyJITkMa9/Q3lDf7ktBaIA7Ijv6SR0KEUksK68MK79NhJ+nM/7fU32aXPuo1wsoKqu6OeJYXKtgTunN0cerWSU4eSEXWl29dY8ywMtN1WTBnABvFwQENL1NCBERWT8mhkQ2bMU3CXjv+RgE+zVdep+I7FteUSWWb4mHXC7D3MlRdy2II5fLapK59k3scy8IAsoqtcgvrqyz9vH26GNmXhnOXM1HZZWu5hoZgJVzRsDdiaOKRES2iokhOQR7Gy2s7a31cVg1eyjcXZykDoWILKy4rArLtsSjQqPDgim90eouU01bSiaTwd3FCe4uTghr1XAUUKfX48T5HPx48Aqy8srh6eqEe/u1QViQJ/LzbHM7ICIiYmJIZBdmrTyAz+aNYPVBIgdSXqnFh1vjUVBciTmToxpN4sSkqdLhQGImfjuWhtyiSrTydcVfxnbBoIggOCkVUMhZwIaIyJYxMSS7Z8+jhbXNWLoPGxfEchNrIgegqdZh1bcJyMgpw+xHI9Ep1NtsfRWXVeH3E+nYczIdZZVahId4YfKoTojq5M9qpkREdoSJIZEdmbZ4L/c4JLJzWp0ea7cl4WJ6EV54sAciOpinANWNgnLsOpqGP09noVqrR+9O/hgbE2bWJJSIiKTDxJDs2m9HU6UOweKmLtrD5JDITun1Ajb+chaJKXn4y9gu6N+tleh9pGQWYWdcKk6ez4FCIcOgiCCM6R/GIldERHaOiSHZtc17LkkdgiSYHBLZH0EQ8NXuC4hLvoHHRnTE8KgQ0drWCwISU/KwMy4VF9IK4apW4r6BbXFP31B4uatF64eIiKwXE0OyW4WlGqlDkBSTQyL78v0fl7H3VAbGDQjDuAFtRWmzWqvHkTPXsfNoKrLyyuHrqcbkUZ0wNDIYLmp+RCAiciT8qU926/VP/jRLu8tnD8OclX+YpW2xTV+8FxsWxEodBhGZaGdcKn45fA3Do1rj0eEdTW6vvLIa++Izsft4GopKqxAa4I7nH+iOfl0DWd2YiMhBMTEkuyQIgtna7hzmY7a2xaYXBKz6NhGzHo2UOhQiMtIfCZnYuvcS+nUNxNOju5hUeTi/uBK7j6dhf3wmKqt06N7OB9Pu74Ye7XxZ0ZiIyMExMSS7NG3xXrO0e3tq5mfzRmDG0n1m6UNs8Zdy8euRa7hPpKlnRGQ5x89l44ud5xDRwRfPP9AdciP3CkzPLsXOo6mIS74BQQD6dQvE2P5haBtk3r0PiYjIdjAxJDKCrU21+nZfCkID3BDZ0V/qUIiohc5cycenP51BxxAvvPxQT4N/7giCgHOphdgZl4rTl/OgcpIjtncIRvdrA39vFzNFTUREtoqJIdkdc21oX7+Qy6Y3RpqtL3NY8U0i/jU9Bq39WXKeyNpdyijCx98norW/G159NBJqJ0WLr9Xp9ThxPgc74lJx7XoJPF2d8NDQ9ojtEwp3FyczRk1ERLaMiSGRA/nbhjismj2UHw6JrFhadilWbE2At7sar0+Kgqtzy/69aqp1OJiYhV1HU5FbVIlWPi54ZmwXDI4IgpOy5YklERE5JiaGZFcsNVpY+3VbGjUEgFkrD+DTuSPgpLSt6bBEjiC7oBwfbomHWqXA3ElR8HJTNXtNcXkV9pxIx56TGSitqEbH1p6YNLITenfyN3pNIhEROR4mhkTNmN1MRc9RfULx+8l0C0UjjheW7cOGBbGQswohkdUoKNFg2eZ46PQC5j3Ru9l1gDcKyvHb0TQcPJ2Faq0eUeH+GBsThk6hXqwwSkREBmNiSHZjzmrz7FvYK/zuBVueHN3Z5hJD4OYeh02NhBKRZZVWVGP5lniUVFRj/hO977oW+HJmMXbGXcOJCzlQyGUY2CMIY/qHcf0wERGZhIkh2Y2CEo3obX46d0SLznt3Wn+8vfGo6P2b29RFe5gcEkmsQqPFR1sTkF1Qgdce74X2wZ4NztELAk6n5GFnXCrOpxXCRa3EuJi2uCc6FN7uagmiJiIie8PEkOxCwqVcs7Tb0nV4IQHuZunfEpgcEkmnWqvDJ9+fxrXrJXj54Qh0a+tT57hWp8eRMzew82gqMnPL4OOhxqSR4RjWqzVc1PwVTkRE4mnRp96ZM2diwoQJmDhxIqZMmYKzZ88CAK5cuYJJkyZhzJgxmDRpEq5evVpzjaWPkWNb+W2i6G1uXBBr0PmfzRshegyWYmsFdIjsgU6vx7ofz+DstQJMvb8rencKqDlWXqnFjrhrmL/2EDb9ehZyGfD8+O5Y/OJAjOkfxqSQiIhE16LEcPHixfjpp5+wbds2TJ06FW+++SYAYOHChZgyZQp27dqFKVOm4J133qm5xtLHyHFVaLRmadfQ4g22tul9fdMX75U6BCKHoRcEfL7jHE5dzMWUezphUEQwACC/uBJb91zC3DV/4pu9KQj2c8Prj/fCP6b2x8CIIJv/OUNERNarRb9hPDw8av5cWloKmUyGvLw8JCcnY/z48QCA8ePHIzk5Gfn5+RY/Ro7t5Y/+EL1NY6dW2vKUTL0gYJUZRl6JqC5BELDl90v48/R1TBzSHvdEt0F6Tik2bk/GgnWHsetYKiI7+mHhs/0w74neiOjgxyqjRERkdi2ei/LWW2/hzz//hCAI2LBhA7KystCqVSsoFDc3zVUoFAgMDERWVhYEQbDoMV9fX1HfFLIdgiBIHYJdib+Ui18OX8X9A9tJHQqR3fr50FXsPp6Ge6JD0bmNN1Z8k4DElDyonOQY0TsEo/u1QUAzW1UQERGJrcWJ4XvvvQcA2LZtG5YsWYLZs2ebLShz8vOzziIhAQEezZ9EDTww50fR2/x5+YPNnnO3+/Xz8gfNEpelfLf/MrqHB6B/9yCpQxEV/43ZHnu8Z9sPXsa2A1egVilw9UYp/nc8HV7uKjw5tivuG9Qeni3Y0N6a2eM9s2e8X7aH98z22NI9M3j1+sSJE/HOO+8gKCgIN27cgE6ng0KhgE6nQ3Z2NoKDgyEIgkWPGSIvrxR6vXWNMgUEeCAnp0TqMAjAiw/2aPZetOR+jYsJw464VDFDs6h3N8bh3Wn9bbraam38N2Z77PGe7Y/PwBc7zwMANFU6FJdq8MyYLhgUEQSVkwKacg1yysXfdsdS7PGe2TPeL9vDe2Z7rPGeyeWyJgfKml1jWFZWhqysrJqv9+zZAy8vL/j5+aFbt27Yvn07AGD79u3o1q0bfH19LX6MHJM5Kmn279ZKlHYeiw0XpR0pvb3xKErKq6QOg8jmlZRX4R+fH6tJCtsEuuPlhyLw/vMDMKJ3CFROCokjJCIiAmRCM4u0cnNzMXPmTFRUVEAul8PLywsLFixAjx49kJKSgjfeeAPFxcXw9PTE4sWL0aFDBwCw+LGW4oih/RA7Mfz41aFwc3Zq9ryW3q+svDK8tT5OjNAk9enc4XBS2vYHV/4bsz32cM+yC8qx61ga9p7MqHnt1cd6oWcHX7ssJmMP98yR8H7ZHt4z22ON9+xuI4bNJob2homhfTDHaGFLK4oacr/sZX/ADQtiIbfhD7L8N2Z7bPmeXckqxo64VJw4n43av2FXzR4Kd5fmHz7ZKlu+Z46I98v28J7ZHmu8ZyZNJSVyBBsM3My+pWx50/vapi/eywqwRHchCAISU3Kx5P9O4t0vjuPMlXxEhfsDAPy9nLH85cF2nRQSEZHtM7j4DJHU1v2YJHqb5hoNs6fNqKct3mvT+zQSmYNWp0dc8g3sPJqKjJwy+Hio8XhsOCI6+OKjrQnwdFNhzuQo+HiopQ6ViIjoruznUys5jKNns0Vtz9zJjj0lU/YyNZbIVBUaLXbGpWLBusPY+MtZAMD08d2w+MWBGBgRhNXfn4amSoc5k6LQysdV4miJiIiaxxFDsinXrlvXPG1HNHXRHrtKdokMUVCiwf+Op2FffAYqNDp0DfPGs+O6IqL9zYIy5ZXV+GhLPApKNZg7qTfaBNrHli9ERGT/mBiSTfnH58dEbc9SCc6mN0ba1Wgbk0NyNBk5pdh5NBVHztyAXhAQ3SUQY2PC0D7Ys+YcTbUOK79NREZuGWY/GonwUC8JIyYiIjIME0OyGVqdXtT2Hhlu2FYnpho/qB22H7pq0T7N6cMt8Xh9UpTUYRCZjSAIuJBWiB1xqUhMyYNKKceIqBDc278NAr1d6pyr1emx5ockXMoowosPRiCig59EURMRERmHiSHZjBlL94na3v0D24naXnMeHtbBrhLDpCv5+OngFUwY0l7qUIhEpdcLOHkhBzviUnElqxjuLk6YOKQ9YvuEwMNV1ej5G7Yn4/TlPDw7riv6dQ2UIGoiIiLTMDEkh/Te8zGS9PvutP54e+NRSfo2h20HryA00B19OgdIHQqRyaqqdfjzdBZ2HU1DdmEFAr1d8PTozhjUMxhqJ0Wj1wiCgP/+dh5Hz2bjsdiOGNartYWjJiIiEgcTQ7IJYq/PC/ZzE7W9lgoJsL9CFJ98fxr/mNqfRTbIZpVWVGPPiXT8fjIdJeXVaB/siZkjOqJP5wDI5Xffyub7Py5jX3wm7h/YFuNi2looYiIiIvExMSSHs3bOcEn7/3TuCLywbJ+kMYht4aaj+OivQ+Dl1nCaHZG1yimswG9H03AgMRNVWj0iO/phXEwYOrfxhqwFe5vuiLuGXw5fw4io1nh4mGXXLBMREYmNiSFZPbFHC5uaEmYpTkr73D70tY8PYt2c4VBJ/P4SNedKVjF2xqXi+PlsyGUyDOjRCmP7hxk0or8/PgPf7E1B/26BeGp0lxYlkkRERNaMiSE5lA0LYqUOAQCwcUEspi3eK3UYontx+X5smB/b7PQ7IksTBAFJV/Kx48g1nEsthItagbH9w3BPdBv4eKgNauvYuWz8Z+d59BqaeZkAACAASURBVOzgh+nju/PvOxER2QUmhmTVxB4tlFvJU317Hl2YvmQvNi6ItevvkWyHVqfH0bM3sDMuFek5ZfDxUOPx2HAMj2oNF7XhvwKTLufhs5/OIDzUCzMfioBSYZ8zAIiIyPEwMSSHYW0bstvbpve1TVu81+reb3IsFRot9sdnYvfxNBSUaBDi74Zp93dDTPdWRidzl9KL8MkPpxHi74bZj0ZKPi2diIhITHzUSVbrZxH3/IsK9xetLTE9MKidaG1JXVSnPntNesm6FZRo8M2+S5i75hC27r2EVj4uePWxSPxzWn8M7hlsdFKYll2KFd8kwMddjdcmRcHV2UnkyImIiKTFEUOyWj/8cVm0tmY9GilaW2J6aFgH0RLgl5bvx4YFsXhh6T7o9IIobZpq6qI9HDkki8jMLcPOo6k4nHQdekFA3y6BGBcThvbBnia3faOgHMu3xEOtUmDO5ChW3yUiIrvEEUOySnlFlaK1NdtKk8Lb/jm1v2htyQB8Nm8EQvyl2aexMRw5JHMRBAEX0gqx8psE/G1DHI4m38DwqNb4YMYAzJwYIUpSWFCiwfLN8dDrBcydHAV/LxcRIiciIrI+TAzJKs1be0i0tnpZ6TTS20JF3Bh+2uK9kMlkeHd6jFVNn1303xNSh0B2RK8XcOJ8Nt778gQWfXUSKZnFeHBIeyydOQhPje6CQB9XUfoprajG8i3xKK2oxuuTeiHYz3oeuBAREYmNiSFZHb2I0yCXvzxYtLbMaZ0Z1gfOejQSI/uEiN6uMS6kF+G7/SlSh0E2rqpah72nMvDm+iNY/UMSSsqr8NTozlg6cxAeHNIeHq7iTfGs0Gjx0dZ4ZBdUYPajkWgXZProIxERkTXjGkOyOtOXiLe/n6H7k0lFzE3ha6/re2p0F/h4qPHdfvHWaxrrl8PXENbKA/26BkodCtmY0opq7DmZjt9PpKOkvBrtgz3w0sQI9O0cYJY9BKu1Onz8XSKuXS/FKw/3RJcwH9H7ICIisjZMDMlumWMUzpzMten9/QPbwdtdjY2/nBW9bUOt3ZaEgGejOfpCLZJTWIHfjqXhQGImqqr1iOzoh7H9w9AlzNts+2Tq9Hqs+/EMzqUW4vnx3RHVyXqmZBMREZkTE0OyKmIWKhFzFM4SxPygu3DTUfyjVlGbwT2D4eHqhBXfJIrWh7H++flxLH95sM2M5pLlXbtegs93nseBhAzIZTIM6N4KY2LCEBog3nrcxugFAZ//eg6nLubiyXs7Y2BEkFn7IyIisiZcY0h2acP8WKlDMIpYWzukZZc2eC2yoz/eeqavKO2bas7qP1FZpZU6DLIigiAg6XIeln59Cv/4/BiOnb2BMf3CsPjFgZg2vrvZk0JBELD594v4M+k6Jg5tj1F9Q83aHxERkbVhYkhWQ8zRQnOsO7KU+wa0FaWdI2euN3itY2svvPd8jCjtm2rmh3+IWmiIbJNWp8fhpOtYuOkYPtyagKy8MjwW2xH/fns0Hh8ZDl9PZ4vE8fOfV/G/4+kY3a8NHhjUziJ9EhERWRMmhmR3Ni6wzdHC2x4d0VGUdj77ObnR14P93KymWuv0JXshCEwOHVGFRotdR1PxxqeHsX57MvSCgKn3dcOSlwZhXExbuLk4WSyW3cfTsO3gFQzuGYTHR4abbf0iERGRNWNiSFbhxWX7RGnnwSHt7eJD3cJn+4nSTkGJptHXfTzU+PjVoaL0YSpzFNwh61VYqsG3+1Iwd80hbNlzCQFeLpj9aCT+Oa0/hkQGQ6mw7K+lQ0lZ+Pp/F9GncwCeHdcVcjv4+UFERGQMJoZkFaq0elHaeXBIe1HakVrbIA9R2pmz+s8mj7k5O2GtlVRuFXMaMVmnrLwy/PvXs5i/9hB2xF1Dj3Y++Nsz0VjwZB/0CveXJCE7dTEHm345h25tffDChO5QyPkrkYiIHBd/C5Lk4pJviNLOW09bR2EVsax5fZgo7dxtHZ/aSYHP5o0QpR9TMTm0P4Ig4EJaIVZ9m4i31sfhSPINDI1sjfdnDMDMh3qiQ2vpti05e60Aa7edQdsgD7zycE84KW2rijEREZHYmBiS5D796Ywo7XQM8RKlHWvhrBJnN5npS+4+VVOpkGPD/Fh4uqlE6c8UTA7tg14v4MT5HLz/5Qks+uokLmUUYcLgdlg6cxCeHtMFrXxcJY3vSlYxVn2XiFY+Lnjt8V5wUXPnJiIiIiaGJKnSimpR2vnoFesopiI2sQrpNFfgRS6X4aNXBks6gnPbOxuPSh0CGalaq8O++Ay8tf4IVv9wGkVlVXjy3s5YOnMQJg7tAE9X6R8+ZOSW4aOtCfBwccLrk6LgbsEiN0RERNaMiSFJatbKA6K04+Vun5uli1VIpyUFXmQyGf72TDT6dwsUpU9jpeeUYvPvFyWNgQxTWlGNn/+8gnlrDuE/O8/DWa3Eiw/2wAcvDMCovqFQO1nHNM3cwgp8uCUeCrkMcydHwcfDPn9uEBERGYPzZ0gyYm1TsPZ16yigYi6b3hhp0SmWLz4YAV/PS9gZl2qxPuv77Vgawlq5Y1BEsGQxUPNyCyvw27E0HEjMgqZah54d/DA2Jgxdw7ytrjpwUVkVlm2Jh6ZKhzee7INAiaezEhERWRsmhiQZsbYpUKusYzTCnEb3a4PfjqWZ1MbURXuw6Y2RLTr38dhweLurJR2527D9LAJ9XBFuZ2tH7cG16yXYeTQVx85mQyYDYrq3wtj+YQgNdJc6tEaVV1bjwy3xKCzVYO7k3lYbJxERkZSYGJJNWz9/hNQhWMTkUZ1MTgwNNbpfG3i6OeGzn5It2m9t7395AkteHAh/bxfJYqCbBEHAmav52BmXiuSrBXBWKXBvv1DcG90Gvp7OUofXJE2VDiu+SURmbhlefawXHzQQERE1gWsMSRJiTI0MC3R3qH3H/vZMtMltrP7+tEHnD+gehDmTo0zu1xTz1x1GeaVW0hgcmVanx+Ez1/H3fx/Dh1sSkJFbhkdHdMSymYMwaWQnq04KtTo9Vm87jZTMIrwwoQd6tPeVOiQiIiKrxRFDslkLn+sndQgWJUbF0BMXcgy+pkc7X7zzbDT++flxk/s31isr/sBn80ZAqXCcBwFSq6zS4o+ELOw+loq8Yg2C/Vzx3H1dMaB7EJyU1n8f9HoB639ORtLlfDw7riuiu0pbVImIiMjaWf9vd7I7YowWPjOmi9UVt7CE1a+Zvun9mSv5Bl/TLsgTH7wwwOS+TTFj6T7RChZR04pKNfhufwrmrj6Ezb9fhJ+XC2Y9Gol3p8dgaGRrm0gKBUHAl7+dx7Fz2Xg8NhzDerWWOiQiIiKrZ/2/4YkaMaJ3iNQhSEKMjbiXb4k36rpWPq74UOL9IsUqWEQNZeWV4fMdZzFv7SH8evgaurXzwVvP9MUbT/ZBVLg/5Db0IObb/SnYH5+J+we2xdiYMKnDISIisglMDMmiFn910uQ2Fj7rWFNI69sgwqb3pRXVRl3n7a7Gx68ONbl/U1hy6w5HcDG9EB9/l4i/rY/DoaQbGBLZGu/PGICXH+qJjq1tr1DLr0euYceRVMT2DsHDwzpIHQ4REZHNYGJIFnU+rdDkNtoGeYgQie0SY+Rm1soDRl/r5uwk+d6RTA5NoxcEnLyQg/e/PIEP/nsSF9IKMX5QOyybOQjPjOmCVr62ucffvvgMfLsvBTHdW+HJ0Z0dcro5ERGRsZgYksVcECEpXP6ytFMZrUVL9yO8G70J6/XUKgU+mzdC0umFTA4NV63VYX98Bv62Pg6ffH8ahaUaTLmnE5bNHIyHhnWAp5tK6hCNdvTsDXy58zwiO/ph2v3dbGrqKxERkTVgYkgWs0iEaaQ+HmoRIrEPw6NMK6gx3cT1ekqFHJ/NG4FACfcYfPXjg5L1bUvKKqux/dBVzFt7GF/sPA+1kwIvPtgDH7wwAPdEt4FapZA6RJOcvpyH9T8no1OoF16aGMHqtUREREbgdhVkEZpqncltfPKq6RU57clfxnbF/vhMSWOQy2X44IUBWLY5HmevFVi8/+KyKmzcnoxp47tbvG9bkFtUgd3H0vFHQiY01TpEtPfFuJgwdG3rYzfTLC+mF2L196cR4u+GWY/2gtrJtpNcIiIiqfCxKlnES8v3m9yGqzOfY9T3xpN9TLpejOmYMpkM857ojSE9g01uyxh/Jl3H3pPpkvRtrVJvlOCzn87gjXVHsOdkOvp0DsA/pvbH65Oi0K2dr90khak3SrDim0T4eDrj9UlR/BlBRERkAv4WJbMTY++5T+eOMD0QO9S5jbfUIdSYen83+Hio8fOhqxbv+8vfLiDIzw3d2vpYvG9rIQgCkq8WYGfcNZy5WgC1SoF7okMxul8b+Ho6Sx2e6G7kl+PDLfFwVikwd1KUTa+PJCIisgYcMSSzM3XvuYgOvjaxqbZUVs02bfsIMYu4PDSsA54e3Vm09gyx9OtTuJFfLknfUtLp9Thy5jr+8e9jWL4lHuk5ZXhkeAcsnzkIk0d1ssukML+4Ess2x0MvAHMnR8HPy/6+RyIiIkvjiCFZvVcf6yV1CFbN3cVJ6hDqiO0TCg9XFdZsS7J43//vsyNYNXuo1b0n5lBZpcWBhCz8diwNecWVCPZzxXPjumJAjyC7fpBSUl6F5VviUVZZjQVT+iDYz03qkIiIiOyC/X56IKtg6mgUy863zIb5pm16v/n3iyJFclN010DMe6K3qG221KyVB6DV6SXp2xKKyqrw/R8pmLfmEL7+/SJ8PdWY9Ugk3p0eg6G9Wtt1Ulih0eKjrQnILarE7EcjHX5PUyIiIjHZ7ycIsguDJSpoYmvkctOS59+OpYkUyR3d2vpg4bP9RG+3JWYs3WfSPo3WKCuvDJ/vOId5aw7hl0PX0CXMB28+3Rf/76m+iOrkb/cPUKq1Onz8XSLSskvx0sQIdAlz3PWkRERE5sDEkMzG1NFCqZIKW2XqpvdXsopFiuSOtkEe+OCFAaK32xLTF+8VpfCR1C5lFOHj7xLxt/VxOJR0HUN6BuG9GQPwysM9ER7iJXV4FqHV6bF22xmcTy3EtPu7ISrcX+qQiIiI7E6ziWFBQQGef/55jBkzBg888ABeeeUV5OfnAwDi4+MxYcIEjBkzBlOnTkVeXl7NdZY+RvaH08QM16dzgNHXvvvFcREjuaOVjyuWvzzYLG03x9TCR1LRCwJOXczB+/89gfe/PIELaYW4f1A7LJ05CM+M7YogX1epQ7QYvSDg37+eQ/ylXDw5ujMG9AiSOiQiIiK71GxiKJPJMH36dOzatQs///wz2rRpg2XLlkGv12PevHl45513sGvXLkRHR2PZsmUAYPFjZH3++9t5k65f8tJAkSJxLK883NOk6yurtCJFUpePh9rk6qnGErPqqrlVa3X4IyETb2+Iw8ffnUZBsQZP3NMJS2cOwsPDOsDLwbZkEAQBX//vIg6fuY6HhnXAyD6hUodERERkt5pNDL29vRETE1PzdVRUFDIzM5GUlAS1Wo3o6GgAwOTJk7Fz504AsPgxsj57TmaYdL2/l4tIkTieVx+LNPramR/+IWIkdbm7OGHN68PM1v7dPDDnR0n6bamyymr8cvgq5q89jM93nIOTUo4ZE7pj0YsDcG90GzirHLOA9I8Hr+D3E+kY3a8Nxg9sK3U4REREds2gTxt6vR5ff/01Ro4ciaysLLRu3brmmK+vL/R6PQoLCy1+zNvbejb5JiAzt8yk66UaWbIXkR1NW38lCAJkZipk4qxS4tO5I/DaxwdRrjHP6GRTpi7aY/I6TLHlFVVi9/E07E/IhKZKhx7tffF8TBi6tfUx2z2wFT/9kYKf/ryKIT2DMWlkuMO/H0REROZmUGL47rvvwtXVFU899RR2795trpjMys/PXeoQGhUQYD/r6Uydutc+zFekSMzH2u/Xf/4+Bs/8fZdR105bvBc/L39Q5Ijq+r93x+HVj/bjqhkK3tzNJ9uS8I/npZ+mfCWzCN/vu4QDpzIgABgWFYKHY8PRvrVjFJNpzu/HUrH+xyQM7BmMuU9HQ6FgnTRbYe0/G6ku3i/bw3tme2zpnrU4MVy8eDGuXbuGdevWQS6XIzg4GJmZmTXH8/PzIZfL4e3tbfFjhsjLK4Veb12VCgMCPJCTUyJ1GKIwdf+4dXOGW/17YU/3qymW+P7efqYvVn2biIQUyxWROnkuG5t+PI0HBrWzWJ+3CYKAs9cKsCMuFWeu5EPtpMCovqG4N7oN/LycAVjmfbd2Jy/kYM0PSYjqFIBnx3RBfr5pMxDIchzhZ6M94f2yPbxntsca75lcLmtyoKxFj2E//PBDJCUlYfXq1VCpbhY/iIiIQGVlJY4fv1nJcPPmzRg7dqwkx8h6zFi6z+hre7TzgcpJIV4wDm79/BFGX2uJgi0ymQyzHo1EbO8Qs/dV2w9/XMapizkW60+n1yMu+Qb+8fkxLNscj7TsUjwyvAOWvTwIk0d1qkkKCTh7NR/rfkxCu2APvPlcfzgpOVJIRERkKc2OGF68eBGffvop2rVrh8mTJwMAQkNDsXr1aixZsgQLFy6ERqNBSEgIli5dCgCQy+UWPUb24bXHo6QOwa4o5Nb/oVomk+HpMV3g7a7CDweuWKzfj787jX9O7Y/QQPNNLddU6fBHYiZ2H0tDblElgnxd8ey4rhjYoxWclHwAUt/lzGKs+v40Wvm64tXHesFFrUSp1EERERE5EJlgDztAG4BTSc3HlFGmZ8Z2wYgoy44cGcvW7pcp98WSxVr2x2fgi52mbXNiqI/+OkT0LSCKy6rwvxPp2HsyHWWVWoSHemFc/zD06uQPOQuoNCojtwyL/nsCrs5KvPFkX/h4qG3u3xnZ3s9GR8f7ZXt4z2yPNd6zu00ldcwa6GR1hvdq3fxJZJS2QR64dt26fig1ZnhUCDxcVfjk+9MW6/O1jw9i3ZzhokxhvpFfjl1HU3Hw9HXodHpEdfLHuJi2CA9lQZm7ySmswPLNp6BUyjFncm/4eKilDomIiMghWf9cM7IJpoxKvflUX5aiN6OFz/Yz+todcddEjKR5fToHYMGU3hbt88Xl+02aRZCSUYTV35/Gm58dwcHT1zG4ZxD+9XwM/vpIJJPCZhSVarB8czyqtXrMmRSFQG/uX0pERCQVJoYkOX54Nr+p93Uz6rpv9qaIHEnzuoT54O/PGZ/MGmP6kr0wZFa9XhAQfzEXH/z3BN778gTOXivA/YPaYunMQfjL2K4I9nMzY7T2oayyGsu3JKCorAqvPtYLoQHWuZUQERGRo2BiSCYzZbTwgxcGiBgJNWVIZLDR12blWX67gLBWHvhghmX/bkxbvLfZc6q1ehxIyMTbG+Kw6rtE5BdX4olRnbDs5UF4eFhH0dcr2itNlQ4rvknA9fwyvPJIT3QM4cMhIiIiqTExJEm18nGVOgSHYWwS/tb6OJEjaZlWvq5Y/vJgi/bZ1EOO8spq/HL4KuavO4R/7zgHJ4UcMx7ojg9eGIh7+7WBs4rLtVuqWqvHJz+cxuXMYrwwoQd6tPOVOiQiIiICi8+QiX47lmb0tSv+OkTESKg5piTh1VqdJFss+HiosXLWEMxeddBifU5dtKemGmt+cSV+O5aG/QmZ0FTp0KOdD6bf3x3d2/lwXawR9HoB67cn48yVfDw3riv6dgmUOiQiIiK6hSOGZJLNv180+lpPTruzuLVzhht13QvL9oscSct5uKqw+rVhFu1z6qI9WP9zMhasO4z/HU9H73B/LHy2H+ZM7o0e7X2ZFBpBEAT8Z9c5HD+XjUkjwzGUlYiJiIisCkcMyWgFJRqjr137unEJCplGbcK2DIIgSJYQuaiV+HTucLzx6RGT/t4Z4vCZ67gnOhSj+7WBvxerZZrq230p+CMhC+MHtcWY/mFSh0NERET1cMSQjDZn9Z9GXRca4A61yvLTEummjQtijbquJcVZzMlJqcCSlwaiQ2tPi/Wp0wtMCkXwy+Gr2BGXitg+IXhoaAepwyEiIqJGMDEko+gNKO1f38LnokWMhAwlk8mgVNjmVEiFXI43n+6Lvl0CLNLf3pMZOJCYaZG+7NW+Uxn4bv9lDOjeCk/e25nTcImIiKwUE0MyynQjR48mDG4HhZx/7aT22TzjRg1N2ZpELHKZDDMnRuCevqEW6e/fv57DpYwii/Rlb+KSb+DLXecR2dEPU+/vBjmTQiIiIqvFT+hkUQ8OaS91CHTLhMHtpA7BaDKZDFPu7YxHhltmWuL7X55AfnGlRfqyF4kpudiwPRmd2nhj5sQIKBX8dUNERGTN+JuaDGbsqNErD/fkNDIrMtHItV7z1x4SORLj3T+wHZ4b19Uifc1dcwgVGq1F+rJ1F9IKseaHJIQGuGPWI5FQmVD0iIiIiCyDiSFZTJ/OllkXRi331tN9Db4mt8i6Rs6G9mqNvz7S0yJ9vfzRH9Dp9Rbpy1al3ijBym8T4evpjNcm9YKrM4tfExER2QImhmQQY0cL//5cP5EjITF0DPEy6rqDiVkiR2Ka3p0CsGBKb4v09fySfRBMKL5kz67nl2P5lni4qBWYMykKnq7cq5SIiMhWMDEkiwhr5SF1CNSEFX8dYvA1m349a4ZIjFdeqcXlzGKL9Sf11h3WKL+4Ess3nwIAzJkUBT8vZ4kjIiIiIkMwMaQWe2HZPqOuW/7yYHEDIVF5uhk3qpNnBVNK84srsWXPRcxd8ye+2ZeC7u188OS9nS3StzVUaLUWxeVVWL4lHuUaLV5/PArBfm5Sh0REREQGYmJILVatNW5tlY+HWuRISGyfzRth8DXzJCxCk55dig3bk7Fg3WHsPpaOXuH+WPhsP8yd3Buj+oZi2cxBFomDySFQodHio60JyC2qxKxHItE2iLMDiIiIbBGrAlCLHD+XbdR1n7w6TORIyByM3UpAq9NbbBsCQRBwLrUQO+NScfpyHlROcsT2DsHofm3g7+1S51xfT2esmDUEr646aPa4pi7ag01vjDR7P9aoqlqHVd8mIj27FK883BNdwnykDomIiIiMxBFDapE125KMuo4VCW3HhgWGb3o/Y+k+8QOpR6fX4+jZG3j3i+NY+vUpXLtejIeGtseymYMx5d7ODZLC2zxdVVj92jCoLbBVgiOOHGp1eqz78QwupBVi2vhu6BXuL3VIREREZAJ+aqdmlVdWG3Xdp3NHiBsImZVcJkOPdj44c7XAoOsEQTDL/pSaah0OJmZh19FU5BZVopWPC54Z2wWDegS1eF88F7USK2cNwT+/OI7M3DLRY6xtxTcJePWxXmbtw1roBQH//vUs4i/l4unRnTGge5DUIREREZGJOGJIzXplxQGDr+nTOQBOSv71sjVzJhu+5YPYFTqLy6uw7cBlzFtzCF/tvgAvNxVefqgn3nt+AEZEhRi8WbrKSYHV80eiSxtvUeOsLzElD78cvmrWPqyBIAj4evdFHD5zAw8P64DYPqFSh0REREQi4Igh3ZWx+7XNfChC5EjIUp4d1xWf7zhn8X5vFJTjt6NpOHg6C9VaPaLC/TE2JgydQr1MHpFUKuSYN6U31v+cjLjkGyJF3NB3+y8jNMDdrqdV/njwCn4/mY4x/dvg/oFtpQ6HiIiIRMIhHborY0aDHo8Nh9wMUwvJMob1am3wNaassbucWYw1P5zGm58dwYHETAzo3gr/mh6DWY9GonMbb9GmqcplMsx4oDvG9G8jSntNWfltIrLyzDttVSq/HUvDT39exdDIYDweG26WKcREREQkDY4YkujGxoRJHQKZaOGz/fCPz4+ZrX29IOB0Sh52xqXifFohXNRKjItpi3uiQ+Htbr7tTWQyGSaN7ARPNxW+2Ztitn7eWh+HVbOHwt3FyWx9WNrBxCxs/v0i+nYJwF/GdmVSSEREZGeYGFKTjBkFcpTiG/bOmL3oFv33BN54qu9dz9Hq9Dhy5gZ2HU1FRm4ZfDzUmDQyHMN6tYaL2nI/jsbFtIWHiwqbfj1rtj5mrTyAT+eOsIu1tifO5+DfO86iRzsfzHigB+RyJoVERET2hokhiSqyo5/UIZBIPvrrELz2ccv3AbyQXtTksfJKLfYnZOB/x9NRUKJBaIAbnh/fHf26BVpsH8T6hkQGw81ZiY+/P222Pl5Ytg8bF8Ta9Oha8tV8fPpTEjoEe+Llh3vaRaJLREREDTExpEYZM1r4r+kxZoiEpOLlpjL4mlMXctC7c0DN1wUlGuw+noZ9pzJQWaVDt7Y+eG5cV/Ro72sVyVLvzgGY/0RvLPn6lNn6mLZ4Lza9MdJs7ZtTSmYRPv7uNFr5umL2Y73grOKvDCIiInvFR78kmtb+blKHQCJbO2e4QeffHn1LzynFxu3JmL/2EHYdTUVkRz8sfLYf5j3RGxEd/KwiKbyta1sfvP2XaLP2YUpxHqmk55RixdYEeLo5Yc6kKLtaL0lEREQN8fEvNfCv/xw3+JpVs4eaIRKSmtrAPQMB4N0vjuFKVglUTnKM6B2C0f3aIMDbxQzRiad9sCf+NT0Gf9sQZ7Y+pi7aYzMjhzmFFVi+JR5KpRxzJ/c2a0EgIiIisg4cMaQGLmcWG3wNRxPs14b5sQadfyWrBBOHtseymYPx5L2drT4pvK21vxuWvDTQrH3YwshhYakGyzafglarx9xJUTZz/4iIiMg0TAypjgtphQZfs87A6YZkW+RyGfp2CWj+xFrGD2xnkw8L/L1csOKvQ8zahzUnh6UV1Vi+JR7FZdV49fFeCAlwlzokIiIishAmhlTHoq9OGnS+m7MSKiOmG5JtmTkxwqDzpy/Za6ZIzM/TTYVPXh0GD1fzJbbz1x4yW9vG0lTpsPKbBNzIL8dfH+mJjq29pA6JiIiILIiJIdWoqtYZfM3KWVxb6AhkMhlG92sjdRgW4+qsxJKXBiE0wDwFlXKLKvHFznNmadsY1Vo9Pvk+EZezivHChAh0b+crdUhERERkYUwMqcaLy/cbdH7/boHc6NoBXMkqxpptSdh9PM2g66x5ymRLmj6LEQAAIABJREFUqJ0UeOfZfujRzscs7e+Pz8QfCZlmadsQer2Az34+gzNXC/DcuG4GTxsmIiIi+8CqpAQAEATB4GtemNDDDJGQNRAEAacv52Nn3DWcSy2Ei1qJsTFhCPV3x/rtyVKHZzFKhRyvTYrCxu1ncfjMddHb/3zHObT2c0N4qDTTNgVBwBc7z+HE+RxMHtUJQyKDJYmDiIiIpMcRQwJwcxNuQzwxqpNV7UVH4tDq9PjzdBbe2XQUK75JwI2CCjweG45lMwfhsRHhGBgRZFB7tj5qCABymQzTx3fD2Jgws7T//n9PIL+40ixt340gCNi69xIOJGbhgUHtHGqqMBERETXEEUMyyr38EGlXKjRa7I/PxO7jaSgo0SAkwA3T7u+GmO6toFTUfX707vQYvG3G/f6skUwmw+Ox4fB0VWHr3kuitz93zSGsfX041CrLFXL69cg17DqahlF9QjFxaHuL9UtERETWiYkhGTyqM2dylJkiIUsrKNHgf8fTsC8+AxUaHbqGeePZcV0R0d63yRHhEH/DCrKs+zEJLz5oWFVTazU2JgxuLkr8+1fxC8e89OF+bFgQC7kFRuL3nkzHd/svY0CPVnjiXo7+ExEREaeSkhF6sGKhzcvILcOmX85i/tpD2Hk0FRHt/fD2X6Ixf0of9Ozg12yi8NErg1vc19Gz2aaGa1WGRrbGyw/1NEvb0xfvNWq9ryGOJF/Hf3+7gKhwf0y9r5tFElEiIiKyfhwxdHCGjhYuemGAmSIhcxMEARfSCrEzLhUJKXlQKeUYHtUao/uHIdDbxaC2vNzVBp1/7loBurY1T3VPKfTtEoC5k6OwbHO86G1PW7wXm94YKXq7AJBwKRcbt59F5zbeePHBHg2mCRMREZHj4qcCMkigj6vUIZCB9HoBx89l41//OYHF/3cKKZnFmDikPZbOHISnRncxOCm8be3rw1t87pKvTxnVhzXr3s4Xf3sm2ixtm6Noz4W0QqzZloTQAHfMejQSKifLrWckIiIi68fE0IGt//mMQeevfm2YmSIhc6iq1mHvqQy8uf4I1mxLQllFNZ4e3RlLZw7ChCHt4eGqMql9tUqB9sEeLT6/vLLapP6sUYfWnnh3eoxZ2hYzObx2vQQrv02Av5czXpvUCy5qThYhIiKiupgYOrDDZ24YdD4/TNqG0opq/HTwCuatPYQvd52Hm7MSMydG4P0ZAxDbJxRqEUeK3nq65SNmr6w4IFq/1iTE3w1LXhxolrbFSA6z8srw4dZ4uKqVmDMpCp4mPhAgIiIi+8RP+g4qI7fMoPPXzx9hnkBINDmFFfjtaBoOJGaiSqtHZEc/jIsJQ+c23marOimXyzCqbyh+P5HeovMFQbDLCpj+3i746JXBmLf2ELQ6cYvHTF20x+g1h/nFlVi+JR4yAHMm94avp7OosREREZH94IihgzJ0HzqFnH9VrNXV68VY92MS3vj0MPbFZ6Bft0C8O60/Xn2sF7qE+Zg9EZtyT6cWnztt8V4zRiItL3c1Vvx1CHw9DSvM0xLv//eEwdcUl1Vh2eZ4VGi0eH1SFIJ8uT6YiIiImsYRQwek1ekNOn/jglgzRULGEgQBSVfysePINZxLLYSLWoGx/cNwT3Qb+HiIn5jcjUwmw5P3dsZXuy9YtF9r5OrshPemD8Di/zuJq9dLRGv3UnoRvv/jMh4e1qFF55dXavHh1njkF1fi9UlRCGvV8rWgRERE5Jg4DOSAZizd1+Jz2wV52OXUP1ul1elxKCkLCzcdxUdbE3CjoAKPx4Zj6UuD8VhsuMWTwttG9Q1t8bnmqLhpTdQqBd58ui8iO/qJ2u72Q1dx4nzze0JWVeuw6rtEZOSUYeZDEejcxlvUOIiIiMg+MTGku3rn2X5Sh0AAKjRa7IxLxYJ1h7Fh+1kIAjDt/m5Y/OJAjI0Jg6uz9IP/sx+NlDoEq6FUyDHrkUgM7hkkarurf0hCenZpk8e1Oj3WbEvCxbRCTB/fHZEd/UXtn4iIiOyX9J8myaIMGa0ZGxNmxkioJQpLNdh9PA37TmWiQqNF1zBv/GVsF/Ts4Gd1I7m9wluehJhSUMVWyOUyTL2vGzxdVdgRlypau+9sOoqVs4Y02G5ELwjY9MtZJKbk4ekxXRDTvZVofRIREZH9a3bEcPHixRg5ciS6dOmCCxfurCG6cuUKJk2ahDFjxmDSpEm4evWqZMfIPB6PDZc6BIeVmVuGTb+exfy1h7AzLhU92vvi7b9EY/6UPojs6G91SeFtCznCXIdMJsNjseF4LLajqO3OXnWwzlphQRDw1e4LOJJ8A48M74DY3iGi9kdERET2r9nEcNSoUfjqq68QElL3g8bChQsxZcoU7Nq1C1OmTME777wj2TFqGUNGC198sIcZI6HGCIKAC2mFWPVtIv62IQ5Hk29gaK/W+GDGAMycGIH2wZ5Sh9istkEtL3LiSMVqxsW0xbPjuora5oyl+yAIN7fG+OHAFew9mYGxMWG4b0BbUfshIiIix9BsYhgdHY3g4OA6r+Xl5SE5ORnjx48HAIwfPx7JycnIz8+3+DEyj/7dOA3NUvR6ASfOZ+P9L09g0VcncSmjCA8OaY+lMwfh6dFdEOhjW9sMLH1pUIvOa+neh/+/vTuPj+H84wD+2d1ckpBbhFASxBkhgjjjKg2RiyZRFC1FVdWZoD89EHFVo1WlqpezkoqjlBahDVE0rtYVR1CJO/ex2Z3fH5FpItncye5mP+/Xqy/NHM98d565vvM8M1Nb9O7QEFN82lVpmW+EHcEvpxKwN+YWenewwwgPR41tTSYiIiLNVqFnDO/fvw9bW1vIZDIAgEwmQ/369XH//n0IglCj4ywtLcsVu5WVaUV+crWzsane18l7zYwq87SfvNen2uPRdlWxfrLlChw+fQe7jl7Hv4/S0cDKGJP8nNHfrTGMDLT38d/yrJunmblo2cSiGqP5jyZs06/Y1EWD+nXxv/UnqqzM7Yevo0eHhpgxqjNk0tqVFGpCnVH5sM60C+tL+7DOtI821Zn2Xn1W0OPHaVAqBXWHUYiNTV08fFh13zyrLDNDmUbFo2kqW19pmXIcPnsXv525i9QMOZrZ1cVkn3ZwbWkDqVSC1ORMaPva/2x6L0xdfbzU6WZ+eqxGXkKjSfuYvWUdzB/tisXfl/+j9ar8ce5fvPGK6reVaiNNqjMqG9aZdmF9aR/WmfbRxDqTSiUqG8oqlBja2dkhKSkJCoUCMpkMCoUCDx48gJ2dHQRBqNFxVLqI6PgyT7tqao9qjES3PXqWiV/+vIPj5/9FjlwJZ0crDO7SBE5NzGtd9z9jI32YGOkhPSu31GmzcxQwNJDVQFSaw7GRGT4a3wX/+/pUlZWpC296JSIioupToe8YWllZoXXr1ti7dy8AYO/evWjdujUsLS1rfByVbt+J22We1txUPR9Ir81uJ6ZiXdRFBH95Ekf/ugc3p/r46I0umD6iA1q9ZFHrksJ8Zb3JMHlVdDVHopns65ti6ST3Ki2zPC+YIiIiIipIIuS/1k6FRYsW4eDBg3j06BEsLCxgbm6Offv2IT4+HsHBwUhJSUG9evUQFhYGBwcHAKjxceWha11Jn6RkYdbamDJN+8WMPjrXclMRZakvQRBw6eYT7I9NwD+3n8LIQAYPl0YY0NkelvWMaihS9Vu6+Syu3nlW6nQb5/at1gRZE7ty5HuWlo35G2KRmV1662pZ1YaWQ02uMyoe60y7sL60D+tM+2hinZXUlbTUxLC20bXEsDwtCLXhYrImlFRfuQol/vznAfbHJuDuwzSYmxpgYOfG6OPSCMZGOvdILxRKJSYsO1qmaatz+9PEA3NBaZlyLPruNB48zayS8lyaW2PacOcqKUtdNL3OqCjWmXZhfWkf1pn20cQ6q/JnDEk7KMuR86+f7VF9geiAzOxcHD/3Lw6evoMnKdloaG2C8Z6t0a2tLfRkFeqxXSvIpFL0cWmI6Lh/1R2KRjOto48Px3XBiu1/If5eSqXLi7v+CAdiEzC4a5MqiI6IiIh0ge5eseqAN8OOlHlaXU5eKuNZWjZ2Ho3H7LUx2Hb4OmzM6uDd4c746I0u6Olsx/UKYMwgpzJNp+vPxxkayDAnqBNcmltXSXk7jlzHpZv81isRERGVDVsMCRvmeKg7BK1z/3E6DsQm4MSlRCiUAlxb2mBw15fg0LCeukPTOBKJBK/2bY4dR66rOxSNp68nxVS/9vhm/2X8fuF+pctbuT0OoW91g62FcRVER0RERLUZmzNqqbK2vtiYG0Em5WZQVtfuPsOir2Mxf0MsTv6dhF7ODbFkYjdM8W3PpLAEZe3SqOuthkBe3/9xnq0wwNW+SsoL+fJklb7YhoiIiGonthjquNC3qvZ1+bWRUhDw19VHOHDqNuLvpaCusQGG9WiKfq72qGdsoO7wtMZkn3b4YtdFdYehFZSCgKep2VVW3tufHMNXc/pCKq2dn0YhIiKiymNiWAuVtdWlZ3s7SGvpN/SqgjxXgT8uJuKXU3eQ9CQD1mZGeG1gS/j0bYHUlKp5e6QucWtVH1+UYbpdx2/Ap1f5P0VTWwiCgG/3X8GZqw8R1L8F9PWl+O7AlUqX++ayI3zzMBEREanEPoQ6bJxnK3WHoJHSMuXYE3MLs9fG4LsDV2BkIMMk77YIfasb+rvaw8iQ91MqKvi1TqVOs/uPW9UfiIYSBAHbD1/H7xfuY1iPphjo1hgeLo0wybttlZTPrrpERESkCq9wa5myXvgFDWhRrR8U10aPkjNx8M87OH7uPrLlCrR3sMLgrk3Qqok511UVadnYvEzT3XuYhkY2xX9jpzbbe+I2Dv55B/1d7eHds5k4vEtrW9Qx1MMnO85Vehnjlx5myyEREREVwRZDHVVVL7aoDW4npuLL3ZcQvO4kjpy9B1cnG3w0vgvee7UDWr9kwaSwii16s2up07y/8VQNRKJZDp+9i5+O3YB72wbF3rhp72CFkFGlt7iWBVsOiYiI6EVsMaxFDv55p0zTvTvcWeeTHUEQcOnWExyITcDft57CyECGgW72GNi5MSzrGak7vFqtobVJmaaT5yqhr6cb965OXkrE5oNX4dLcGuM8W6l89reFvTk+GOeGDzb9WellsuWQiIiICtKNqy4dse23a2WarkMVfUBbG+UqlDhxKREfbPoTq7afw71H6Rju4YgVU7ojoF8LJoU1ZMWU7qVO89aKo9UfiAaIu/4IX+39B05NzDHZpy30ZCUflpvY1kXoW91QFbd22HJIRERE+dhiWEukZcrLNN2CMZ2rORLNlJWTi2Pn7uPQnwl4nJINOytjjPNshW5tGuhMq5QmKWsCLghCrW7dvpLwFF/suogmtqZ4x98Z+nqyMs1na2GMFW/3wAebTiE1o2z7vipsOSQiIiKALYa1xrRPj5dpOl37CHtyWjYiouMx6/MYbPvtGqzqGWHacGd8/GZX9HJuyKRQjdZM71XqNG+EHamBSNTjdmIqPt15HtZmRnjv1Q6oU8633VrUNcTiCd1gZ2Vc6VjWRfH7kkRERLqOLYa1gCAIZZruo/FdqjkSzXH/cTp+OXUHMRfvQ6EQ0MnJBoO7NoFjQzN1h0bPmRjpw8zUAMlpOeoOpcbdf5yOldvjYGKkj5kBLqhrbFChckzr6GPBmM5Y/eM5XLubXOF4Tv3zAE5N7qFvx0YVLoOIiIi0G5tLaoGytqrY16/9r/+/fjcZayLOY8GGWMRcTERP54ZYMrEb3vZtz6RQA4W95V7qNLXtObjHyVlYuT0OUgkwK9Cl0s+11jHUw6zAjnCp5LPD3/9yBVfvPKtUGURERKS92GKoI0IndlN3CNVGKQiIu/YIB2ITcP1eMkyM9DC0e1P0d7VHPZOKtcRQzTDQl6FlY3OdSUhS0nOwYnscMrMVmDuyI2wtK98NFAD09aR4268dvtl/GX9cSKxwOUs3n8WKKd35EiYiIiIdxBZDLVfW1pSqugDVJPJcBaLj7mHBhlh8FnkBz9KyMXJAC6yY0gO+vR2YFGqJ2UEupU5TG1oNM7JysWpHHJ6mZGH6CGc0sa1bpeXLpFKM82yNl90aV6qcWWtjkJ2jqKKoiIiISFuwxVAHLJtcenc9bZKeJceRs/fw65m7SEnPwUu2dTHJuy1cnWwgk/Jeh7aRSaXo0a4B/rhY8ZYuTZctVyB85znce5iOacOd0cLevFqWI5VIENCvOUzq6OOnYzcqXM7kVdH4am5fld9TJCIiotqHiaEWK2srirVZnWqOpGY8Ss7EoT/v4ti5f5EtV6BdM0u80rUJWr1kUas/aaALxg9pXWpiuO/ELQxxb1oj8VSlXIUSX+y6iGt3k/GWd1u0d7Cq1uVJJBJ4dW8KUyM9fH/waoXLeTPsCD9jQUREpEOYGNZyq6b2UHcIlZaQlIoDsQk49c8DSCRAl9a2GNy1CRrrwMt0dIVEIsHQ7k2xN+aWymkiom9oXWKoVArYuO8fnI9/jDGDndCltW2NLbtvJ3vUMdLD+t1/V7gMfuOQiIhIdzAx1FJlaS1sZG0Cc1PDGoim6gmCgL9vP8WBk7dx6dZTGBrIMKCzPV52a8wXY9RSvr2alZgYAsCDpxmob6Edz8sKgoDNh64i9u8kDPdwhIdLzX8KolubBqhjoIdPd56vcBlMDomIiHQDH8iqxeaM7KjuEMpNoVTi5N+J+HDTn1i5LQ53H6bDv48DVk7pjsD+LZgU1mISiQQjB7QocZrgL0/WUDSVF3nsBo78dQ+vdG0Cz24vqS2ODs2tEfxap0qVURte/kNEREQlY2KohY6f/7fUabq0rl/hj2arQ1ZOLg79eQfB605i/e6/IVcoMfaVVlg2uTuGuDeFsZG+ukOkGjCgc+lv1MxVKGsgkso5EJuAfSduo3eHhhju4ajucNCysTkWjnWrVBlMDomIiGo3JoZaaNPPl0udZsygVjUQSeUlp+cg8lg8Zq+NwdbfrsGyniGm+Tvj4ze7oneHhtDX4yaqayZ4tSlx/MTlR2smkAo6du5f7DhyHW6t6mPMICeNeTHSSw3qYkklv2fK5JCIiKj24lW3lsnKyS11msFdmsDYSLMfH018koFvD1zG7LUx2BdzG05NLDBvtCtCRrnCpYU1X5Ovw9zbNlB3CBV2+vIDfHvgMto5WGKCVxtIpZq1HTewNMaKKd0rVQaTQyIiotqJiaGWmbLqWKnTePdsVgORVMz1e8n4LPIC5q8/iT8uJKJH+wZYPLEbpvq1R/NGZuoOjzTEtOHOJY7XxOTk0s0n+HL3JTg2NMPbPu2hJ9PMw6tlPSN8Oq0nKnPvZfWP56ouICIiItIImt2sRIUIglDqNCM8HGFoIKuBaMpOKQg4d/0R9scm4PrdZJgY6WFI96bo72oPMxPteQ6Sao5Lc2t1h1Au1+8lY03kedhZmeDdEc4atw++qK6xAT6b3hsh608iJT2n3POfj3+MQ6fvYGAZngklIiIi7aCZt7SpWG+EHSl1mgGd7WsgkrKR5ypx7Ny/eP+rWKyJuICnKdkIGtACy6d0h19vByaFVKLZgS4ljteUVsM7D9Kwesc5mJsaYmagC0y05EVJdQz1sHyyOxrZmFRo/q2/XsPVO8+qOCoiIiJSF7YY1iKjBzlBX0/9LRXpWXIc/esefj19F8npOWhia4qJw9rArVV9yKS8F0Fl07qppbpDKNWDpxlYtT0OBvpSzApw0bqbHfp6Mnwwzg0rt8XhckL5k7ylm89i5ds9YFFXO7+XSkRERP/hVbqWKEvrSC9nuxqIRLXHyVnY9ts1zFobg4joG7Cvb4qZgS5YONYN3do0YFJI5RYyquTv76mz1fBpajZWbIuDQilgZmBHWJvXUVsslSGTSjErqCM6t6pfoflnfv4HsuWKKo6KiIiIahpbDGuJN4e2VtvLLu48SMOB2Ns49c8DCALQpU19DO7SBE1s66olHqo9WtibqzuEYqVlyrFyexxSM+WYE9QRjawr1h1TU0glEkz2bostxgb47ezdcs8/eWU0vprbl28TJiIi0mJswtECZWkV6damZl/xLwgC/r71BKu2x2Hh16dw9uoj9He1R9gkd0z0asukkKrMgjGdSxy/+/ebNRRJnszsXHyy4xwePM3ENH9nNLOrV6PLry4SiQSvvdwSPhV8q/GbZXgGmoiIiDQXWwxrgSk+7Wrse2kKpRKnLz/EgdgE3E5KRT0TA/j3cYBHx0Za89IN0i4ODUtOvHb9fhPDaugTLfJcBT6LvIDbial427cdWr9kUSPLrUnDejaDSR19bD50tdzzjl96GF8H96uGqIiIiKi6scVQw5WltbCTk021x5Gdo8Ch03cQ8uVJfLn7ErLlCox9pRWWT3bHEPemTAqpWpXWavgoObPaY1AolVgXdQn/3H6KcZ6t0LFl9e936tLf1R4Th7Wp0Lya8rZYIiIiKh+2GGq56SOcq/W5npT0HPx65i6OnL2L9KxcNLc3Q1D/FujQwprPE1GNKa3VcM4XJ6q1pUopCPhm/2X8de0Rgga0QI/26n3RU03o1qYBjA31K/Qxe7YcEhERaR+2GGqwo3H3Sp2mvYNVtSw76UkGvjtwGbO/iMG+mFto2dgc80a5Yt4oV3RsacOkkGrc/NGuJY5XKoVqWa4gCNj+23X8cSER3j2b6dRH3Z0drTBvVMnrXRW2HBIREWkXJoYa7LsDV0ocPzuoIyRVnKDF30vG55EXMG/9Sfx+IRHubRtg0YSueMffGc3tzap0WUTl4dio5O3vzWXV8/KTPTG3cOj0HQxwtcewHk2rZRmarLm9GT4a36VC8zI5JCIi0h5MDDVUTinfBZNKJFX24gulICDu2iMs/eEMFn9/Bv/cfooh3V/C8indMfaVVrCz0u5X8VPtEfxayd81rGq/nbmLXcdvonu7Bggc0KLKb8RoC/v6plg6yb1C8zI5JCIi0g5MDDXUpJXRJY4v7cPfZSHPVeL4uX/x/lexCI84j8cpWQjq3wIr3u4Ov96OMDMxqPQyiKpSy8Ylf9ewKpOQE5cSsfnQVbg0t8Y4z1Y63326vnkdfDK1R4XmZXJIRESk+ZgYaiBBKPlZqfrmdUrtVleSjCw5fj55G3PWxWDT/svQl0kx0asNQt9yx0C3xjAy4DuJSHPNDupY7cuIu/YIG/f+g1ZNzDHZpy1kUh4qAcDM1BCfTe9VoXmX/HCmiqMhIiKiqsSrHQ30Rikfip7i265C5T5JycK2365h5toY7DwaD3trE8wMcMHCcW7o1rYB9GTcHEjzldaFurKtU5dvP8XaXRfxUgNTvOPvDH09WaXKq22MjfTxxcw+5Z7v+t1k/HzydjVERERERFWBTUNapoW9GZrY1i3XPHcfpGF/bAJO/ZMEQQC6tK6PQV2a4KUG5SuHSFNMH9GhQp9RKM3N+ykIjziP+hZ1MH1EB9Qx5CGyOIb6Mqyf7YGJy4+Wa76dR+PRrEFdtG5qWT2BERERUYWxiUjDlNba8frgVmUqRxAE/HPrCVbtiMP/vj6Fs1cfom+nRlg6qRsmDmvLpJC0WnuHkhOLirQa/vsoHZ/sOAcTI33MDHBBXWM+Y1sSPZkUX83tW+75lm+Lw8NnmdUQEREREVUGE0Mt0rGFNRpal/yGUIVSiVP/JOGjb09j+bY4JCSmwre3A5ZP6Y6RA1rC2qxODUVLVH0kEgne9m1fZeU9Ss7Eyu1xkEolmBXkAou6hlVWdm0mlUiwcW5fGBqUr7vt3HUnkJmdW01RERERUUUwMdQgpbVyBPRvoXJcdo4Cv525i5AvT2Jd1CVk5Sjw+mAnLJ/SHV7dm8K0jn5Vh0ukVh1bWpc4fvH3p8tUTnJ6DlZui0N2jgIzXu0AWwvjqghPZ0gkEnwxow9sLcu33t7+5BgUSmU1RUVERETlxcRQS/Ro3wD1zYu29qVk5GDX8RuYtfYPbD50FWamBpjq1x6LJ3RFH5dGfHEG1VpSiQQTh7VROT7+XkqpZWRkyfHJ9jg8Tc3G9BEdyv38Lv0ndGI3tLQv39uSJyw7WupbmImIiKhmMDHUEKW1Fvr2cij0d9LTDHz3yxXMXhuD3X/cQsvG5ggZ1QnzR3dGp5Y2Ov/NNdINXVrZljg+ISlV5bhsuQKf7jyPe4/SMdWvPZqXM6mhooJHuaKzk0255intLcxERERUM5gYaoEBrvawrGcEAIj/Nxmf/3QB8748id/P/wv3trZYPKEr3vF3Rgv7kj/+TVTbSKUSjPNU/UKmDzb9WezwXIUSa3+6iOt3kzFxWFu0c7CqrhB1zhTf9ujbqVG55qnsJ0aIiIio8vgudg3w+oe/lDh+SPemiLv+CAdiE3D1zjMYG+rB0/0l9He1h7kpX5JBus29bQNs+vmyyvHKF7oqKpUCvtr7Ny7ceIzXBzvBrVX96g5R54x+2QmmRvrYE3OrzPOMX3oYXwf3q76giIiIqERsMdQAT1KyVI4zMzHA8q1/IXzneTxKzkRgv+ZYPqU7/Ps4MikkQt5nE0YOUP1ipjcLdFUUBAE/HLyCU/88wAgPR/RxKV/LFpWdb28HBJbwwqzisOWQiIhIfbQuMbx58yYCAgIwaNAgBAQE4NatW+oOqVKepmaXOD45PQdSiQQTvNpg6VvueLlLE350m+gFvTs0LNN0kcdu4Gjcv/Ds9hJe6fZSNUdFL7s1xnjP1uWah8khERGRemhdYrhw4UKMHDkSv/zyC0aOHIn//e9/6g6pUmZ+/keJ42cEdMCH493g3rYB9GRaV11ENcJAXwb/Pg4qx3vNjML+2NvYd+I2PFwaljgtVa2eznbl/uYkk0MiIqKaJxG06F3hjx8/xqBBgxAbGwuZTAaFQoGuXbvi4MGDsLS0LGMZaVAqNecn8wKIiKh4s4M6Vl/h2nPqK1VFfolQ4H+E/L8K/CMUmEAQSp4nf3i9ekZITs4qNE/B+QpebvxXplBoufllv1g9gopl/jdeeCG+wgsq/Jv+i0XVPEJDoYQ/AAAcFklEQVTh2f+LtcBMgsrfVKAMFdP+95sKlF7W31xg3RX+TUXjLHae54OaN7ZAPSPVPZAKrFHtVkt+BgB0cW6EjDTVjx+R5rGxqYuHD1W/IV0dpFIJrKxMix2nVX0S79+/D1tbW8hked/mk8lkqF+/Pu7fv1/mxFDVilAHr5lR6g6BiEhjLd/6l7pDIKq1jpy9p+4QqJyG3EvBJD9ndYdB5WRjoz3fSNaqxLAqaFqLIRERERFRafb9cRP7/rip7jCoHDbOHwiJQqHuMAopqcVQqx5as7OzQ1JSEhTPV7BCocCDBw9gZ2en5siIiIiIiIj+k5WTq+4QykWrWgytrKzQunVr7N27F97e3ti7dy9at25d5m6kRESkPULf6gYJAIlE8t+/krx/AUCaNzBvWMHxyJ+uuHkLjpeo78fVQpr4LA2pxvrSPqwz7aNtdaZViSEAfPDBBwgODsbatWtRr149hIWFqTskIiKqBrYWxuoOgYiISGdoXWLo6OiIH3/8Ud1hVImXGtTF7UTtuYtARERERES1k1Y9Y1jbvOzWWN0hEBERERERMTFUp6YNtOf1tUREREREVHtpXVfS2sTc1FDdIVQ5A30pGlmboKGVCRpYGcParA7MTQ1gYqQPYyM9GBnIoCeTQiaTQCqRaMXLHwRBgEIpQJ6rhFyhhJmZMRIfpCD3+d+5uQLkCiXkuUrkKvL+y59WHKZiWvHfYqbJfaEMea4Siir61IpMKoEgAMpa9JHv0sikEjSubwo7KxNYmRnBsp4hLEwNUc/EAAb6MhjoSWGgl3evTKEUitSjPFeJnNzn9VJgeE6uIu9fuRJZObnIylEgMzvv36ycXGRmK5D5fHh2jma9spqIiIgoHxNDNTLQr74GW2szI1jVM4LV83/NTQ1Q19gApnX0YWggg6H+8/+e/7+eTDuSNHWQSCTQk0mgJ5OiDgBr8zoQ5Op5/bBSEKAQExYB8lwFchVCoSQyP6ksmNjkKoSiyWgx0+Y+L1OuEFROm5+8altSuWFOX3WHoBEEIa/ulEoBuYq8mx7K54lwtlyBHHnevy8muS8mu1k5zxPe58MzsnKRkV11+0VtvHFGRESkyZgYqpG0QCI2wsMRdY0NUNdYH0YGMhXJG3v+6jqpRAKpngz6ejJ1hwKFskDrZilJZLGtqEWGCQXmKdpamteyKiA3P3EtMG1pKaqdlUmNrBNtIJFIIJNIIJMC+hp8BtC2V3wTERFpOw2+LKj9JBIJ/Ho7oG+Xl2Cix9Y60i4yqRQyA8AQ6k1SC3b1LZpE5iWPrR1tIM/KUWucRERERJqMiaGaDe3elHfGiSqhYFdfVczrGuIhE0MiIiIildg3kYiIiIiISMcxMSQiIiIiItJxTAyJiIiIiIh0HBNDIiIiIiIiHcfEkIiIiIiISMcxMSQiIiIiItJxTAyJiIiIiIh0HBNDIiIiIiIiHcfEkIiIiIiISMfpqTuAmiaVStQdQrE0NS4qHutL+7DOtA/rTPuwzrQL60v7sM60j6bVWUnxSARBEGowFiIiIiIiItIw7EpKRERERESk45gYEhERERER6TgmhkRERERERDqOiSEREREREZGOY2JIRERERESk45gYEhERERER6TgmhkRERERERDqOiSEREREREZGOY2JIRERERESk45gYEhERERER6Tg9dQdARESkqeLj47Fw4UJkZmbC3d0ds2bNUndIAIAnT57g888/x8WLF5GTk4P+/ftj6tSpNbZ8QRDw448/Yu/evcjMzIQgCNi+fTtkMlmNxfCin3/+Gd9++y2USiXGjRsHT09PtcVCRKSNJIIgCOoOgoiIiMpGLpdj1KhReP311/HKK69AIpHUeAzr16/HjRs3MH/+fNStW7fGl09ERFWPXUkL+OuvvxAUFIRhw4Zh2LBh+P3333H+/HkEBATAy8sLAQEBOH/+PAAgJCQE3t7e8PDwQOfOneHt7Q1vb2+kpaUhODgYCxYsQGBgIAYNGoQFCxYgJycHSUlJ6NmzJ7Kzs8VlTpo0CXv27BH/7tq1Kzw9PeHt7Y127drh6tWrAIDIyEhMmzZNnO7q1avo168fAODu3bvo2rWrOG7RokVwcnJCeno6AIj/r1AoMGPGDISFhVXfSqxBTk5O8PLyEtd9hw4dEBsbCwB48OABpk2bhuHDh8PLywvr1q0T5+vXr5+4XgFg2rRpiIyMBACkpqYiJCQEXl5eGDZsGD766CMAwJo1a8T1lp6ejl69eon1oVAoEBYWhqFDh2Lo0KEICwuDQqEAAAQHB+OHH34Ql/XDDz8gODhY/Hv9+vUYPnw4fH19MWnSJDx8+FBc3rvvvosxY8Zg8ODBeOedd5Camlrl67AmlbQdrl+/Xlx/ISEh4ra7Zs0aODk54e+//wYAKJVK9O/fH35+fgCA2NhYdOjQQdwGvLy84OTkJJY7c+ZM+Pn5wcvLC2+//TaSk5PF+fLLAPLqtOB8BfefgtLT0xESEiLGumHDBnHc6NGjceTIEfHvsLAwrFmzplLrrLq9uA+dPn0aALBr1y54eXmJ6+3x48fiPF9++aW4fwQGBkKpVAIAdu7cKR47/f398ejRIwB5x6c2bdoUqZ8PP/wQX331lVju33//jUGDBkEQBHzzzTeYNGkScnNzCx3fnjx5Al9fX3E/37NnD0aMGAEfHx/4+PjgxIkTYnkF9/MXj59+fn5iGSXt2wXnUyqV8PHxKbTdqMuJEyeQlpaG9evXY9iwYRg9ejTi4+MBAD/99BNGjBgBPz8/jBkzBjdu3AAA/Prrr/Dz84OPj494fgPyfuO4ceMwadIkeHp6YsyYMUhKSgIAXLlyBSNHjoSvry88PT3xzTffiDF8//33ePz4MYKCguDl5YUff/xRHFfR86aqY+WL9Zev4LG74PyJiYlwcXGp8nNdSeec0aNHY/HixRg+fDgGDhyIVatWifOVdD7K/x0eHh7w9vZGly5dxN9RcN/J/69Nmza4e/cugLxtfMWKFfDz88PAgQMLrb+C2//Bgwfh5OQkHp9KOlaVtj84OTnh0KFD4ryjRo0S98/c3Fy88cYb8PPzw5AhQxASEoKcnBwoFAox/i5duqB3797w9vbG9OnTAQA3btzAm2++CX9/fwwbNgwREREq13vB36/qOK0JXrye/Pbbb+Ht7Q1PT89CdXry5EkMHTpU3EcAYNOmTXj//ffFv1VtHwAQHR2NwMBA+Pn5ISAgAHFxcQBKPsd98skn8Pb2xsCBAwudP/OPFbVZ/raUf519+fJlldPm71fe3t7o2LFjoX1G1XG24LHq1q1bGDx4MM6ePQsAKnMDAEhLS8P8+fPFY8SiRYugUChw6NAhsX4K7ge7d+8uMY7KYFfS5549e4apU6dizZo16NSpExQKBZ4+fYrhw4cjNDQU7u7uiImJwbRp03Dw4EGEhoYCyNsIjh49ivDw8ELlnTt3Dtu2bYOhoSEmTpyIHTt2YNSoUXBzc8PPP/8MX19f3L17FxcvXiw0r1KpxMaNG2FnZycmfuVx7do18cBQkCAImDNnDmxsbDB37txyl6uptm3bBhMTEwAodBCcO3cupkyZAjc3N+Tk5GDs2LFo3749evToUWJ5S5YsgbGxMaKioiCVSvHkyZMi06xbtw7W1tbi39u3b8c///wjXqBMmDAB27dvx8iRI0tcVlRUFO7cuYMdO3ZAKpViy5YtWLp0KVauXAkAOHPmDHbt2gVra2uEhIRg7dq1Wl93xW2H0dHR2L17t1iXc+fOxdq1azF79mwAQKdOnbBlyxYsWrQI0dHRsLW1RVZWllimo6OjuO7T09PRqVMncdz8+fNhaWkJIO9kuGHDhkp1BVy7di2USiX27NmD9PR0BAQEoGXLlujTp0+Fy1S3gvsQkHfTacWKFYiMjET9+vWxevVqfPzxx1i9ejV++uknHD58GFu3boWpqSmePn0KqVSK2NhYfPnll9iyZQtsbGyQnp4OPb2804tSqYSZmRmioqIK1c+oUaMwadIkvPHGG5BIJPjhhx8wcuRISCQSjB07VrxJk3+STUtLw+TJkzF16lTxQrRnz54YOnQoJBIJbty4gbFjx+LYsWMVXhcv7tsF7dixQy0tc8V5/PgxEhISEBkZiRYtWmD37t2YPn06Fi5ciP3792Pz5s0wMDBAdHQ05s2bh23btmHAgAEYMGAAAGDr1q3YsGEDevbsCeC/Y42DgwM+++wzLF68GOHh4WjUqBG++eYbGBgYID09HSNGjECvXr3g6OiIBw8ewN/fHxs2bMDjx4/h5+eHVq1awcnJCdOmTavQebOqLFu2DM2aNauWslWdc4C8bsfbtm1DdnY2AgMD0bFjR/Tt27fU85FCocD06dPh4+NT6MYhANStWxdRUVHi3wVvAgN520JkZCQePXoEHx8fdO7cGa1atRLHZ2dnY82aNWjatGm5f2tx+0OHDh2wfft2DBw4EFevXhVvDAGATCbDihUrYGFhAUEQMHfuXERERCAoKEj8DcHBwWjXrh1GjRoFIC+ZnDVrFpYvXw5HR0ekpaXB398fLi4ucHR0FG+y7tixA3Xq1Cny+zVRcdeTaWlpeP3113H37l34+/sXqtPXXnsNW7duhbOzMwRBwNatWwvtG6q2j4SEBKxduxYbN26Eqakprl27hgkTJuDo0aMlxvfee+/hvffeQ2xsLMLCwsTzp67Ytm0bjI2NMWfOHERHRxfaXwpSKpUIDQ1F586dMXr0aHH46dOnVR5n8yUkJGDy5MlYvHhxoWsSVblBaGgo3NzcsHjxYiiVSsyaNQsRERF49dVXMXDgQAB5SW3B409Z4qgIJobPxcXFwdHRUaxAmUyGx48fQ19fH+7u7gCA7t27Q19fHzdv3izUulAcT09PsfJ8fHxw8OBBjBo1CqNHj0ZoaCh8fX2xbds2+Pv7w8DAAEDeRpiRkQEzM7Niy4yJiYG3tzcAFGp1LGjJkiUICQkpkpTMmzcPsbGxpR4waoOMjAycOnWqUFKXnp6O+Ph48UQ8bdo0GBoaAgDu3bsHDw8PAMCRI0cQGRkJqTSvMT0/qciXkJCA48eP47XXXsPx48cB5N299/X1FevRz88Pv/76q1gH69evF++mP336FN27dwcAHD58GBcvXoSvry+AvIO/qampuCwPDw/xpDx8+HAsWrSoitaQ+hS3HZ44cQKenp7ib3/11VexZMkScbyLiwvOnTuHlJQUbN++HQEBAfj222/LtLyoqCjs2bMHcrkcGRkZhS6O4uPjxf2p4MVNvsDAQEilUlhbW2P+/PlwcHDAiRMnMG/ePEgkEpiammLIkCE4ceKEmBguWrQIq1evBpDXSlDazQFNFBsbiz59+qB+/foA8tZD/no6cuQIgoKCxLqysLAAABw9ehTe3t6wsbEBgEKJZmZmJoyMjIosx9HREY0bN8axY8fg4uKCw4cPIyQkRBz/1ltvoW/fvmIr8+TJk2FsbIz+/fuL09y5cwczZ85EUlIS9PT08OjRIzx8+FCMozyK27fzpaSk4LvvvsOMGTOwdu3acpdd1QRBgKurK1q0aAEAYu+GqKgoXL58GSNGjBCnS0lJEefbvXs3Pv/8czx9+rRQi5arqyscHBwAACNGjICXlxcAICsrCx988AGuXLkCiUSCBw8e4PLly3B0dASQt20AgJWVFQYOHIgTJ07AwMCgwudNVcdK4L/znyAI6NatW5EEKt/p06fx7Nkz9OvXDxkZGWVco1XDx8cHenp60NPTg6enJ06ePImuXbuWej5KS0uDubl5hZY5fPhwAIC1tTU8PDxw6tSpQhe6GzduhKenJ2JiYgrNV9qxStX+0KBBAwDAzZs3sXnzZowcORIff/wxgLzj6Ndff41jx45BqVQiOTm52H2/oFu3biE+Ph4zZswQh8nlcty4cQOOjo7IzMyERCIRz9cvKu44rW7FXU+quq4DAG9vb3z++ed49uwZzp8/Dysrq0J1qGr7OH78OBISEvDaa6+Jw3Jzc8XeGqWd43RVYGAg0tLSIJFIxBvQxUlLSyu23g4fPlzicfb+/fsYM2YMPDw84OrqWmheVbnB4cOHcf78eWzatAlA3rHX1ta2xN9RWhwVxcSwhuXfPTpz5gx++ukn7Ny5Uxx37949mJqawtjYuNh5u3fvLt5Funr1KiZNmlRo/KFDh2BpaVlkQwTyTtyBgYFYtmwZFi5cWIW/SPMolUpIJBLs3LkT+vr6xU4THh6Oli1bAkCxXZRUCQ0NxezZs8WuVmUxceJE8e7oDz/8gIsXLwLI24knT54snth1QUW3Q19fX3z66acwMDAQL0xKc/r0aWzduhXbtm2DpaUl9uzZgx07dojjS2ppBP5rGfjqq6+wYsWKMiUECxYsQN++fQGg1nTZrqzExESVJ7jRo0dj69atiI+Px8svv1zoWbXw8HAMHz4ciYmJSE1NxcCBAxEdHY2DBw/i5ZdfBgDMmDEDwcHBGDBgAJRKJTp06KDypllpStq3w8PDMXbsWI15lk5VHIIgwN/fH++++26x4/O7tUVGRiIyMlJsMVRl1apVsLGxwdKlS6Gnp4fx48eL67fgTSwAVdKaqupYCfx3/svOzkZQUBAOHz5cZH6lUomlS5di6dKl2L9/f6XjqQplOR/dvn0bDRs2rPJlJyYmYv/+/YiIiCiSGJZ2rCppfwgKCsL69etx584dTJgwQRy+Z88enDlzBps3b4apqSnWrVuHW7dulRijIAiwsLAo1IJWUFJSEmxsbMSbtS+qyHFa0xgbG8PLywuRkZE4depUoUQPKHn76NWrF5YtW1ZkeHx8fKnnOF1VcJtZvnw5li9fXmSa7OxsPHjwoNjrjdKOs9euXcPq1auxbNkyxMXFwcXFpdSYBEHA2rVr0bhx4zL/jtLiqCg+Y/ici4sL4uPj8ddffwHIa72xsrKCXC7HyZMnAeS1bOTm5papi8qBAweQkZGB3NxcREVFoVu3buK40aNHY8aMGejYsSPs7OzE4fv27RObjMsrNzcX4eHhmDNnTrHjZ82ahalTp+L8+fOIjo6u0DK0hampKVxdXbF+/Xpx2P3798Xn90rSt29fbNy4EfnvZCp4l/fEiROQSqVFuqO6u7tj165dkMvlkMvl2LVrV6E73ar069cPW7ZsEZ97y8nJKdTf/ejRo+LyIyMjC21D2qq47dDd3R379+9HWloaBEHAzp07i6w/Ly8vnD17tsgJsyQpKSkwNTWFubk5cnJyijy3UlaWlpaQy+VirBERERAEAWlpafj555/LVNfapGvXroiOjhb3lx07doi/sW/fvti6dSvS0tIA5LXqAHmt21FRUeKd6vT0dGRnZ0OpVGL37t0qt90+ffrg5s2b2LRpU6EWi/Pnz+P333/H22+/jXfeeQfm5uYYM2YMFi1ahJUrV4r7RWpqKuzt7QEAERER4vMa5aVq3wbyWkbOnTunUTdwnJ2dcenSJfG5wn379sHOzg7e3t6IiopCYmIigLzzWH5y9ezZM3H+OnXqiM/tAsDZs2fFC/iIiAixvlJTU9GgQQPo6enh6tWr4jOoAODm5ibeaHny5Al+/fVXuLu7o1mzZhU+b5aFoaEhTExMiq3ryMhIdOzYEc2bN6+SZZXX7t27kZubi4yMDOzfvx/dunUr9Xx09uxZGBgYiDcqy+unn34CkFcH0dHRhbparlq1Cu+++67Ym6WsStofgLzj4P379/Hqq68WGp6amgoLCwuYmpoiNTUVe/fuLXVZzZo1g5GREXbt2iUOi4+PF48xu3btKtO5r+BxWt2Ku57MP8+rMnLkSHz77be4ePGieOMLKHn76NGjB44fP45r166Jwwo+q0glq1evXrGPCwF5z+W6uroWexOuX79+Ko+zANC7d294eHhg6dKlCA4OLvQcrKrcoF+/fli/fr3YdfrJkye4c+dOifGXFkdFscXwOXNzc6xZswZLly5FRkYGpFIp5s6di/DwcCxevBgZGRkwNjYWWy1K0759e4wfPx5PnjxBly5dCh1AhwwZgo8++qjQhdCWLVuwevVqNGzYUGz6z39g/cCBA6UuLy0tDUOGDCmx6VlfXx/Lli3DxIkT8eOPPxbpJlmbrFixAqGhoWKXKBMTEyxevLjULmYhISFYsmQJhg4dCplMhi5dumDBggUA8k5Wxb1MJCAgAAkJCWKX0J49exY5YRbHx8cHz549E++QC4KAoKAgsQtJ586d8d577yEpKQnNmzdX2XVK27y4Hfbp0wdXrlwRu6W1a9cOkydPLjSPkZGReAGU/7KH0vTq1Qu7d+/GoEGDYGFhgc6dO+PChQtljjP/2Te5XI4PP/wQADBlyhR8/PHH4nY1bNgw9O7du8xlaoOWLVti1qxZGD9+PACgcePG4kuYfHx8kJSUhICAAOjp6cHY2BibN29G165dMXHiRIwbNw4SiQQGBgZYt24dPv74YxgYGBRqVShIKpXCx8cHx44dE7f7nJwcLFiwAKGhoUWOtXZ2dpgwYQI+/PBDfPrppwgJCcGUKVNgZmaGXr16FeluNXfuXBgZGeHJkyd49uwZgoKCAOQle4sWLRJf/KVq3wbyemds2bJFZYuFOtja2mLx4sWYPn06JBIJzMzMsHr1ajg6OmL69OmYPHkyFAoF5HI5Bg8ejHbt2mHHjh3i75XJZPjf//4nltepUyeEhYXh9u3bsLa2Fu+gT548GXPmzMHOnTvRrFkzuLm5ifMsXLgQ77//vviM55QpU9C+fXsAqPB5sySxsbEICgpCdnY2HBwcMGDAgCLnxqSkJLzzzjuVWk5lODg4IDAwEMnJyXjllVfEFjlV56PExES88cYbMDAwEM/79+/fR0xMDDp06CB21S6JhYUF/Pz8kJqairfeeqtQd11LS0vxudLyKGl/yJf/IqL8F8EAeceH3377DYMHD4aVlRVcXV1LbcHX09PDunXrsGTJEmzcuBFKpRJWVlZYvXo1QkNDcfv2bbGranGKO06rm6rryZJuIjZu3BgODg5wdnYW95ULFy6UuH20b98ey5cvx/z585GVlQW5XI5OnTrB2dm5Rn6ntsrvfiyTycRzW0G//fYbQkJCYG5uLq73hIQELFq0CG3atIGbm5vK42xBzs7OGDp0KBYvXiw+HqMqN5g3bx6WL18Ob29vSCQS6OvrY968eSW2IJY1jnITqMrNnTtX+P7771WO//PPP4UhQ4YISqVSHBYeHi6cPHmyyLSjRo2qlhhJs4WHhwtLly5VdxhE1W7s2LHCzz//XOPL5bE1T0REhPDOO++oOwytN2rUKOHw4cPlmufkyZNCeHh4keGqrgde1LdvX+HKlSvlWiZpptTUVKFHjx5CYmKiOKyy2wdVTEREhBAREVFk+Ny5c4U7d+5UuNzScgNNwRbDGjZv3jzExMQgLCys0DMZgwYNgpWVVZHpp0yZUpPhERHViAsXLuC9995DmzZtMGjQoBpfvr+/f40vk6ig5s2bF9sqqOp6gGqnrVu34osvvsD48eML9fri9qEeBXtGFBQUFFSre9rl4wfuiYiIiIiIdJzmPDRBREREREREasHEkIiIiIiISMcxMSQiIiIiItJxTAyJiIiIiIh0HBNDIiIiIiIiHfd/H4g50spDa4UAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#построим график для групп по типу занятости\n",
    "#sns.barplot(x = 'income_type',y = 'days_employed', data = df).set(xlabel = 'Тип занятости', ylabel='Стаж в днях');\n",
    "plt.plot(df['income_type'], df['days_employed'])\n",
    "#plt.legend(income_type, ncol=2, loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим, что значения очень сильно разнятся для пенсионеров, безработных и остальных. \n",
    "\n",
    "Скорее всего это систематическая ошибка,связанная с неправильным заполнением форм.\n",
    "Данные по стажу в заявки на кредит вносятся не в днях, а датами, например: 20/12/2011.\n",
    "\n",
    "При заполении могла возникуть ошибка с разделителями значений и дефис между датой приема и датой увольнения мог быть считан как знак вычитания 00.01.2011 - 30.12.2020 . Из-за чего появились числа совершенно различных порядков и отрицательные числа. Попробуем привести всё это к адекватному виду. \n",
    "\n",
    "Для начала заменим отсутствующие значения на нули:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['days_employed'] = df['days_employed'].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для замены данных  используем метод `abs()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['days_employed'] = df['days_employed'].abs()\n",
    "#df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Используем медианы значений по группам\n",
    "employee_days = df[df['income_type'] == 'сотрудник']['days_employed'].median()\n",
    "companion_days = df[df['income_type'] == 'компаньон']['days_employed'].median()\n",
    "pensioner_days = df[df['income_type'] == 'пенсионер']['days_employed'].median()\n",
    "civil_days = df[df['income_type'] == 'госслужащий']['days_employed'].median()\n",
    "buisnessman_days = df[df['income_type'] == 'предприниматель']['days_employed'].median()\n",
    "\n",
    "#Осуществим замены\n",
    "df.loc[(df['income_type'] == 'сотрудник') & (df['days_employed'] == 0),'days_employed'] = employee_days\n",
    "df.loc[(df['income_type'] == 'компаньон') & (df['days_employed'] == 0),'days_employed'] = companion_days\n",
    "df.loc[(df['income_type'] == 'пенсионер') & (df['days_employed'] == 0),'days_employed'] = pensioner_days\n",
    "df.loc[(df['income_type'] == 'госслужащий') & (df['days_employed'] == 0),'total_income'] = civil_days\n",
    "df.loc[(df['income_type'] == 'предприниматель') & (df['days_employed'] == 0),'total_income'] = buisnessman_days\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Доходы клиентов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видно, что отстутствующие значения присутствуют также в столбце *'total_incomе'*. Этот столбец является важным для дальнейших исследований. Для этого сгруппируем значения по типу занятости и заменим значения *NaN* в этом столбце по медианной зарплате для каждой из групп для более точной модели.\n",
    "К тому же, все значения пропуски в столбцах *'total_income'* и *'days_employed'* встречаются в одинаковых строках, что говорит об ошибке при выгрузке."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "income_type\n",
       "компаньон     508\n",
       "пенсионер     413\n",
       "сотрудник    1105\n",
       "Name: income_type, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['total_income'].isna() == True].groupby('income_type')['income_type'].count()#.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Из полученных ранее данных так же используем группы,в которых требуется найти медианное значение (репрезентативнее, чем среднее, когда речь идёт о зарплатах) дохода для заполнения пропусков в данных.\n",
    "Создадим переменные с медианными значениями по группам."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Заменим отстутствующие занчения на 0\n",
    "df['total_income'] = df['total_income'].fillna(0)\n",
    "\n",
    "#Создадим переменные с медианами зарплат по группам\n",
    "employee_income = df[df['income_type'] == 'сотрудник']['total_income'].median()\n",
    "companion_income = df[df['income_type'] == 'компаньон']['total_income'].median()\n",
    "pensioner_income = df[df['income_type'] == 'пенсионер']['total_income'].median()\n",
    "civil_income = df[df['income_type'] == 'госслужащий']['total_income'].median()\n",
    "buisnessman_income = df[df['income_type'] == 'предприниматель']['total_income'].median()\n",
    "\n",
    "\n",
    "# Заменим пропуски в значениях\n",
    "df.loc[(df['income_type'] == 'сотрудник') & (df['total_income'] == 0),'total_income'] = employee_income\n",
    "df.loc[(df['income_type'] == 'компаньон') & (df['total_income'] == 0),'total_income'] = companion_income\n",
    "df.loc[(df['income_type'] == 'пенсионер') & (df['total_income'] == 0),'total_income'] = pensioner_income\n",
    "df.loc[(df['income_type'] == 'госслужащий') & (df['total_income'] == 0),'total_income'] = civil_income\n",
    "df.loc[(df['income_type'] == 'предприниматель') & (df['total_income'] == 0),'total_income'] = buisnessman_income\n",
    "#df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Количество детей"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверим столбец с количеством детей. Для начала найдём уникальные значения и их количество."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0     14149\n",
       " 1      4818\n",
       " 2      2055\n",
       " 3       330\n",
       " 20       76\n",
       "-1        47\n",
       " 4        41\n",
       " 5         9\n",
       "Name: children, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['children'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим, что есть отрицательные значения,что невозможно. Возьмем модуль."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['children'] = df['children'].abs()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#рассмотрим людей с \"20 детьми\"\n",
    "df_ch = df[df['children'] == 20]\n",
    "\n",
    "#df_ch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "76 человек с именно 20 детьми - довольно странные данные. Скорее всего проблема в разделите порядков при переносе данных из экселя. Должно было быть 2 ребенка, но при форматировании разделитель исчез. Исправим это."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize_children (children):\n",
    "    if children == 20:\n",
    "        return 2\n",
    "    else:\n",
    "        return children\n",
    "df['children'] = df['children'].apply(normalize_children) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Пол заявителя"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Найдём уникальные значения в столбце *'gender'*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F      14236\n",
       "M       7288\n",
       "XNA        1\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['gender'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XNA  - это что за зверь? Посмотрим."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>10701</td>\n",
       "      <td>0</td>\n",
       "      <td>2358.600502</td>\n",
       "      <td>24</td>\n",
       "      <td>неоконченное высшее</td>\n",
       "      <td>2</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>XNA</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>203905.157261</td>\n",
       "      <td>покупка недвижимости</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years            education  education_id  \\\n",
       "10701         0    2358.600502         24  неоконченное высшее             2   \n",
       "\n",
       "          family_status  family_status_id gender income_type  debt  \\\n",
       "10701  гражданский брак                 1    XNA   компаньон     0   \n",
       "\n",
       "        total_income               purpose  \n",
       "10701  203905.157261  покупка недвижимости  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['gender'] == 'XNA']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Такой человек нашелся только один. Скорее всего это единичная человеческая ошибка,просто не был указан пол при подаче заявки на кредит. Пол так же не является в целом предметом  исследования,тип занятости относится к одному из довольно обширных, и не повлияет на неё, поэтому его можно заменить на более многочисленный вариант - F."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['gender'] == 'XNA', 'gender'] = 'F'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Возраст клиентов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Остался последний не проанализированный столбец *'dob_years'*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "75\n"
     ]
    }
   ],
   "source": [
    "df['dob_years'].sort_values(ascending = False).value_counts()\n",
    "\n",
    "# Можно найти ошибку и так,но если бы значений было бы сильно больше, не факт,что получилось бы. \n",
    "# Посмотрим min и max, как вариант\n",
    "print(df['dob_years'].min())\n",
    "print(df['dob_years'].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Так значительно лучше. \n",
    "\n",
    "Видим,что есть нулевые значения возраста,которых быть в этом столбце в принципе не может. Посмотрим на таблицу с этими значениями:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>0</td>\n",
       "      <td>346541.618895</td>\n",
       "      <td>0</td>\n",
       "      <td>Среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>71291.522491</td>\n",
       "      <td>автомобиль</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>0</td>\n",
       "      <td>2664.273168</td>\n",
       "      <td>0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>в разводе</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>70176.435951</td>\n",
       "      <td>операции с жильем</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>270</td>\n",
       "      <td>3</td>\n",
       "      <td>1872.663186</td>\n",
       "      <td>0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>102166.458894</td>\n",
       "      <td>ремонт жилью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>578</td>\n",
       "      <td>0</td>\n",
       "      <td>397856.565013</td>\n",
       "      <td>0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>97620.687042</td>\n",
       "      <td>строительство собственной недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1040</td>\n",
       "      <td>0</td>\n",
       "      <td>1158.029561</td>\n",
       "      <td>0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>в разводе</td>\n",
       "      <td>3</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>303994.134987</td>\n",
       "      <td>свой автомобиль</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>19829</td>\n",
       "      <td>0</td>\n",
       "      <td>1360.363902</td>\n",
       "      <td>0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>133546.457238</td>\n",
       "      <td>жилье</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20462</td>\n",
       "      <td>0</td>\n",
       "      <td>338734.868540</td>\n",
       "      <td>0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>259193.920299</td>\n",
       "      <td>покупка своего жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20577</td>\n",
       "      <td>0</td>\n",
       "      <td>331741.271455</td>\n",
       "      <td>0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>129788.762899</td>\n",
       "      <td>недвижимость</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21179</td>\n",
       "      <td>2</td>\n",
       "      <td>108.967042</td>\n",
       "      <td>0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>240702.007382</td>\n",
       "      <td>строительство жилой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21313</td>\n",
       "      <td>0</td>\n",
       "      <td>1268.487728</td>\n",
       "      <td>0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>Не женат / не замужем</td>\n",
       "      <td>4</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>152419.184851</td>\n",
       "      <td>операции с жильем</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years education  education_id  \\\n",
       "99            0  346541.618895          0   Среднее             1   \n",
       "149           0    2664.273168          0   среднее             1   \n",
       "270           3    1872.663186          0   среднее             1   \n",
       "578           0  397856.565013          0   среднее             1   \n",
       "1040          0    1158.029561          0    высшее             0   \n",
       "...         ...            ...        ...       ...           ...   \n",
       "19829         0    1360.363902          0   среднее             1   \n",
       "20462         0  338734.868540          0   среднее             1   \n",
       "20577         0  331741.271455          0   среднее             1   \n",
       "21179         2     108.967042          0    высшее             0   \n",
       "21313         0    1268.487728          0   среднее             1   \n",
       "\n",
       "               family_status  family_status_id gender income_type  debt  \\\n",
       "99           женат / замужем                 0      F   пенсионер     0   \n",
       "149                в разводе                 3      F   сотрудник     0   \n",
       "270          женат / замужем                 0      F   сотрудник     0   \n",
       "578          женат / замужем                 0      F   пенсионер     0   \n",
       "1040               в разводе                 3      F   компаньон     0   \n",
       "...                      ...               ...    ...         ...   ...   \n",
       "19829        женат / замужем                 0      F   сотрудник     0   \n",
       "20462        женат / замужем                 0      F   пенсионер     0   \n",
       "20577  Не женат / не замужем                 4      F   пенсионер     0   \n",
       "21179        женат / замужем                 0      M   компаньон     0   \n",
       "21313  Не женат / не замужем                 4      M   сотрудник     0   \n",
       "\n",
       "        total_income                                 purpose  \n",
       "99      71291.522491                              автомобиль  \n",
       "149     70176.435951                       операции с жильем  \n",
       "270    102166.458894                            ремонт жилью  \n",
       "578     97620.687042  строительство собственной недвижимости  \n",
       "1040   303994.134987                         свой автомобиль  \n",
       "...              ...                                     ...  \n",
       "19829  133546.457238                                   жилье  \n",
       "20462  259193.920299                    покупка своего жилья  \n",
       "20577  129788.762899                            недвижимость  \n",
       "21179  240702.007382        строительство жилой недвижимости  \n",
       "21313  152419.184851                       операции с жильем  \n",
       "\n",
       "[101 rows x 12 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['dob_years'] == 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "101 значение. Никакой логической взаимосвязи тут не видно. Думаю, можно попробовать построить диаграмму рассеивания"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(data = df, x = 'dob_years', y ='income_type' ).set(xlabel ='Возраст', ylabel ='Тип занятости');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Из графика видно,что распределение нормальное, за исключением выпадающих точек у сотрудников, пенсионеров, компаньонов и госслужащих, но этих значений минимальное количество. Опять же, заменим их на средний возраст в группе."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Создадим переменные с медианами зарплат по группам\n",
    "employee_age = df[df['income_type'] == 'сотрудник']['dob_years'].mean()\n",
    "companion_age = df[df['income_type'] == 'компаньон']['dob_years'].mean()\n",
    "pensioner_age = df[df['income_type'] == 'пенсионер']['dob_years'].mean()\n",
    "civil_age = df[df['income_type'] == 'госслужащий']['dob_years'].mean()\n",
    "\n",
    "\n",
    "\n",
    "# Заменим пропуски в значениях\n",
    "df.loc[(df['income_type'] == 'сотрудник') & (df['dob_years'] == 0),'dob_years'] = employee_age\n",
    "df.loc[(df['income_type'] == 'компаньон') & (df['dob_years'] == 0),'dob_years'] = companion_age\n",
    "df.loc[(df['income_type'] == 'пенсионер') & (df['dob_years'] == 0),'dob_years'] = pensioner_age\n",
    "df.loc[(df['income_type'] == 'госслужащий') & (df['dob_years'] == 0),'dob_years'] = civil_age\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    21525.00000\n",
       "mean        43.49822\n",
       "std         12.22943\n",
       "min         19.00000\n",
       "25%         34.00000\n",
       "50%         43.00000\n",
       "75%         53.00000\n",
       "max         75.00000\n",
       "Name: dob_years, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Проверка заполнения данных\n",
    "display(df['dob_years'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь, когда данные заполнены, можно начинать обработку дуликатов."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Вывод**\n",
    "\n",
    "Были заполнены отсутствующие значения в различных столбцах:\n",
    "\n",
    "- Значения столбца *'children'* поправлено исходя из ошибки считывания данных;\t\n",
    "- Значения столбца *'days_employed'* заполнено по медиане и приведено в года (ошибка осталась,однако....1100 лет стажа - это сильно)\n",
    "- Столбец *'dob_years'* скорректирован по медиане для групп по типу занятости;\n",
    "- Единичная ошибка в столбце *'gender'* исправлена по наиболее вероятному варианту;\n",
    "- Пропущенные значения столбца *'total_income'* дополнены по медиане дохода внутри группы по типу заработка;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Замена типа данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Есть ещё одна существенная проблема со столбцом *'total_income'*, а именно тип данных данного столбца (*float*). Для дальнейшей более удобной работы, а так же проведения расчётов требуется привести значения данного столбца к целочисленному виду. Из-за большого разряда целой части каждого значения,округление можно приводить простым отбрасыванием дробной части без потери точности результата. Для данного метода подойдёт метод для серий `.astype(int)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['total_income'] = df['total_income'].astype(int)\n",
    "df['days_employed'] = df['days_employed'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Вывод**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Таким образом,все значения приведены к удобному для дальнейшего использования виду."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Обработка дубликатов"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для начала исправим столбец *'education'*, приведя все значения к нижнему регистру."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8437</td>\n",
       "      <td>42.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>253875</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4024</td>\n",
       "      <td>36.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>112080</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5623</td>\n",
       "      <td>33.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>145885</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4124</td>\n",
       "      <td>32.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>267628</td>\n",
       "      <td>дополнительное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>340266</td>\n",
       "      <td>53.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>158616</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>926</td>\n",
       "      <td>27.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>255763</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2879</td>\n",
       "      <td>43.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>240525</td>\n",
       "      <td>операции с жильем</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>50.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>135823</td>\n",
       "      <td>образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6929</td>\n",
       "      <td>35.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>95856</td>\n",
       "      <td>на проведение свадьбы</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2188</td>\n",
       "      <td>41.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>144425</td>\n",
       "      <td>покупка жилья для семьи</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4171</td>\n",
       "      <td>36.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>113943</td>\n",
       "      <td>покупка недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>792</td>\n",
       "      <td>40.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>77069</td>\n",
       "      <td>покупка коммерческой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>360505</td>\n",
       "      <td>65.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>110179</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1846</td>\n",
       "      <td>54.0</td>\n",
       "      <td>неоконченное высшее</td>\n",
       "      <td>2</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>130458</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1844</td>\n",
       "      <td>56.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>1</td>\n",
       "      <td>165127</td>\n",
       "      <td>покупка жилой недвижимости</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    children  days_employed  dob_years            education  education_id  \\\n",
       "0          1           8437       42.0               высшее             0   \n",
       "1          1           4024       36.0              среднее             1   \n",
       "2          0           5623       33.0              среднее             1   \n",
       "3          3           4124       32.0              среднее             1   \n",
       "4          0         340266       53.0              среднее             1   \n",
       "5          0            926       27.0               высшее             0   \n",
       "6          0           2879       43.0               высшее             0   \n",
       "7          0            152       50.0              среднее             1   \n",
       "8          2           6929       35.0               высшее             0   \n",
       "9          0           2188       41.0              среднее             1   \n",
       "10         2           4171       36.0               высшее             0   \n",
       "11         0            792       40.0              среднее             1   \n",
       "12         0         360505       65.0              среднее             1   \n",
       "13         0           1846       54.0  неоконченное высшее             2   \n",
       "14         0           1844       56.0               высшее             0   \n",
       "\n",
       "       family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0    женат / замужем                 0      F   сотрудник     0        253875   \n",
       "1    женат / замужем                 0      F   сотрудник     0        112080   \n",
       "2    женат / замужем                 0      M   сотрудник     0        145885   \n",
       "3    женат / замужем                 0      M   сотрудник     0        267628   \n",
       "4   гражданский брак                 1      F   пенсионер     0        158616   \n",
       "5   гражданский брак                 1      M   компаньон     0        255763   \n",
       "6    женат / замужем                 0      F   компаньон     0        240525   \n",
       "7    женат / замужем                 0      M   сотрудник     0        135823   \n",
       "8   гражданский брак                 1      F   сотрудник     0         95856   \n",
       "9    женат / замужем                 0      M   сотрудник     0        144425   \n",
       "10   женат / замужем                 0      M   компаньон     0        113943   \n",
       "11   женат / замужем                 0      F   сотрудник     0         77069   \n",
       "12  гражданский брак                 1      M   пенсионер     0        110179   \n",
       "13   женат / замужем                 0      F   сотрудник     0        130458   \n",
       "14  гражданский брак                 1      F   компаньон     1        165127   \n",
       "\n",
       "                              purpose  \n",
       "0                       покупка жилья  \n",
       "1             приобретение автомобиля  \n",
       "2                       покупка жилья  \n",
       "3          дополнительное образование  \n",
       "4                     сыграть свадьбу  \n",
       "5                       покупка жилья  \n",
       "6                   операции с жильем  \n",
       "7                         образование  \n",
       "8               на проведение свадьбы  \n",
       "9             покупка жилья для семьи  \n",
       "10               покупка недвижимости  \n",
       "11  покупка коммерческой недвижимости  \n",
       "12                    сыграть свадьбу  \n",
       "13            приобретение автомобиля  \n",
       "14         покупка жилой недвижимости  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['education'] = df['education'].str.lower()\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Далее найдём количество явных дубликатов комбинацией методов `duplicated()` и `sum()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Рассмотрим дубликаты подробнее"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>education_id</th>\n",
       "      <th>family_status</th>\n",
       "      <th>family_status_id</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2849</td>\n",
       "      <td>0</td>\n",
       "      <td>1360</td>\n",
       "      <td>41.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
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       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>133546</td>\n",
       "      <td>покупка жилья для семьи</td>\n",
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       "      <td>3290</td>\n",
       "      <td>0</td>\n",
       "      <td>360505</td>\n",
       "      <td>58.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>110179</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4182</td>\n",
       "      <td>1</td>\n",
       "      <td>1360</td>\n",
       "      <td>34.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>133546</td>\n",
       "      <td>свадьба</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4851</td>\n",
       "      <td>0</td>\n",
       "      <td>360505</td>\n",
       "      <td>60.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>110179</td>\n",
       "      <td>свадьба</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5557</td>\n",
       "      <td>0</td>\n",
       "      <td>360505</td>\n",
       "      <td>58.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>110179</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\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",
       "      <td>20702</td>\n",
       "      <td>0</td>\n",
       "      <td>360505</td>\n",
       "      <td>64.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>110179</td>\n",
       "      <td>дополнительное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21032</td>\n",
       "      <td>0</td>\n",
       "      <td>360505</td>\n",
       "      <td>60.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>110179</td>\n",
       "      <td>заняться образованием</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21132</td>\n",
       "      <td>0</td>\n",
       "      <td>1360</td>\n",
       "      <td>47.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>133546</td>\n",
       "      <td>ремонт жилью</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21281</td>\n",
       "      <td>1</td>\n",
       "      <td>1360</td>\n",
       "      <td>30.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>133546</td>\n",
       "      <td>покупка коммерческой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21415</td>\n",
       "      <td>0</td>\n",
       "      <td>360505</td>\n",
       "      <td>54.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>110179</td>\n",
       "      <td>операции с жильем</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>71 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       children  days_employed  dob_years education  education_id  \\\n",
       "2849          0           1360       41.0   среднее             1   \n",
       "3290          0         360505       58.0   среднее             1   \n",
       "4182          1           1360       34.0    высшее             0   \n",
       "4851          0         360505       60.0   среднее             1   \n",
       "5557          0         360505       58.0   среднее             1   \n",
       "...         ...            ...        ...       ...           ...   \n",
       "20702         0         360505       64.0   среднее             1   \n",
       "21032         0         360505       60.0   среднее             1   \n",
       "21132         0           1360       47.0   среднее             1   \n",
       "21281         1           1360       30.0    высшее             0   \n",
       "21415         0         360505       54.0   среднее             1   \n",
       "\n",
       "          family_status  family_status_id gender income_type  debt  \\\n",
       "2849    женат / замужем                 0      F   сотрудник     0   \n",
       "3290   гражданский брак                 1      F   пенсионер     0   \n",
       "4182   гражданский брак                 1      F   сотрудник     0   \n",
       "4851   гражданский брак                 1      F   пенсионер     0   \n",
       "5557   гражданский брак                 1      F   пенсионер     0   \n",
       "...                 ...               ...    ...         ...   ...   \n",
       "20702   женат / замужем                 0      F   пенсионер     0   \n",
       "21032   женат / замужем                 0      F   пенсионер     0   \n",
       "21132   женат / замужем                 0      F   сотрудник     0   \n",
       "21281   женат / замужем                 0      F   сотрудник     0   \n",
       "21415   женат / замужем                 0      F   пенсионер     0   \n",
       "\n",
       "       total_income                            purpose  \n",
       "2849         133546            покупка жилья для семьи  \n",
       "3290         110179                    сыграть свадьбу  \n",
       "4182         133546                            свадьба  \n",
       "4851         110179                            свадьба  \n",
       "5557         110179                    сыграть свадьбу  \n",
       "...             ...                                ...  \n",
       "20702        110179         дополнительное образование  \n",
       "21032        110179              заняться образованием  \n",
       "21132        133546                       ремонт жилью  \n",
       "21281        133546  покупка коммерческой недвижимости  \n",
       "21415        110179                  операции с жильем  \n",
       "\n",
       "[71 rows x 12 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_dupl = df[df.duplicated() == True]\n",
    "display(df_dupl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видим, что в датафрейме присутствуют реально дулирующиеся значения, удалим дубликаты."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "children            21454\n",
       "days_employed       21454\n",
       "dob_years           21454\n",
       "education           21454\n",
       "education_id        21454\n",
       "family_status       21454\n",
       "family_status_id    21454\n",
       "gender              21454\n",
       "income_type         21454\n",
       "debt                21454\n",
       "total_income        21454\n",
       "purpose             21454\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered = df.drop_duplicates()\n",
    "#а так же проверим,остались ли где-то незаполненные значения:\n",
    "df_filtered.isna().count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Так же можно для большего удобства убрать из  датафрейма столбцы с по сути дублированными значениями, несущими идентичный смысл, предварительно сохранив их в отдельных датафреймах."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "sl_educations = df_filtered[['education','education_id']]\n",
    "sl_family_statuses = df_filtered[['family_status','family_status_id']]\n",
    "#print(sl_educations.head())\n",
    "#print(sl_family_statuses.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>children</th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>family_status</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>8437</td>\n",
       "      <td>42.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>253875</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4024</td>\n",
       "      <td>36.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>112080</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5623</td>\n",
       "      <td>33.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>145885</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4124</td>\n",
       "      <td>32.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>267628</td>\n",
       "      <td>дополнительное образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>340266</td>\n",
       "      <td>53.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>158616</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>926</td>\n",
       "      <td>27.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>255763</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2879</td>\n",
       "      <td>43.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>240525</td>\n",
       "      <td>операции с жильем</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>50.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>135823</td>\n",
       "      <td>образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6929</td>\n",
       "      <td>35.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>95856</td>\n",
       "      <td>на проведение свадьбы</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2188</td>\n",
       "      <td>41.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>144425</td>\n",
       "      <td>покупка жилья для семьи</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   children  days_employed  dob_years education     family_status gender  \\\n",
       "0         1           8437       42.0    высшее   женат / замужем      F   \n",
       "1         1           4024       36.0   среднее   женат / замужем      F   \n",
       "2         0           5623       33.0   среднее   женат / замужем      M   \n",
       "3         3           4124       32.0   среднее   женат / замужем      M   \n",
       "4         0         340266       53.0   среднее  гражданский брак      F   \n",
       "5         0            926       27.0    высшее  гражданский брак      M   \n",
       "6         0           2879       43.0    высшее   женат / замужем      F   \n",
       "7         0            152       50.0   среднее   женат / замужем      M   \n",
       "8         2           6929       35.0    высшее  гражданский брак      F   \n",
       "9         0           2188       41.0   среднее   женат / замужем      M   \n",
       "\n",
       "  income_type  debt  total_income                     purpose  \n",
       "0   сотрудник     0        253875               покупка жилья  \n",
       "1   сотрудник     0        112080     приобретение автомобиля  \n",
       "2   сотрудник     0        145885               покупка жилья  \n",
       "3   сотрудник     0        267628  дополнительное образование  \n",
       "4   пенсионер     0        158616             сыграть свадьбу  \n",
       "5   компаньон     0        255763               покупка жилья  \n",
       "6   компаньон     0        240525           операции с жильем  \n",
       "7   сотрудник     0        135823                 образование  \n",
       "8   сотрудник     0         95856       на проведение свадьбы  \n",
       "9   сотрудник     0        144425     покупка жилья для семьи  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered = df_filtered.drop(['education_id','family_status_id'],1)\n",
    "#df_filtered = df_filtered.drop('family_status', 1)\n",
    "df_filtered.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Вывод**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пробелов в данных не осталось,все стобцы одинаковой длины,нулевых значений нет и мы избавились от дубликатов. Скорее всего дубликаты могли появиться при повторных обращениях клиента в банк в другом возрасте. Однако остаётся ещё столбец *'purpose'* в котором присутствует много однотипных запросов, написанных разных способом. Это можно увидеть методом `unique()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['покупка жилья', 'приобретение автомобиля',\n",
       "       'дополнительное образование', 'сыграть свадьбу',\n",
       "       'операции с жильем', 'образование', 'на проведение свадьбы',\n",
       "       'покупка жилья для семьи', 'покупка недвижимости',\n",
       "       'покупка коммерческой недвижимости', 'покупка жилой недвижимости',\n",
       "       'строительство собственной недвижимости', 'недвижимость',\n",
       "       'строительство недвижимости', 'на покупку подержанного автомобиля',\n",
       "       'на покупку своего автомобиля',\n",
       "       'операции с коммерческой недвижимостью',\n",
       "       'строительство жилой недвижимости', 'жилье',\n",
       "       'операции со своей недвижимостью', 'автомобили',\n",
       "       'заняться образованием', 'сделка с подержанным автомобилем',\n",
       "       'получение образования', 'автомобиль', 'свадьба',\n",
       "       'получение дополнительного образования', 'покупка своего жилья',\n",
       "       'операции с недвижимостью', 'получение высшего образования',\n",
       "       'свой автомобиль', 'сделка с автомобилем',\n",
       "       'профильное образование', 'высшее образование',\n",
       "       'покупка жилья для сдачи', 'на покупку автомобиля', 'ремонт жилью',\n",
       "       'заняться высшим образованием'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "purposes = df_filtered['purpose'].unique()\n",
    "display(purposes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Лемматизация и категоризация "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проще всего будет исправить данные в этом столбце используя лемматизацию с использованием функции для создания нового столбца измененных значений, имеющих одинаковый смысл для того, чтобы можно было проводить дальнейшие исследования. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'автомобиль': 2, 'покупка   жилье': 1, 'приобретение   автомобиль': 1, 'дополнительный   образование': 1, 'сыграть   свадьба': 1, 'операция   с   жилье': 1, 'образование': 1, 'на   проведение   свадьба': 1, 'покупка   жилье   для   семья': 1, 'покупка   недвижимость': 1, 'покупка   коммерческий   недвижимость': 1, 'покупка   жилой   недвижимость': 1, 'строительство   собственный   недвижимость': 1, 'недвижимость': 1, 'строительство   недвижимость': 1, 'на   покупка   подержать   автомобиль': 1, 'на   покупка   свой   автомобиль': 1, 'операция   с   коммерческий   недвижимость': 1, 'строительство   жилой   недвижимость': 1, 'жилье': 1, 'операция   со   свой   недвижимость': 1, 'заниматься   образование': 1, 'сделка   с   подержанный   автомобиль': 1, 'получение   образование': 1, 'свадьба': 1, 'получение   дополнительный   образование': 1, 'покупка   свой   жилье': 1, 'операция   с   недвижимость': 1, 'получение   высокий   образование': 1, 'свой   автомобиль': 1, 'сделка   с   автомобиль': 1, 'профильный   образование': 1, 'высокий   образование': 1, 'покупка   жилье   для   сдача': 1, 'на   покупка   автомобиль': 1, 'ремонт   жилье': 1, 'заниматься   высокий   образование': 1})\n"
     ]
    }
   ],
   "source": [
    "m = Mystem() \n",
    "\n",
    "#Напишем цикл, который пройдет по всему столбцу 'purpose'  и посчитает уникальные значения\n",
    "lemmas_list =[]\n",
    "for purpose in purposes:\n",
    "    lemmas = ' '.join(m.lemmatize(purpose)).strip()\n",
    "    lemmas_list.append(lemmas)\n",
    "print(Counter(lemmas_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как мы видим на основе полученных данных, можно сделать категоризацию именно по данному столбцу."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чаще всего упоминаются категории \"авто\", \"недвижимость\" или \"жилье\", \"свадьба\" и \"образование\". Попробуем сделать функцию для обработки по этим ключевым словам. Если что-то упустили, отработает дополнительное условие и выведет что значение не подошло ни к одному из перечисленных вариантов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0</td>\n",
       "      <td>340266</td>\n",
       "      <td>53.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>158616</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>926</td>\n",
       "      <td>27.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>255763</td>\n",
       "      <td>покупка жилья</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2879</td>\n",
       "      <td>43.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>240525</td>\n",
       "      <td>операции с жильем</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>152</td>\n",
       "      <td>50.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>135823</td>\n",
       "      <td>образование</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6929</td>\n",
       "      <td>35.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>95856</td>\n",
       "      <td>на проведение свадьбы</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2188</td>\n",
       "      <td>41.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>144425</td>\n",
       "      <td>покупка жилья для семьи</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4171</td>\n",
       "      <td>36.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>113943</td>\n",
       "      <td>покупка недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>792</td>\n",
       "      <td>40.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>77069</td>\n",
       "      <td>покупка коммерческой недвижимости</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>360505</td>\n",
       "      <td>65.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>1</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>M</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>110179</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1846</td>\n",
       "      <td>54.0</td>\n",
       "      <td>неоконченное высшее</td>\n",
       "      <td>2</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>0</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>130458</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>1844</td>\n",
       "      <td>56.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>0</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>1</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>1</td>\n",
       "      <td>165127</td>\n",
       "      <td>покупка жилой недвижимости</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    children  days_employed  dob_years            education  education_id  \\\n",
       "0          1           8437       42.0               высшее             0   \n",
       "1          1           4024       36.0              среднее             1   \n",
       "2          0           5623       33.0              среднее             1   \n",
       "3          3           4124       32.0              среднее             1   \n",
       "4          0         340266       53.0              среднее             1   \n",
       "5          0            926       27.0               высшее             0   \n",
       "6          0           2879       43.0               высшее             0   \n",
       "7          0            152       50.0              среднее             1   \n",
       "8          2           6929       35.0               высшее             0   \n",
       "9          0           2188       41.0              среднее             1   \n",
       "10         2           4171       36.0               высшее             0   \n",
       "11         0            792       40.0              среднее             1   \n",
       "12         0         360505       65.0              среднее             1   \n",
       "13         0           1846       54.0  неоконченное высшее             2   \n",
       "14         0           1844       56.0               высшее             0   \n",
       "\n",
       "       family_status  family_status_id gender income_type  debt  total_income  \\\n",
       "0    женат / замужем                 0      F   сотрудник     0        253875   \n",
       "1    женат / замужем                 0      F   сотрудник     0        112080   \n",
       "2    женат / замужем                 0      M   сотрудник     0        145885   \n",
       "3    женат / замужем                 0      M   сотрудник     0        267628   \n",
       "4   гражданский брак                 1      F   пенсионер     0        158616   \n",
       "5   гражданский брак                 1      M   компаньон     0        255763   \n",
       "6    женат / замужем                 0      F   компаньон     0        240525   \n",
       "7    женат / замужем                 0      M   сотрудник     0        135823   \n",
       "8   гражданский брак                 1      F   сотрудник     0         95856   \n",
       "9    женат / замужем                 0      M   сотрудник     0        144425   \n",
       "10   женат / замужем                 0      M   компаньон     0        113943   \n",
       "11   женат / замужем                 0      F   сотрудник     0         77069   \n",
       "12  гражданский брак                 1      M   пенсионер     0        110179   \n",
       "13   женат / замужем                 0      F   сотрудник     0        130458   \n",
       "14  гражданский брак                 1      F   компаньон     1        165127   \n",
       "\n",
       "                              purpose  \n",
       "0                       покупка жилья  \n",
       "1             приобретение автомобиля  \n",
       "2                       покупка жилья  \n",
       "3          дополнительное образование  \n",
       "4                     сыграть свадьбу  \n",
       "5                       покупка жилья  \n",
       "6                   операции с жильем  \n",
       "7                         образование  \n",
       "8               на проведение свадьбы  \n",
       "9             покупка жилья для семьи  \n",
       "10               покупка недвижимости  \n",
       "11  покупка коммерческой недвижимости  \n",
       "12                    сыграть свадьбу  \n",
       "13            приобретение автомобиля  \n",
       "14         покупка жилой недвижимости  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Cоставим функцию на вход будет подаваться строка из столбца 'purpose'.\n",
    "#цикл последовательно выполняет проверку на соответствие строки заданным значениям \n",
    "#и возвращает одну из выбранных строк-категорий.\n",
    "\n",
    "def category_purpose(row):\n",
    "    lemm_purpose = m.lemmatize(row['purpose'])\n",
    "    if 'автомобиль' in lemm_purpose:\n",
    "        return 'машина'\n",
    "    if ('жилье' in lemm_purpose) or ('недвижимость' in lemm_purpose):\n",
    "        return 'недвижимость'\n",
    "    if 'образование' in lemm_purpose:\n",
    "        return 'образование'\n",
    "    if 'свадьба' in lemm_purpose: \n",
    "        return 'свадьба'\n",
    "    else: \n",
    "        return not_mentioned\n",
    "df_filtered['purpose_category'] = df_filtered.apply(category_purpose, axis = 1)\n",
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['недвижимость', 'машина', 'образование', 'свадьба'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Заодно проверим, не осталось ли необработанных значений, которые не были лемматизированы\n",
    "\n",
    "display(df_filtered['purpose_category'].unique())\n",
    "#и уберем старый столбец с целями кредита\n",
    "df = df.drop(['purpose'],1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Вывод**\n",
    "\n",
    "Как мы видим,все замены осуществлены корректно,ошибок в выполнении кода не выявлено. Основными целями для получения кредита, таким образом,являются недвижимость, свадьба, автомобиль и образование какой-то значимой корреляции между целью кредита и другими данными выявить проблематично,но для рассчёта скоринга этого уже вполне достаточно. По этим данным мы и будем в дельнейшем категоризировать данный датафрейм."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Категоризация данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сначала посчитаем, сколько клиентов, желающих взять кредит в каждой из групп, сформированных по цели кредита, имеют задолженность."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "purpose_category\n",
       "недвижимость    782\n",
       "машина          403\n",
       "образование     370\n",
       "свадьба         186\n",
       "Name: debt, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Узнаем, сколько в каждой из групп людей с задолженностями по кредитам\n",
    "\n",
    "df_filtered[df_filtered['debt'] == 1].groupby('purpose_category')['debt'].count().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>days_employed</th>\n",
       "      <th>dob_years</th>\n",
       "      <th>education</th>\n",
       "      <th>family_status</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>total_income</th>\n",
       "      <th>purpose</th>\n",
       "      <th>purpose_category</th>\n",
       "      <th>debt_status</th>\n",
       "      <th>children_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>8437</td>\n",
       "      <td>42.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>253875</td>\n",
       "      <td>покупка жилья</td>\n",
       "      <td>недвижимость</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>есть дети</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4024</td>\n",
       "      <td>36.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>112080</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "      <td>машина</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>есть дети</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5623</td>\n",
       "      <td>33.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>145885</td>\n",
       "      <td>покупка жилья</td>\n",
       "      <td>недвижимость</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>детей нет</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4124</td>\n",
       "      <td>32.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>267628</td>\n",
       "      <td>дополнительное образование</td>\n",
       "      <td>образование</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>есть дети</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>340266</td>\n",
       "      <td>53.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>158616</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "      <td>свадьба</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>детей нет</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>926</td>\n",
       "      <td>27.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>255763</td>\n",
       "      <td>покупка жилья</td>\n",
       "      <td>недвижимость</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>детей нет</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>2879</td>\n",
       "      <td>43.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>240525</td>\n",
       "      <td>операции с жильем</td>\n",
       "      <td>недвижимость</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>детей нет</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>152</td>\n",
       "      <td>50.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>135823</td>\n",
       "      <td>образование</td>\n",
       "      <td>образование</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>детей нет</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>6929</td>\n",
       "      <td>35.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>95856</td>\n",
       "      <td>на проведение свадьбы</td>\n",
       "      <td>свадьба</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>есть дети</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>2188</td>\n",
       "      <td>41.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>144425</td>\n",
       "      <td>покупка жилья для семьи</td>\n",
       "      <td>недвижимость</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>детей нет</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>4171</td>\n",
       "      <td>36.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>M</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>0</td>\n",
       "      <td>113943</td>\n",
       "      <td>покупка недвижимости</td>\n",
       "      <td>недвижимость</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>есть дети</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>792</td>\n",
       "      <td>40.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>77069</td>\n",
       "      <td>покупка коммерческой недвижимости</td>\n",
       "      <td>недвижимость</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>детей нет</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>360505</td>\n",
       "      <td>65.0</td>\n",
       "      <td>среднее</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>M</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>110179</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "      <td>свадьба</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>детей нет</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>1846</td>\n",
       "      <td>54.0</td>\n",
       "      <td>неоконченное высшее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>130458</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "      <td>машина</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>детей нет</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>1844</td>\n",
       "      <td>56.0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>F</td>\n",
       "      <td>компаньон</td>\n",
       "      <td>1</td>\n",
       "      <td>165127</td>\n",
       "      <td>покупка жилой недвижимости</td>\n",
       "      <td>недвижимость</td>\n",
       "      <td>задолженность</td>\n",
       "      <td>детей нет</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    days_employed  dob_years            education     family_status gender  \\\n",
       "0            8437       42.0               высшее   женат / замужем      F   \n",
       "1            4024       36.0              среднее   женат / замужем      F   \n",
       "2            5623       33.0              среднее   женат / замужем      M   \n",
       "3            4124       32.0              среднее   женат / замужем      M   \n",
       "4          340266       53.0              среднее  гражданский брак      F   \n",
       "5             926       27.0               высшее  гражданский брак      M   \n",
       "6            2879       43.0               высшее   женат / замужем      F   \n",
       "7             152       50.0              среднее   женат / замужем      M   \n",
       "8            6929       35.0               высшее  гражданский брак      F   \n",
       "9            2188       41.0              среднее   женат / замужем      M   \n",
       "10           4171       36.0               высшее   женат / замужем      M   \n",
       "11            792       40.0              среднее   женат / замужем      F   \n",
       "12         360505       65.0              среднее  гражданский брак      M   \n",
       "13           1846       54.0  неоконченное высшее   женат / замужем      F   \n",
       "14           1844       56.0               высшее  гражданский брак      F   \n",
       "\n",
       "   income_type  debt  total_income                            purpose  \\\n",
       "0    сотрудник     0        253875                      покупка жилья   \n",
       "1    сотрудник     0        112080            приобретение автомобиля   \n",
       "2    сотрудник     0        145885                      покупка жилья   \n",
       "3    сотрудник     0        267628         дополнительное образование   \n",
       "4    пенсионер     0        158616                    сыграть свадьбу   \n",
       "5    компаньон     0        255763                      покупка жилья   \n",
       "6    компаньон     0        240525                  операции с жильем   \n",
       "7    сотрудник     0        135823                        образование   \n",
       "8    сотрудник     0         95856              на проведение свадьбы   \n",
       "9    сотрудник     0        144425            покупка жилья для семьи   \n",
       "10   компаньон     0        113943               покупка недвижимости   \n",
       "11   сотрудник     0         77069  покупка коммерческой недвижимости   \n",
       "12   пенсионер     0        110179                    сыграть свадьбу   \n",
       "13   сотрудник     0        130458            приобретение автомобиля   \n",
       "14   компаньон     1        165127         покупка жилой недвижимости   \n",
       "\n",
       "   purpose_category        debt_status children_status  \n",
       "0      недвижимость  нет задолженности       есть дети  \n",
       "1            машина  нет задолженности       есть дети  \n",
       "2      недвижимость  нет задолженности       детей нет  \n",
       "3       образование  нет задолженности       есть дети  \n",
       "4           свадьба  нет задолженности       детей нет  \n",
       "5      недвижимость  нет задолженности       детей нет  \n",
       "6      недвижимость  нет задолженности       детей нет  \n",
       "7       образование  нет задолженности       детей нет  \n",
       "8           свадьба  нет задолженности       есть дети  \n",
       "9      недвижимость  нет задолженности       детей нет  \n",
       "10     недвижимость  нет задолженности       есть дети  \n",
       "11     недвижимость  нет задолженности       детей нет  \n",
       "12          свадьба  нет задолженности       детей нет  \n",
       "13           машина  нет задолженности       детей нет  \n",
       "14     недвижимость      задолженность       детей нет  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#создадим фукцию,которая создаст новую серию на основе столбца 'debt'\n",
    "def debt_status(debt):\n",
    "    if debt == 0:\n",
    "        return 'нет задолженности'\n",
    "    if debt == 1:\n",
    "        return 'задолженность'   \n",
    "    #применим функцию и удалим столбец 'debt'\n",
    "df_filtered['debt_status'] = df_filtered['debt'].apply(debt_status)\n",
    "\n",
    "\n",
    "def children_status(children):\n",
    "    if children > 0:\n",
    "        return 'есть дети'\n",
    "    else:\n",
    "        return 'детей нет'   \n",
    "    #применим функцию и удалим столбцы 'debt' и 'children'\n",
    "df_filtered ['children_status'] = df_filtered['children'].apply(children_status)\n",
    "#df_filtered.drop(['debt'],axis = 1, inplace = True)\n",
    "df_filtered.drop(['children'],axis = 1, inplace = True)\n",
    "df_filtered.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Так же для того чтобы в дальнейшем сделать вывод о зависимости между уровнем дохода и возвращением кредита в срок, следует сначала определить уровни доходов клиентов. Для этого можно воспользоваться категориями [Росстата](https://zavtra.ru/blogs/eto_interesno_i_polezno_znat_),согласно которому население делится по доходам на 5 классов:\n",
    "\n",
    "1) Нищие – душевой доход **до 1** ПМ/чел;\n",
    "\n",
    "2) Бедные - душевой доход **от 1 до 3** ПМ/чел;\n",
    "\n",
    "3) Выше бедности - доход **от 3 до 7** ПМ/чел;\n",
    "\n",
    "4) Средний класс – доход **от 7 до 11** ПМ/чел;\n",
    "\n",
    "5) Высший класс (богатые и сверхбогатые) – доход **более 11** ПМ/чел.\n",
    "\n",
    "Где **ПМ** - величина прожиточного минимума, которая на текущий год составляет [18 029 рублей](https://www.interfax.ru/moscow/797225)\n",
    "\n",
    "Таким образом составим новый столбец исходя из данных о доходе клиентов и статистической информации."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "pm = 18029\n",
    "#создадим фукцию,которая создаст новую серию на основе столбца 'total_income'\n",
    "def income_status(row):\n",
    "    if row['total_income'] < pm:\n",
    "        return 'нищий'\n",
    "    if pm <= row['total_income'] < 3*pm  :\n",
    "        return 'бедный'\n",
    "    if 3*pm <= row['total_income'] < 7*pm  :\n",
    "        return 'ниже среднего'\n",
    "    if 7*pm <= row['total_income'] < 11*pm  :\n",
    "        return 'средний'\n",
    "    if row['total_income'] >= 11*pm  :\n",
    "        return 'высокий достаток'\n",
    "    #применим функцию и удалим столбец 'total_income'\n",
    "df_filtered ['income_status'] = df_filtered.apply(income_status, axis = 1)\n",
    "df_filtered.drop(['total_income'],axis = 1, inplace = True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Так же для улучшения читаемости отбросим столбцы *'days_employed'* и *'dob_years'*, сохранив их в отдельную переменную, если вдруг где-то понадобится."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>education</th>\n",
       "      <th>family_status</th>\n",
       "      <th>gender</th>\n",
       "      <th>income_type</th>\n",
       "      <th>debt</th>\n",
       "      <th>purpose</th>\n",
       "      <th>purpose_category</th>\n",
       "      <th>debt_status</th>\n",
       "      <th>children_status</th>\n",
       "      <th>income_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>высшее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>покупка жилья</td>\n",
       "      <td>недвижимость</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>есть дети</td>\n",
       "      <td>высокий достаток</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>среднее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>F</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>приобретение автомобиля</td>\n",
       "      <td>машина</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>есть дети</td>\n",
       "      <td>ниже среднего</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>среднее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>покупка жилья</td>\n",
       "      <td>недвижимость</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>детей нет</td>\n",
       "      <td>средний</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>среднее</td>\n",
       "      <td>женат / замужем</td>\n",
       "      <td>M</td>\n",
       "      <td>сотрудник</td>\n",
       "      <td>0</td>\n",
       "      <td>дополнительное образование</td>\n",
       "      <td>образование</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>есть дети</td>\n",
       "      <td>высокий достаток</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>среднее</td>\n",
       "      <td>гражданский брак</td>\n",
       "      <td>F</td>\n",
       "      <td>пенсионер</td>\n",
       "      <td>0</td>\n",
       "      <td>сыграть свадьбу</td>\n",
       "      <td>свадьба</td>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>детей нет</td>\n",
       "      <td>средний</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  education     family_status gender income_type  debt  \\\n",
       "0    высшее   женат / замужем      F   сотрудник     0   \n",
       "1   среднее   женат / замужем      F   сотрудник     0   \n",
       "2   среднее   женат / замужем      M   сотрудник     0   \n",
       "3   среднее   женат / замужем      M   сотрудник     0   \n",
       "4   среднее  гражданский брак      F   пенсионер     0   \n",
       "\n",
       "                      purpose purpose_category        debt_status  \\\n",
       "0               покупка жилья     недвижимость  нет задолженности   \n",
       "1     приобретение автомобиля           машина  нет задолженности   \n",
       "2               покупка жилья     недвижимость  нет задолженности   \n",
       "3  дополнительное образование      образование  нет задолженности   \n",
       "4             сыграть свадьбу          свадьба  нет задолженности   \n",
       "\n",
       "  children_status     income_status  \n",
       "0       есть дети  высокий достаток  \n",
       "1       есть дети     ниже среднего  \n",
       "2       детей нет           средний  \n",
       "3       есть дети  высокий достаток  \n",
       "4       детей нет           средний  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_time = df_filtered[['days_employed','dob_years']]\n",
    "df_filtered.drop(['days_employed'],axis = 1, inplace = True)\n",
    "df_filtered.drop(['dob_years'],axis = 1, inplace = True)\n",
    "# Посмотрим, что получилось\n",
    "df_filtered.head()\n",
    "#job_time.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Вывод**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Мы лемматизировали столбец целей кредита, определили нужные и добавили в датафрейм в виде отдельного столбца;\n",
    "- Создали категоризацию по уровню дохода, разделив людей на 5 групп;\n",
    "- Касательно семейного статуса, считаю, что дополнительная категоризация не нужна, эта представляется наиболее диверсифицированной и при этом точной. Можно было бы объединить категории женат/замужем и гражданский брак, а так же не женат/не замужем и вдова/вдовец. Но замужние и женатые могут быть больше связаны юридически, например другими кредитами (супруг или супруга заявителя), которые по законодательству будут являться родственниками,обязанными выплачивать кредит. В то время как гражданский брак не имеет такой юридической подоплёки и кредитная ситуация сожителя может быть не отражена.  Так же сложно судить о прямой взаимосвязи людей неженатых/незамужних и вдов/вдовцов, хотя логически они ближе всего друг к другу. Не объединяю их с разведенными, так как у разведенных людей могут быть алименты, дети как рожденные в семье(которые могут иметь долю в наследстве и при разделе имущества), так и вне семьи (что может не отражаться в заявлении). Слишком много различных нюансов,которые могут прямо или косвенно влиять на метрику, из-за этого полагаю, пусть лучше остаются в разных категориях;\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 3. Ответьте на вопросы"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Есть ли зависимость между наличием детей и возвратом кредита в срок?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>children_status</th>\n",
       "      <th>детей нет</th>\n",
       "      <th>есть дети</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debt_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>13028</td>\n",
       "      <td>6685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>задолженность</td>\n",
       "      <td>1063</td>\n",
       "      <td>678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>доля</td>\n",
       "      <td>7.54%</td>\n",
       "      <td>9.21%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "children_status   детей нет есть дети\n",
       "debt_status                          \n",
       "нет задолженности     13028      6685\n",
       "задолженность          1063       678\n",
       "доля                  7.54%     9.21%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_children = df_filtered.pivot_table(\n",
    "index = 'debt_status',\n",
    "columns = ['children_status'],\n",
    "values = 'debt',\n",
    "aggfunc = 'count')\n",
    "pcnt = '{:.2%}'.format\n",
    "table_children.loc['доля'] = (table_children.iloc[0]/(table_children.iloc[1]+table_children.iloc[0])).map(pcnt)\n",
    "display(table_children.sort_index(ascending = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  **Промежуточный вывод**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Исходя из полученной таблицы,можно сделать следующие выводы:\n",
    "- Клиенты, у которых есть дети, в два раза реже берут кредиты;\n",
    "- Клиенты с детьми чаще  имеют задолженность по кредитам.\n",
    "\n",
    "Первый факт может быть обоснован тем, что содержание ребенка реже позволяет взять на себя дополнительную статью расходов в виде оплаты кредита.\n",
    "С другой стороны, семейные люди, у которых есть дети, являются менее надежными и платежеспособными клиентами, поскольку больше подвержены рискам,связанным с вынужденными незапланированными тратами на детей."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Есть ли зависимость между семейным положением и возвратом кредита в срок?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>family_status</th>\n",
       "      <th>Не женат / не замужем</th>\n",
       "      <th>в разводе</th>\n",
       "      <th>вдовец / вдова</th>\n",
       "      <th>гражданский брак</th>\n",
       "      <th>женат / замужем</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debt_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>2536</td>\n",
       "      <td>1110</td>\n",
       "      <td>896</td>\n",
       "      <td>3763</td>\n",
       "      <td>11408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>задолженность</td>\n",
       "      <td>274</td>\n",
       "      <td>85</td>\n",
       "      <td>63</td>\n",
       "      <td>388</td>\n",
       "      <td>931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>доля</td>\n",
       "      <td>9.75%</td>\n",
       "      <td>7.11%</td>\n",
       "      <td>6.57%</td>\n",
       "      <td>9.35%</td>\n",
       "      <td>7.55%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "family_status     Не женат / не замужем в разводе вдовец / вдова  \\\n",
       "debt_status                                                        \n",
       "нет задолженности                  2536      1110            896   \n",
       "задолженность                       274        85             63   \n",
       "доля                              9.75%     7.11%          6.57%   \n",
       "\n",
       "family_status     гражданский брак женат / замужем  \n",
       "debt_status                                         \n",
       "нет задолженности             3763           11408  \n",
       "задолженность                  388             931  \n",
       "доля                         9.35%           7.55%  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_family = df_filtered.pivot_table(\n",
    "index = 'debt_status',\n",
    "columns = ['family_status'],\n",
    "values = 'debt',\n",
    "aggfunc = 'count')\n",
    "pcnt = '{:.2%}'.format\n",
    "table_family.loc['доля'] = (table_family.iloc[0]/(table_family.iloc[1]+table_family.iloc[0])).map(pcnt)\n",
    "display(table_family.sort_index(ascending = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Промежуточный вывод**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Из данной таблицы можно выявить следующее:\n",
    "- Чаще всего за кредитом обращаются *замужние и женатые* клиенты, а реже всего - *овдовевшие и люди в разводе*, последние к тому же имеют самую низкую долю невозвратов по кредиту;\n",
    "- Доля людей, имеющих задолженность по кредитам выше в группе *не женат / не замужем* и проживающих в *гражданском браке*;\n",
    "- Посередине между этими двумя группами находятся люди в группе *женат / замужем*,из них кредитную задолженность имеют 7,5%.\n",
    "\n",
    "На самом деле если бы мы получили больше данных по группам *в разводе* и *вдовец / вдова*, результаты могли бы отличаться, так как их количество, например, в десять раз ниже количества замужних. Тогда выборка была бы более репрезентативна.\n",
    "Тем не менее, процент невозвратов кредита вовремя **ниже у двух групп:  в разводе, вдовец / вдова**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Есть ли зависимость между уровнем дохода и возвратом кредита в срок?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь можно непосредственно посмотреть, как влияет уровень дохода на платежеспособность."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>income_status</th>\n",
       "      <th>бедный</th>\n",
       "      <th>высокий достаток</th>\n",
       "      <th>ниже среднего</th>\n",
       "      <th>нищий</th>\n",
       "      <th>средний</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debt_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>496</td>\n",
       "      <td>4811</td>\n",
       "      <td>6729</td>\n",
       "      <td>139</td>\n",
       "      <td>7538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>задолженность</td>\n",
       "      <td>34</td>\n",
       "      <td>365</td>\n",
       "      <td>622</td>\n",
       "      <td>7</td>\n",
       "      <td>713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>доля</td>\n",
       "      <td>6.42%</td>\n",
       "      <td>7.05%</td>\n",
       "      <td>8.46%</td>\n",
       "      <td>4.79%</td>\n",
       "      <td>8.64%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "income_status     бедный высокий достаток ниже среднего  нищий средний\n",
       "debt_status                                                           \n",
       "нет задолженности    496             4811          6729    139    7538\n",
       "задолженность         34              365           622      7     713\n",
       "доля               6.42%            7.05%         8.46%  4.79%   8.64%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_income = df_filtered.pivot_table(\n",
    "index = 'debt_status',\n",
    "columns = ['income_status'],\n",
    "values = 'debt',\n",
    "aggfunc = 'count')\n",
    "pcnt = '{:.2%}'.format\n",
    "table_income.loc['доля'] = (table_income.iloc[0]/(table_income.iloc[1]+table_income.iloc[0])).map(pcnt)\n",
    "display(table_income.sort_index(ascending = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Промежуточный вывод**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Люди, которые находятся в тяжелом материальном положении, имеют при этом более низкий процент невозвратов;\n",
    "- Довольно низкий процент невозвратов так же у людей с высоким достатком;\n",
    "- Люди, имеющие средний (и немного ниже среднего) достаток, имеют больше задолженностей по кредитам;\n",
    "- Чаще всего берут кредит люди со средним достатком или достатком ниже среднего;\n",
    "\n",
    "Низкий процент невозвратов у людей с низким достатком может быть связан с тем, что суммы могут браться \"до зарплаты\" и возможно чаще суммы кредитов меньше. Так же стоить заметить, что их доля среди всех заемщиков крайне мала."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Как разные цели кредита влияют на его возврат в срок?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>purpose_category</th>\n",
       "      <th>машина</th>\n",
       "      <th>недвижимость</th>\n",
       "      <th>образование</th>\n",
       "      <th>свадьба</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>debt_status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>нет задолженности</td>\n",
       "      <td>3903</td>\n",
       "      <td>10029</td>\n",
       "      <td>3643</td>\n",
       "      <td>2138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>задолженность</td>\n",
       "      <td>403</td>\n",
       "      <td>782</td>\n",
       "      <td>370</td>\n",
       "      <td>186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>доля</td>\n",
       "      <td>9.36%</td>\n",
       "      <td>7.23%</td>\n",
       "      <td>9.22%</td>\n",
       "      <td>8.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "purpose_category  машина недвижимость образование свадьба\n",
       "debt_status                                              \n",
       "нет задолженности   3903        10029        3643    2138\n",
       "задолженность        403          782         370     186\n",
       "доля               9.36%        7.23%       9.22%   8.00%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table_purpose = df_filtered.pivot_table(\n",
    "index = 'debt_status',\n",
    "columns = ['purpose_category'],\n",
    "values = 'debt',\n",
    "aggfunc = 'count')\n",
    "pcnt = '{:.2%}'.format\n",
    "table_purpose.loc['доля'] = (table_purpose.iloc[0]/(table_purpose.iloc[1]+table_purpose.iloc[0])).map(pcnt)\n",
    "display(table_purpose.sort_index(ascending = False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **Промежуточный вывод**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Самые низкие проценты по категориям целей кредитов являются *покупка недвижимости* и *свадьба*\n",
    "- Кредиты,которые были взяты для *покупки автомобиля* и на *образование* клиенты возвращают хуже\n",
    "\n",
    "Это может быть связано с тем, что кредиты, взятые для проведения свадьбы могут возвращаться лучше из-за подарков деньгами молодоженам, которыми и закрывается основная сумма кредита или кредит полностью.\n",
    "Чаще всего кредит для покупки недвижимости берут люди,уверенные в своем финансовом положении,так что чаще их материальное состояние является более стабильным. Так же многие покупают недвижимость для дальнейшей сдачи в аренду и могут оплачивать кредит деньгами с ренты. К тому же, другая часть часто уже въезжает в купленную недвижимость,если она взята например на вторичном рынке и поскольку сделка формально проводится через банк,до выплаты кредита банк часто является собственником до выплаты кредита и в случае неуплаты квартира может быть изъята банком для погашения кредита и пени за просрочку платежа и процентов.\n",
    "Покупка автомобиля, как потенциально опасного средства перемещения, может быть омрачена высоким риском попадания человека в [ДТП](http://stat.gibdd.ru/) и при отсутствии необязательной страховки, оплата ремонта машин участников ДТП может лечь на человека,взявшего кредит на машину, что будет негативно сказываться на его платежеспособности. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Шаг 4. Общий вывод"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В целом все проверенные факторы являются относительно релевантыми для проверки человека, который хочет получить кредит в большей или меньшей степени.\n",
    "Категориально стоит обратить особое внимание на достаток, семейное положение и наличие детей.\n",
    "Порядок влияния всех исследованных факторов находится примерно на одном уровне.\n",
    "\n",
    "### Наиболее надежные клиенты по группам\n",
    "- Самые надежные в группе по *наличию детей* являются клиенты **без детей**;\n",
    "- Самыми надежными в группе по *семейному положению* являются люди **в разводе** и **вдовцы / вдовы**;\n",
    "- В группе по *уровню достатка* - люди **с низким доходом,бедные, нищие** и **богатые**;\n",
    "- Самые надежные *цели* для кредита - **покупка недвижимости** и **свадьба**.\n",
    "\n",
    "### Наименее надежные клиенты по группам\n",
    "- Самыми ненадежными в группе по *наличию детей* являются клиенты **с детьми**;\n",
    "- Самыми надежными в группе по *семейному положению* являются люди **в гражданском браке** и **не женатые /не замужем**;\n",
    "- В группе по *уровню достатка* - люди **доходом ниже среднего** и **средним**,они же самая большая группа выборки;\n",
    "- Самые рискованные *цели* для кредита - **покупка автомобиля** и **образование**.\n",
    " "
   ]
  }
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