{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b35cf393",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T15:58:39.122478Z",
     "start_time": "2022-10-14T15:58:38.474014Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "32c487e7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T15:59:21.528350Z",
     "start_time": "2022-10-14T15:59:19.662516Z"
    }
   },
   "outputs": [
    {
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       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   EmployeeNumber Attrition  Age     BusinessTravel  DailyRate  \\\n",
       "0               1       Yes   41      Travel_Rarely       1102   \n",
       "1               2        No   49  Travel_Frequently        279   \n",
       "2               3       Yes   37      Travel_Rarely       1373   \n",
       "3               4        No   33  Travel_Frequently       1392   \n",
       "4               5        No   27      Travel_Rarely        591   \n",
       "\n",
       "               Department  DistanceFromHome  Education EducationField  \\\n",
       "0                   Sales                 1          2  Life Sciences   \n",
       "1  Research & Development                 8          1  Life Sciences   \n",
       "2  Research & Development                 2          2          Other   \n",
       "3  Research & Development                 3          4  Life Sciences   \n",
       "4  Research & Development                 2          1        Medical   \n",
       "\n",
       "   EmployeeCount  ...  RelationshipSatisfaction StandardHours  \\\n",
       "0              1  ...                         1            80   \n",
       "1              1  ...                         4            80   \n",
       "2              1  ...                         2            80   \n",
       "3              1  ...                         3            80   \n",
       "4              1  ...                         4            80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  \\\n",
       "0                 0                  8                      0               1   \n",
       "1                 1                 10                      3               3   \n",
       "2                 0                  7                      3               3   \n",
       "3                 0                  8                      3               3   \n",
       "4                 1                  6                      3               3   \n",
       "\n",
       "   YearsAtCompany YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "0               6                  4                        0   \n",
       "1              10                  7                        1   \n",
       "2               0                  0                        0   \n",
       "3               8                  7                        3   \n",
       "4               2                  2                        2   \n",
       "\n",
       "   YearsWithCurrManager  \n",
       "0                     5  \n",
       "1                     7  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_excel(\"/Users/rishavdas/Downloads/Data/HR_Employee_Attrition-1.xlsx\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e15db34d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T15:59:58.990531Z",
     "start_time": "2022-10-14T15:59:58.985084Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "41d3719c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:00:50.437133Z",
     "start_time": "2022-10-14T16:00:50.427784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset['Attrition'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "02c0c4ba",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:04:34.469374Z",
     "start_time": "2022-10-14T16:04:34.441896Z"
    }
   },
   "outputs": [
    {
     "data": {
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      ],
      "text/plain": [
       "   EmployeeNumber Attrition  Age     BusinessTravel  DailyRate  \\\n",
       "0               1       Yes   41      Travel_Rarely       1102   \n",
       "1               2        No   49  Travel_Frequently        279   \n",
       "2               3       Yes   37      Travel_Rarely       1373   \n",
       "3               4        No   33  Travel_Frequently       1392   \n",
       "4               5        No   27      Travel_Rarely        591   \n",
       "\n",
       "               Department  DistanceFromHome  Education EducationField  \\\n",
       "0                   Sales                 1          2  Life Sciences   \n",
       "1  Research & Development                 8          1  Life Sciences   \n",
       "2  Research & Development                 2          2          Other   \n",
       "3  Research & Development                 3          4  Life Sciences   \n",
       "4  Research & Development                 2          1        Medical   \n",
       "\n",
       "   EmployeeCount  ...  RelationshipSatisfaction StandardHours  \\\n",
       "0              1  ...                         1            80   \n",
       "1              1  ...                         4            80   \n",
       "2              1  ...                         2            80   \n",
       "3              1  ...                         3            80   \n",
       "4              1  ...                         4            80   \n",
       "\n",
       "   StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear WorkLifeBalance  \\\n",
       "0                 0                  8                      0               1   \n",
       "1                 1                 10                      3               3   \n",
       "2                 0                  7                      3               3   \n",
       "3                 0                  8                      3               3   \n",
       "4                 1                  6                      3               3   \n",
       "\n",
       "   YearsAtCompany YearsInCurrentRole  YearsSinceLastPromotion  \\\n",
       "0               6                  4                        0   \n",
       "1              10                  7                        1   \n",
       "2               0                  0                        0   \n",
       "3               8                  7                        3   \n",
       "4               2                  2                        2   \n",
       "\n",
       "   YearsWithCurrManager  \n",
       "0                     5  \n",
       "1                     7  \n",
       "2                     0  \n",
       "3                     0  \n",
       "4                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1ea2fdbb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:04:45.445159Z",
     "start_time": "2022-10-14T16:04:45.424627Z"
    }
   },
   "outputs": [
    {
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       "      <td>No</td>\n",
       "      <td>39</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>613</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>2938</td>\n",
       "      <td>No</td>\n",
       "      <td>27</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>155</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>Life Sciences</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>80</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2938</th>\n",
       "      <td>2939</td>\n",
       "      <td>No</td>\n",
       "      <td>49</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1023</td>\n",
       "      <td>Sales</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2939</th>\n",
       "      <td>2940</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>628</td>\n",
       "      <td>Research &amp; Development</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>Medical</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      EmployeeNumber Attrition  Age     BusinessTravel  DailyRate  \\\n",
       "2935            2936        No   36  Travel_Frequently        884   \n",
       "2936            2937        No   39      Travel_Rarely        613   \n",
       "2937            2938        No   27      Travel_Rarely        155   \n",
       "2938            2939        No   49  Travel_Frequently       1023   \n",
       "2939            2940        No   34      Travel_Rarely        628   \n",
       "\n",
       "                  Department  DistanceFromHome  Education EducationField  \\\n",
       "2935  Research & Development                23          2        Medical   \n",
       "2936  Research & Development                 6          1        Medical   \n",
       "2937  Research & Development                 4          3  Life Sciences   \n",
       "2938                   Sales                 2          3        Medical   \n",
       "2939  Research & Development                 8          3        Medical   \n",
       "\n",
       "      EmployeeCount  ...  RelationshipSatisfaction StandardHours  \\\n",
       "2935              1  ...                         3            80   \n",
       "2936              1  ...                         1            80   \n",
       "2937              1  ...                         2            80   \n",
       "2938              1  ...                         4            80   \n",
       "2939              1  ...                         1            80   \n",
       "\n",
       "      StockOptionLevel  TotalWorkingYears  TrainingTimesLastYear  \\\n",
       "2935                 1                 17                      3   \n",
       "2936                 1                  9                      5   \n",
       "2937                 1                  6                      0   \n",
       "2938                 0                 17                      3   \n",
       "2939                 0                  6                      3   \n",
       "\n",
       "     WorkLifeBalance  YearsAtCompany YearsInCurrentRole  \\\n",
       "2935               3               5                  2   \n",
       "2936               3               7                  7   \n",
       "2937               3               6                  2   \n",
       "2938               2               9                  6   \n",
       "2939               4               4                  3   \n",
       "\n",
       "      YearsSinceLastPromotion  YearsWithCurrManager  \n",
       "2935                        0                     3  \n",
       "2936                        1                     7  \n",
       "2937                        0                     3  \n",
       "2938                        0                     8  \n",
       "2939                        1                     2  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b98e098b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:07:53.359812Z",
     "start_time": "2022-10-14T16:07:53.352840Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['EmployeeNumber', 'Attrition', 'Age', 'BusinessTravel', 'DailyRate',\n",
       "       'Department', 'DistanceFromHome', 'Education', 'EducationField',\n",
       "       'EmployeeCount', 'EnvironmentSatisfaction', 'Gender', 'HourlyRate',\n",
       "       'JobInvolvement', 'JobLevel', 'JobRole', 'JobSatisfaction',\n",
       "       'MaritalStatus', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n",
       "       'Over18', 'OverTime', 'PercentSalaryHike', 'PerformanceRating',\n",
       "       'RelationshipSatisfaction', 'StandardHours', 'StockOptionLevel',\n",
       "       'TotalWorkingYears', 'TrainingTimesLastYear', 'WorkLifeBalance',\n",
       "       'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n",
       "       'YearsWithCurrManager'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1d82b2ea",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:08:44.332598Z",
     "start_time": "2022-10-14T16:08:44.321173Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Travel_Rarely', 'Travel_Frequently', 'Non-Travel'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset['BusinessTravel'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4167d182",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:09:22.000422Z",
     "start_time": "2022-10-14T16:09:21.993469Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2940, 35)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9966cc28",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:09:37.317011Z",
     "start_time": "2022-10-14T16:09:37.308911Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102900"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4e9fa07a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:10:44.949834Z",
     "start_time": "2022-10-14T16:10:44.929746Z"
    }
   },
   "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>Attrition</th>\n",
       "      <th>Age</th>\n",
       "      <th>BusinessTravel</th>\n",
       "      <th>DailyRate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>41</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No</td>\n",
       "      <td>49</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Yes</td>\n",
       "      <td>37</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>33</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No</td>\n",
       "      <td>27</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>No</td>\n",
       "      <td>59</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>No</td>\n",
       "      <td>30</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>No</td>\n",
       "      <td>38</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>No</td>\n",
       "      <td>36</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>No</td>\n",
       "      <td>29</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>No</td>\n",
       "      <td>31</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Yes</td>\n",
       "      <td>28</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>No</td>\n",
       "      <td>29</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>No</td>\n",
       "      <td>32</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>No</td>\n",
       "      <td>22</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>1123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>No</td>\n",
       "      <td>53</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>No</td>\n",
       "      <td>38</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>No</td>\n",
       "      <td>24</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Yes</td>\n",
       "      <td>36</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>No</td>\n",
       "      <td>21</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Yes</td>\n",
       "      <td>34</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>No</td>\n",
       "      <td>53</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Yes</td>\n",
       "      <td>32</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>No</td>\n",
       "      <td>42</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>No</td>\n",
       "      <td>44</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>No</td>\n",
       "      <td>46</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>No</td>\n",
       "      <td>33</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>No</td>\n",
       "      <td>44</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>No</td>\n",
       "      <td>30</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Yes</td>\n",
       "      <td>39</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Yes</td>\n",
       "      <td>24</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>No</td>\n",
       "      <td>43</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Yes</td>\n",
       "      <td>50</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>No</td>\n",
       "      <td>36</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>No</td>\n",
       "      <td>33</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>No</td>\n",
       "      <td>27</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Yes</td>\n",
       "      <td>26</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>No</td>\n",
       "      <td>27</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>No</td>\n",
       "      <td>30</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Yes</td>\n",
       "      <td>41</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>No</td>\n",
       "      <td>37</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>No</td>\n",
       "      <td>46</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>1211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Yes</td>\n",
       "      <td>48</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Yes</td>\n",
       "      <td>28</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>No</td>\n",
       "      <td>44</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>No</td>\n",
       "      <td>35</td>\n",
       "      <td>Non-Travel</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>No</td>\n",
       "      <td>26</td>\n",
       "      <td>Travel_Rarely</td>\n",
       "      <td>1443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>No</td>\n",
       "      <td>33</td>\n",
       "      <td>Travel_Frequently</td>\n",
       "      <td>515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attrition  Age     BusinessTravel  DailyRate\n",
       "0        Yes   41      Travel_Rarely       1102\n",
       "1         No   49  Travel_Frequently        279\n",
       "2        Yes   37      Travel_Rarely       1373\n",
       "3         No   33  Travel_Frequently       1392\n",
       "4         No   27      Travel_Rarely        591\n",
       "5         No   32  Travel_Frequently       1005\n",
       "6         No   59      Travel_Rarely       1324\n",
       "7         No   30      Travel_Rarely       1358\n",
       "8         No   38  Travel_Frequently        216\n",
       "9         No   36      Travel_Rarely       1299\n",
       "10        No   35      Travel_Rarely        809\n",
       "11        No   29      Travel_Rarely        153\n",
       "12        No   31      Travel_Rarely        670\n",
       "13        No   34      Travel_Rarely       1346\n",
       "14       Yes   28      Travel_Rarely        103\n",
       "15        No   29      Travel_Rarely       1389\n",
       "16        No   32      Travel_Rarely        334\n",
       "17        No   22         Non-Travel       1123\n",
       "18        No   53      Travel_Rarely       1219\n",
       "19        No   38      Travel_Rarely        371\n",
       "20        No   24         Non-Travel        673\n",
       "21       Yes   36      Travel_Rarely       1218\n",
       "22        No   34      Travel_Rarely        419\n",
       "23        No   21      Travel_Rarely        391\n",
       "24       Yes   34      Travel_Rarely        699\n",
       "25        No   53      Travel_Rarely       1282\n",
       "26       Yes   32  Travel_Frequently       1125\n",
       "27        No   42      Travel_Rarely        691\n",
       "28        No   44      Travel_Rarely        477\n",
       "29        No   46      Travel_Rarely        705\n",
       "30        No   33      Travel_Rarely        924\n",
       "31        No   44      Travel_Rarely       1459\n",
       "32        No   30      Travel_Rarely        125\n",
       "33       Yes   39      Travel_Rarely        895\n",
       "34       Yes   24      Travel_Rarely        813\n",
       "35        No   43      Travel_Rarely       1273\n",
       "36       Yes   50      Travel_Rarely        869\n",
       "37        No   35      Travel_Rarely        890\n",
       "38        No   36      Travel_Rarely        852\n",
       "39        No   33  Travel_Frequently       1141\n",
       "40        No   35      Travel_Rarely        464\n",
       "41        No   27      Travel_Rarely       1240\n",
       "42       Yes   26      Travel_Rarely       1357\n",
       "43        No   27  Travel_Frequently        994\n",
       "44        No   30  Travel_Frequently        721\n",
       "45       Yes   41      Travel_Rarely       1360\n",
       "46        No   34         Non-Travel       1065\n",
       "47        No   37      Travel_Rarely        408\n",
       "48        No   46  Travel_Frequently       1211\n",
       "49        No   35      Travel_Rarely       1229\n",
       "50       Yes   48      Travel_Rarely        626\n",
       "51       Yes   28      Travel_Rarely       1434\n",
       "52        No   44      Travel_Rarely       1488\n",
       "53        No   35         Non-Travel       1097\n",
       "54        No   26      Travel_Rarely       1443\n",
       "55        No   33  Travel_Frequently        515"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[['Attrition', 'Age', 'BusinessTravel', 'DailyRate']][0:56]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fc3de63b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:12:25.176500Z",
     "start_time": "2022-10-14T16:12:25.145723Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2940 entries, 0 to 2939\n",
      "Data columns (total 35 columns):\n",
      " #   Column                    Non-Null Count  Dtype \n",
      "---  ------                    --------------  ----- \n",
      " 0   EmployeeNumber            2940 non-null   int64 \n",
      " 1   Attrition                 2940 non-null   object\n",
      " 2   Age                       2940 non-null   int64 \n",
      " 3   BusinessTravel            2940 non-null   object\n",
      " 4   DailyRate                 2940 non-null   int64 \n",
      " 5   Department                2940 non-null   object\n",
      " 6   DistanceFromHome          2940 non-null   int64 \n",
      " 7   Education                 2940 non-null   int64 \n",
      " 8   EducationField            2940 non-null   object\n",
      " 9   EmployeeCount             2940 non-null   int64 \n",
      " 10  EnvironmentSatisfaction   2940 non-null   int64 \n",
      " 11  Gender                    2940 non-null   object\n",
      " 12  HourlyRate                2940 non-null   int64 \n",
      " 13  JobInvolvement            2940 non-null   int64 \n",
      " 14  JobLevel                  2940 non-null   int64 \n",
      " 15  JobRole                   2940 non-null   object\n",
      " 16  JobSatisfaction           2940 non-null   int64 \n",
      " 17  MaritalStatus             2940 non-null   object\n",
      " 18  MonthlyIncome             2940 non-null   int64 \n",
      " 19  MonthlyRate               2940 non-null   int64 \n",
      " 20  NumCompaniesWorked        2940 non-null   int64 \n",
      " 21  Over18                    2940 non-null   object\n",
      " 22  OverTime                  2940 non-null   object\n",
      " 23  PercentSalaryHike         2940 non-null   int64 \n",
      " 24  PerformanceRating         2940 non-null   int64 \n",
      " 25  RelationshipSatisfaction  2940 non-null   int64 \n",
      " 26  StandardHours             2940 non-null   int64 \n",
      " 27  StockOptionLevel          2940 non-null   int64 \n",
      " 28  TotalWorkingYears         2940 non-null   int64 \n",
      " 29  TrainingTimesLastYear     2940 non-null   int64 \n",
      " 30  WorkLifeBalance           2940 non-null   int64 \n",
      " 31  YearsAtCompany            2940 non-null   int64 \n",
      " 32  YearsInCurrentRole        2940 non-null   int64 \n",
      " 33  YearsSinceLastPromotion   2940 non-null   int64 \n",
      " 34  YearsWithCurrManager      2940 non-null   int64 \n",
      "dtypes: int64(26), object(9)\n",
      "memory usage: 804.0+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "466686b9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:23:17.442190Z",
     "start_time": "2022-10-14T16:23:17.431736Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Attrition', 'BusinessTravel', 'Department', 'EducationField', 'Gender',\n",
       "       'JobRole', 'MaritalStatus', 'Over18', 'OverTime'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.select_dtypes('object').columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "013dd866",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:24:16.830250Z",
     "start_time": "2022-10-14T16:24:16.804628Z"
    }
   },
   "outputs": [],
   "source": [
    "for i in dataset.select_dtypes('object').columns:\n",
    "    dataset[i] = dataset[i].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8b9cc2bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:24:25.683862Z",
     "start_time": "2022-10-14T16:24:25.645731Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2940 entries, 0 to 2939\n",
      "Data columns (total 35 columns):\n",
      " #   Column                    Non-Null Count  Dtype   \n",
      "---  ------                    --------------  -----   \n",
      " 0   EmployeeNumber            2940 non-null   int64   \n",
      " 1   Attrition                 2940 non-null   category\n",
      " 2   Age                       2940 non-null   int64   \n",
      " 3   BusinessTravel            2940 non-null   category\n",
      " 4   DailyRate                 2940 non-null   int64   \n",
      " 5   Department                2940 non-null   category\n",
      " 6   DistanceFromHome          2940 non-null   int64   \n",
      " 7   Education                 2940 non-null   int64   \n",
      " 8   EducationField            2940 non-null   category\n",
      " 9   EmployeeCount             2940 non-null   int64   \n",
      " 10  EnvironmentSatisfaction   2940 non-null   int64   \n",
      " 11  Gender                    2940 non-null   category\n",
      " 12  HourlyRate                2940 non-null   int64   \n",
      " 13  JobInvolvement            2940 non-null   int64   \n",
      " 14  JobLevel                  2940 non-null   int64   \n",
      " 15  JobRole                   2940 non-null   category\n",
      " 16  JobSatisfaction           2940 non-null   int64   \n",
      " 17  MaritalStatus             2940 non-null   category\n",
      " 18  MonthlyIncome             2940 non-null   int64   \n",
      " 19  MonthlyRate               2940 non-null   int64   \n",
      " 20  NumCompaniesWorked        2940 non-null   int64   \n",
      " 21  Over18                    2940 non-null   category\n",
      " 22  OverTime                  2940 non-null   category\n",
      " 23  PercentSalaryHike         2940 non-null   int64   \n",
      " 24  PerformanceRating         2940 non-null   int64   \n",
      " 25  RelationshipSatisfaction  2940 non-null   int64   \n",
      " 26  StandardHours             2940 non-null   int64   \n",
      " 27  StockOptionLevel          2940 non-null   int64   \n",
      " 28  TotalWorkingYears         2940 non-null   int64   \n",
      " 29  TrainingTimesLastYear     2940 non-null   int64   \n",
      " 30  WorkLifeBalance           2940 non-null   int64   \n",
      " 31  YearsAtCompany            2940 non-null   int64   \n",
      " 32  YearsInCurrentRole        2940 non-null   int64   \n",
      " 33  YearsSinceLastPromotion   2940 non-null   int64   \n",
      " 34  YearsWithCurrManager      2940 non-null   int64   \n",
      "dtypes: category(9), int64(26)\n",
      "memory usage: 624.6 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "82cada1e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:26:39.631701Z",
     "start_time": "2022-10-14T16:26:39.542245Z"
    }
   },
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EmployeeNumber</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>1470.500000</td>\n",
       "      <td>848.849221</td>\n",
       "      <td>1.0</td>\n",
       "      <td>735.75</td>\n",
       "      <td>1470.5</td>\n",
       "      <td>2205.25</td>\n",
       "      <td>2940.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>36.923810</td>\n",
       "      <td>9.133819</td>\n",
       "      <td>18.0</td>\n",
       "      <td>30.00</td>\n",
       "      <td>36.0</td>\n",
       "      <td>43.00</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DailyRate</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>802.485714</td>\n",
       "      <td>403.440447</td>\n",
       "      <td>102.0</td>\n",
       "      <td>465.00</td>\n",
       "      <td>802.0</td>\n",
       "      <td>1157.00</td>\n",
       "      <td>1499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DistanceFromHome</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>9.192517</td>\n",
       "      <td>8.105485</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>14.00</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Education</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>2.912925</td>\n",
       "      <td>1.023991</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EmployeeCount</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnvironmentSatisfaction</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>2.721769</td>\n",
       "      <td>1.092896</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HourlyRate</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>65.891156</td>\n",
       "      <td>20.325969</td>\n",
       "      <td>30.0</td>\n",
       "      <td>48.00</td>\n",
       "      <td>66.0</td>\n",
       "      <td>84.00</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobInvolvement</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>2.729932</td>\n",
       "      <td>0.711440</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobLevel</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>2.063946</td>\n",
       "      <td>1.106752</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JobSatisfaction</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>2.728571</td>\n",
       "      <td>1.102658</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>6502.931293</td>\n",
       "      <td>4707.155770</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>2911.00</td>\n",
       "      <td>4919.0</td>\n",
       "      <td>8380.00</td>\n",
       "      <td>19999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyRate</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>14313.103401</td>\n",
       "      <td>7116.575021</td>\n",
       "      <td>2094.0</td>\n",
       "      <td>8045.00</td>\n",
       "      <td>14235.5</td>\n",
       "      <td>20462.00</td>\n",
       "      <td>26999.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NumCompaniesWorked</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>2.693197</td>\n",
       "      <td>2.497584</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PercentSalaryHike</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>15.209524</td>\n",
       "      <td>3.659315</td>\n",
       "      <td>11.0</td>\n",
       "      <td>12.00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18.00</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PerformanceRating</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>3.153741</td>\n",
       "      <td>0.360762</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RelationshipSatisfaction</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>2.712245</td>\n",
       "      <td>1.081025</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StandardHours</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.00</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StockOptionLevel</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>0.793878</td>\n",
       "      <td>0.851932</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalWorkingYears</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>11.279592</td>\n",
       "      <td>7.779458</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.00</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.00</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TrainingTimesLastYear</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>2.799320</td>\n",
       "      <td>1.289051</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WorkLifeBalance</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>2.761224</td>\n",
       "      <td>0.706356</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsAtCompany</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>7.008163</td>\n",
       "      <td>6.125483</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.0</td>\n",
       "      <td>9.00</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsInCurrentRole</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>4.229252</td>\n",
       "      <td>3.622521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsSinceLastPromotion</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>2.187755</td>\n",
       "      <td>3.221882</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearsWithCurrManager</th>\n",
       "      <td>2940.0</td>\n",
       "      <td>4.123129</td>\n",
       "      <td>3.567529</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.00</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count          mean          std     min      25%  \\\n",
       "EmployeeNumber            2940.0   1470.500000   848.849221     1.0   735.75   \n",
       "Age                       2940.0     36.923810     9.133819    18.0    30.00   \n",
       "DailyRate                 2940.0    802.485714   403.440447   102.0   465.00   \n",
       "DistanceFromHome          2940.0      9.192517     8.105485     1.0     2.00   \n",
       "Education                 2940.0      2.912925     1.023991     1.0     2.00   \n",
       "EmployeeCount             2940.0      1.000000     0.000000     1.0     1.00   \n",
       "EnvironmentSatisfaction   2940.0      2.721769     1.092896     1.0     2.00   \n",
       "HourlyRate                2940.0     65.891156    20.325969    30.0    48.00   \n",
       "JobInvolvement            2940.0      2.729932     0.711440     1.0     2.00   \n",
       "JobLevel                  2940.0      2.063946     1.106752     1.0     1.00   \n",
       "JobSatisfaction           2940.0      2.728571     1.102658     1.0     2.00   \n",
       "MonthlyIncome             2940.0   6502.931293  4707.155770  1009.0  2911.00   \n",
       "MonthlyRate               2940.0  14313.103401  7116.575021  2094.0  8045.00   \n",
       "NumCompaniesWorked        2940.0      2.693197     2.497584     0.0     1.00   \n",
       "PercentSalaryHike         2940.0     15.209524     3.659315    11.0    12.00   \n",
       "PerformanceRating         2940.0      3.153741     0.360762     3.0     3.00   \n",
       "RelationshipSatisfaction  2940.0      2.712245     1.081025     1.0     2.00   \n",
       "StandardHours             2940.0     80.000000     0.000000    80.0    80.00   \n",
       "StockOptionLevel          2940.0      0.793878     0.851932     0.0     0.00   \n",
       "TotalWorkingYears         2940.0     11.279592     7.779458     0.0     6.00   \n",
       "TrainingTimesLastYear     2940.0      2.799320     1.289051     0.0     2.00   \n",
       "WorkLifeBalance           2940.0      2.761224     0.706356     1.0     2.00   \n",
       "YearsAtCompany            2940.0      7.008163     6.125483     0.0     3.00   \n",
       "YearsInCurrentRole        2940.0      4.229252     3.622521     0.0     2.00   \n",
       "YearsSinceLastPromotion   2940.0      2.187755     3.221882     0.0     0.00   \n",
       "YearsWithCurrManager      2940.0      4.123129     3.567529     0.0     2.00   \n",
       "\n",
       "                              50%       75%      max  \n",
       "EmployeeNumber             1470.5   2205.25   2940.0  \n",
       "Age                          36.0     43.00     60.0  \n",
       "DailyRate                   802.0   1157.00   1499.0  \n",
       "DistanceFromHome              7.0     14.00     29.0  \n",
       "Education                     3.0      4.00      5.0  \n",
       "EmployeeCount                 1.0      1.00      1.0  \n",
       "EnvironmentSatisfaction       3.0      4.00      4.0  \n",
       "HourlyRate                   66.0     84.00    100.0  \n",
       "JobInvolvement                3.0      3.00      4.0  \n",
       "JobLevel                      2.0      3.00      5.0  \n",
       "JobSatisfaction               3.0      4.00      4.0  \n",
       "MonthlyIncome              4919.0   8380.00  19999.0  \n",
       "MonthlyRate               14235.5  20462.00  26999.0  \n",
       "NumCompaniesWorked            2.0      4.00      9.0  \n",
       "PercentSalaryHike            14.0     18.00     25.0  \n",
       "PerformanceRating             3.0      3.00      4.0  \n",
       "RelationshipSatisfaction      3.0      4.00      4.0  \n",
       "StandardHours                80.0     80.00     80.0  \n",
       "StockOptionLevel              1.0      1.00      3.0  \n",
       "TotalWorkingYears            10.0     15.00     40.0  \n",
       "TrainingTimesLastYear         3.0      3.00      6.0  \n",
       "WorkLifeBalance               3.0      3.00      4.0  \n",
       "YearsAtCompany                5.0      9.00     40.0  \n",
       "YearsInCurrentRole            3.0      7.00     18.0  \n",
       "YearsSinceLastPromotion       1.0      3.00     15.0  \n",
       "YearsWithCurrManager          3.0      7.00     17.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "50ad8b78",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:36:14.399240Z",
     "start_time": "2022-10-14T16:36:14.388640Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     2466\n",
       "Yes     474\n",
       "Name: Attrition, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.Attrition.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c7b7c193",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:37:06.041466Z",
     "start_time": "2022-10-14T16:37:06.034593Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.16122448979591836"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "474/2940"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7210f58e",
   "metadata": {},
   "source": [
    "**Attrition Rate is nearly 16%**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "66995344",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:38:00.694209Z",
     "start_time": "2022-10-14T16:38:00.683801Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      1764\n",
       "Female    1176\n",
       "Name: Gender, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.Gender.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "45106f27",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:38:17.520160Z",
     "start_time": "2022-10-14T16:38:17.514225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1764/2940"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "34ea3c9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:42:33.734973Z",
     "start_time": "2022-10-14T16:42:33.694724Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Value count for Attrition\n",
      "No     83.9%\n",
      "Yes    16.1%\n",
      "Name: Attrition, dtype: object\n",
      "**************************************************\n",
      "Value count for BusinessTravel\n",
      "Travel_Rarely        71.0%\n",
      "Travel_Frequently    18.8%\n",
      "Non-Travel           10.2%\n",
      "Name: BusinessTravel, dtype: object\n",
      "**************************************************\n",
      "Value count for Department\n",
      "Research & Development    65.4%\n",
      "Sales                     30.3%\n",
      "Human Resources            4.3%\n",
      "Name: Department, dtype: object\n",
      "**************************************************\n",
      "Value count for EducationField\n",
      "Life Sciences       41.2%\n",
      "Medical             31.6%\n",
      "Marketing           10.8%\n",
      "Technical Degree     9.0%\n",
      "Other                5.6%\n",
      "Human Resources      1.8%\n",
      "Name: EducationField, dtype: object\n",
      "**************************************************\n",
      "Value count for Gender\n",
      "Male      60.0%\n",
      "Female    40.0%\n",
      "Name: Gender, dtype: object\n",
      "**************************************************\n",
      "Value count for JobRole\n",
      "Sales Executive              22.2%\n",
      "Research Scientist           19.9%\n",
      "Laboratory Technician        17.6%\n",
      "Manufacturing Director        9.9%\n",
      "Healthcare Representative     8.9%\n",
      "Manager                       6.9%\n",
      "Sales Representative          5.6%\n",
      "Research Director             5.4%\n",
      "Human Resources               3.5%\n",
      "Name: JobRole, dtype: object\n",
      "**************************************************\n",
      "Value count for MaritalStatus\n",
      "Married     45.8%\n",
      "Single      32.0%\n",
      "Divorced    22.2%\n",
      "Name: MaritalStatus, dtype: object\n",
      "**************************************************\n",
      "Value count for Over18\n",
      "Y    100.0%\n",
      "Name: Over18, dtype: object\n",
      "**************************************************\n",
      "Value count for OverTime\n",
      "No     71.7%\n",
      "Yes    28.3%\n",
      "Name: OverTime, dtype: object\n",
      "**************************************************\n"
     ]
    }
   ],
   "source": [
    "for i in dataset.select_dtypes('category').columns:\n",
    "    print(f\"Value count for {i}\")\n",
    "    print(dataset[i].value_counts(normalize=True).mul(100).round(1).astype(str)+'%')\n",
    "    print('*'*50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "08263083",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:44:54.956557Z",
     "start_time": "2022-10-14T16:44:54.947428Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EmployeeNumber              0\n",
       "Attrition                   0\n",
       "Age                         0\n",
       "BusinessTravel              0\n",
       "DailyRate                   0\n",
       "Department                  0\n",
       "DistanceFromHome            0\n",
       "Education                   0\n",
       "EducationField              0\n",
       "EmployeeCount               0\n",
       "EnvironmentSatisfaction     0\n",
       "Gender                      0\n",
       "HourlyRate                  0\n",
       "JobInvolvement              0\n",
       "JobLevel                    0\n",
       "JobRole                     0\n",
       "JobSatisfaction             0\n",
       "MaritalStatus               0\n",
       "MonthlyIncome               0\n",
       "MonthlyRate                 0\n",
       "NumCompaniesWorked          0\n",
       "Over18                      0\n",
       "OverTime                    0\n",
       "PercentSalaryHike           0\n",
       "PerformanceRating           0\n",
       "RelationshipSatisfaction    0\n",
       "StandardHours               0\n",
       "StockOptionLevel            0\n",
       "TotalWorkingYears           0\n",
       "TrainingTimesLastYear       0\n",
       "WorkLifeBalance             0\n",
       "YearsAtCompany              0\n",
       "YearsInCurrentRole          0\n",
       "YearsSinceLastPromotion     0\n",
       "YearsWithCurrManager        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a36a69fc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:47:00.164300Z",
     "start_time": "2022-10-14T16:47:00.152837Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4787.0928270042195"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset['Attrition'] == 'Yes']['MonthlyIncome'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "36d13546",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:47:20.503909Z",
     "start_time": "2022-10-14T16:47:20.493200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6832.739659367397"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[dataset['Attrition'] == 'No']['MonthlyIncome'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "347cebe4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:51:23.704693Z",
     "start_time": "2022-10-14T16:51:23.693800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attrition\n",
       "No      8.915653\n",
       "Yes    10.632911\n",
       "Name: DistanceFromHome, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.groupby(['Attrition'])['DistanceFromHome'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4390da7f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:53:08.409611Z",
     "start_time": "2022-10-14T16:53:08.034180Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f91782c5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:53:13.433120Z",
     "start_time": "2022-10-14T16:53:12.749028Z"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "dd799fe7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:54:51.253214Z",
     "start_time": "2022-10-14T16:54:51.093436Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [2,3,4,5,6,6,7,3,2,5,3,4]\n",
    "y = [10,20,40,20,10,24,15,43,23,12,13,14]\n",
    "\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f84dfd06",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:55:13.976070Z",
     "start_time": "2022-10-14T16:55:13.850735Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "8525e2ac",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:56:14.540469Z",
     "start_time": "2022-10-14T16:56:14.406917Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "00e0faf3",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:57:39.532064Z",
     "start_time": "2022-10-14T16:57:39.528986Z"
    }
   },
   "outputs": [],
   "source": [
    "cars = ['TaTa', 'BMW', 'mercedes', 'suzuki']\n",
    "market_share = ['50', '10', '10', '30']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e919f4b8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:58:25.696633Z",
     "start_time": "2022-10-14T16:58:25.590426Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,12))\n",
    "plt.pie(market_share, labels=cars)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "44282b1f",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T16:59:44.908849Z",
     "start_time": "2022-10-14T16:59:44.761184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bWfUvLS3BaDTCarWGWPWHE5hww8bYNEsmKEklXIjLYDBgdHQUTU1NdEETkwivrc2en9xnuBHCkTy/kk4om5GRkYGsrCxs27YNOp2ONuLNzs7CZrOFLWNNJKkQ8lLi5wqEWvUHAgH6myP9TTzPrwl/RiowV+M0SyYoScLv96/pLRkaGoLT6URnZ+eGieFkzSsBruzoRkZG4HA4Nr3PaJ9fqYIiRtiIJy5jFVYZFRcXJ8QiJhUEJRUIBALQaDTIz8+P2qqfDRu7AhOUBBOut4R4XFVWVkYUOpJbUNZ7fmGIq7GxMeZFbCPhSAVBERKujNVms8FqtWJwcBA+ny+kgkzqJtRUIRUEL1wYORarfuDqnWbJBCWBiEfzchyHubk5TE1NhW0AXI9knFCIk3EsIa5Inp/8earDcdyaJDDZ4c7MzIDn+ZBph1JYxCj9hJIqm4RI8pKRWPULBYY832YCky7DxpigJIBwo3n9fj8GBgYAgHpcRYrcoSHh4hQMBjE6OgqbzRZziCvc8xNBFF4rVUJe0SAepyvc4Y6Pj0OtVtPwWKwWMakgKEq+P0IshS7hrPqXl5dhMBgwNjYW1qofWDsuWThsLJUFhgmKzIQbzbuysoL+/n5qnxItcp9QCG63Gz09PSgpKZHMyZiQLiGvaBHvcL1eL6xWKxYWFjA8PEyddouLiyO2iFH6gi1nRaKUBAKBuE+MWq0W5eXlKC8vB4CwVv1CJ+VwVv2pPM2SCYqMhOstmZycxMLCAtra2pCTkxPT8yZCUPx+P/70pz9h165dNEEpFRt10F9taLVaVFRU0KZVYhEzNTUVMmSsuLg4ZS1ilC54BDmEbz2r/rm5uais+oWzYILBIDIyMpCdna04J2UmKDIQrrfE6/Wit7cXOp0O11xzTVxfXDkFJRgMYmxsDB6PBwcPHpSlSinVq7zkROy0S3yqRkdHQ4aMFRcX0/Cj0hfsVDmhJOI+17PqJ1NK17PqJ/A8j7m5OajVaipSarUaJ0+exF133SXrvUcCExSJCddbYjabMTQ0hB07dtCjcDzIJSgkxFVcXAydTidbyatQOJS+GCYTsU8V6fK2Wq3o7++nQ8Z4nkdWVlayb3ddUukzTvR9xmLVHwwGkZWVBbVaTTev3//+95mgpBtut5t+CciiOTY2huXlZezbt0+yH70cExWJ6JGGSoPBIOnzC0nnKi85EXZ5C4eMTU9Pw2w2w2QyKXLIWKqcUJJNOBPTcFb9fr8fWq2WCrXL5YJOp4vqWsePH8dzzz2H8vJy9PX1hbsXDsB3ARwC4ARwJ8/zb2z2vExQJIDsEmw2GyYmJtDW1kZ7NkpKSrB//35JF8tIOtkjRSh6Usyjj4R06kNJJmTImMvlAgCUl5eHlLCSIWOkgixZgp1KJxQlsZ5V//DwMBYWFjAzM4PHHnsMdXV1UW9W77zzTtx7772444471nvIuwHs+PN/1wD4jz//3w1hghInwt4StVqNYDBInXel6NkIh1QhL4/Hg56eHuoZlqgfvVhQ2IITHzzP01LTcEPGSAKYDBkjIc1Evefs85UGEgLV6XTQ6/XIzc1FMBjE6dOnMTw8jLa2NnR0dOBtb3sbjhw5smFf28GDB2nn/zocAfBf/JUf6mscxxVyHFfJ8/zCRv+ICUqMhOstAYCVlRXwPC9Zz0Y4pBAUEuJqbGykJayJQuiWLBYXtvBEz3qnuvWGjE1MTIQMGSsuLpY1B8NCXtJCyps5jsN1110HnU4Hm82GH/3oR3jjjTfwyiuvYGVlJeJG6XWoAjAj+N+zf/4zJihSE663xG63o6enB2q1Gu3t7bIujPEICs/zuHz5MiwWi6R5nVjuI5o/Z2zMZt+3cPF5MidkeHiYDhkrLi5GYWGhpJuhVDihpNL3Ttwv43A4kJOTA41GgwMHDuDAgQNSXCbcB7bpm8QEJUrC9ZbMzs5ienoau3fvxtDQkOw/HpVKRU9G0eDxeNDb24v8/Pywtvhi5FoIlL64pBqxfE5cmEFUZMjY7OwsHTJWXFxMTRBjJRVOKKkgeoRwgpKbmyv1ZWYBVAv+tx7A/Gb/iAlKhITrLfH5fOjv76c7A5JDkZtYqrwsFgsGBwexc+dOGmPfCDmHeG2UlE+VH7WSkGJ3vdGQsYmJCfr3xcXFIRYikd6f0j/XVBA9QiAQCLlXmQTlFwDu5TjuSVxJxq9slj8BmKBERLjekuXlZfT396O+vp42KSWKaKq8eJ7H+Pg4zGZzVCEuOZsMWZWXtMixYIcbMkaGUBELkeLi4pAO7/VIhcWaFNWkAqQIg0BCXtFw++2349VXX4XJZIJer8dXvvIV+Hw+fPKTn7yb5/mHAZzFlZLhMVwpG46oyYUJyiaQxLvwRzsxMYGlpSW0t7dHXf8tBZHmUEh3fm5ubkQhLiGJEBTSoJeVlUUXJyYosSH3CSAjIyPEo4pYiJAOb51ORz/D7OzsNd3dSj+hiHf9qUQsJ5Sf/vSnYf/87rvvfhgA/lzddU+098IEZR3ChbhIDiIvLw8HDhxI2hcwEkGxWq0YGBiIOMQVyzXiYXV1FTabDa2trQgEAiEDqgYHB9fYizDWJxkL9npDxsbGxuB2u5GbmxuySVC6oKTCKWo9HA4H9YJLNkxQwhBubonJZMLw8HDMC7SUbLTY8zyPiYkJGI1GdHR0xGwoKNcJxe/3Y2xsDD6fD295y1vg9/vB8zxt3jp//jy2bt0Ki8WCvr4+BINBGrtXUve3kkj2qW69IWMWiwUDAwNwuVzIzMxETk4OndOuNFJdUGTIocQEExQB4t4SsqiOjIxgdXU1qWW2QtZLypMQV05ODjo7O+M2oJR6oSKl1RUVFXC5XGHvj+M4ai+ybds2Oj9E2P1dXFyM4uLiTWP3VxNKeh+EQ8bq6uowNzcHu92O1dVVOqddaZuEVBGUcKc9JigKJNxoXqfTid7eXpSVlUXVSS73ET9cUp4UCUhlQCmlvQsALCwsYGJiAi0tLQCu5KHWu64Q8fwQj8cDi8VCY/c5OTlUYJQg9skgFUJKubm5dPaPeJMgxZCxeEkVQQk3s4UJisIQ95ZwHIfFxUVcvnwZzc3NKCwsjPi55Cy3JQhDXjzPY3JyEgaDIa4QlxipQl7BYBDDw8Nwu93o7OxERkYGbDbbuo/f7JqZmZkhsXuHwwGLxUKb80jvRFFRUVy9E6mE0gVFXJW03pCx+fl5OiOEfIaRDhmLl1Sp8lpPUGKdrSQ1V8cvbh2EIS6SeA8EAhgcHITf78eBAweijveSXhQ5dztEUHw+H3p7e5GdnR13iGu9a8QDscMvKytDU1MTXRiEYhWPcAnt3Ulz3srKCiwWC6ampsBxHN35Rts7wZCOYDC4obivN2SMWLiTUyipIJODVKnyWk9Q8vLyknRHoVy1ghKut8Rms6G3txfV1dXQ6/Ux7YyIKMm5OyYVZxcuXEBDQ4MsFR7xnlCIV1i4iY9yNTaK57eT3gkyfpXsfBNtjig3qXBCieb+1hsyNjIyAo/Hg7y8PCowUlUBspCXNFyVghLOPmV6ehpzc3NoaWmJS+3lLrfleZ6GBq6//nrZ+mBiFRQSgjMajesWMSSqsVHcO+FyuWCxWOhsCbIwpXp5stIFJZ7Fer0hYxaLBXNzcwgEAnQGTGFhYcwbuVQWFKfTyQQlGaxnn9LX1wetVkvtU+JBrVYjEAhIdMehkHvNyMhAfn6+rE2VsQgjub+srKwNGymTNeo3OzsbVVVV1ByRLEx9fX10YSLmiKkQTxeiZEGRUvCEQ8aIRQwJc05OTtIwJxkyFqlIpLKgeL3ehMwxioSrRlDChbhI89/27dvpfOZ4keuEsrKyQq1eSktL8ac//UnyawiJdtEn4cL6+vpN38vNQl6J2HGLS1uJdxU5wYiHUymZZPehbIacnycZMkbCqj6fD8vLyzAYDBgbG6Nl5kVFRRsOGdssz6MUwgmKklD+OygBbrcbbrcbmZmZ9At1+fJlmEwmye1TpBYUnucxPT2N+fl5tLW1IScnB8FgUHYTymgEZX5+HpOTk2htbZXk6J2MEI7Yu4oMp5qdnYXNZoPf78fS0hK2bNkiW2I4VtI55BUt4YaMWSwW+jlmZ2dTgRHm0ZS+UBPE96m0zz6tBYWEuIxGI6xWKxobG+F2u9Hb24uCggLJK6MAaUNefr+fhriE4bhEhIwi6UMJBoMYHByEz+fDgQMHIt7hbdQ0qZQfh3g4VXd3N4LBIE0MFxQU0NBKsju/lbaoiEnm/YnLzMV5NGIR4/V6UzbkpaTPP20FJdxoXqPRiJGRETQ1NdGdqNRIdUJZXV1FX18ftm3btsbNOBFfns065V0uF7q7u1FZWYmampqo72mzkJeS4DgOGo0GlZWV0Ol0tDzZarViZubKUDth53eiFyalvV9ilJKfEA4ZIxYxdrsdFosFZrMZFouFnl6UsFEIh7iCVGnlzmknKOFG86pUKpjNZjidTuzfv1/WBFa8gsLzPGZmZjA3N0dDXMlgo4Wd+Jrt3r2bluhK9dyA8hfIcOXJJG4/OjqKzMxMmn9JVGOeUnao4VDSDlqIcMiY2+2m1YBko0AsYkgFmRJCYmJBUVLJMJBmghJuNK/D4cDAwAA4jkNHR4fsX+x4Ql5+vx/9/f1Qq9WSVJzFQzhhJOODrVZrXMKcbgO2xHF7cWNeXl4eFRg5NjNKXbAJSjmhbARJypPPCnjTIoaEyIhFTFFRUdIaZcMN11JKlzyQRoISzj6FJIsbGhowPz+fsFBRLCcUUiVVV1eHrVu3ynBn0SFe9IWzVfbt2xfXjymSKq9URtyYJ3Te9fv9dNdbVFQkyaZB6e+X0gUPCC964SxilpeXQxplyeeYKKNSsUUMExSJCddb4vf7MTg4iGAwiAMHDiAYDNJYt9xEKyg8z2N2dhazs7OSVUlJgXBhX1lZQV9fn2Rd+cLnXu8klC6sV55MTjAqlYrG7fPz82NelJS8YKeCoERS5aXVatc0ygqHjOXk5FCBEQ8Zkwq/3x9yn3a7XTFrBpDighKut4Qks2tqalBVVQWO4+D3+2VrNhSjVqvh8Xgieqzf76fhuGSHuMSQhX52dhYzMzPYu3evZDshoaCIxUPpC0+8iMuTvV4vLBYL5ufnMTQ0FDL5MNJydqUv2KkS8or2HsUnUafTCYvFQoeMkfBZUVGRZKFO8QmFCJlSSFlBIacS4Y9pamoK8/Pza3b6cnavi4n0hEJCXLW1tdTWW0kQixedTpdQsUuHkFc0aLXakPJk8aKUn59PBWa9qiOlv19KFzwgftETDhkjFjGkgoyEOkmpeTxDxsQnFKfTyQQlHsKFuLxeL7X8CLf4JfLLHImgzM7OYnp6Oq4QF+kTkWPn53Q6MTk5iYKCArS0tEj+/m30fFeboAgJtyitrq7CYrHQqiOS3BeXJyt5wU7XE8pGqFSqNaFOUmpOhowJLWIi3bCFy6EoyckhpQQl3Ghei8WCwcFB2Vx3o4X0vIQjEAhgYGAAPM9H1QgYDrkWXlL6WlVVBbVanZCFKhV2sMlApVKhsLCQzuPx+/2wWq30M9JqtSguLqYl8kolFT5f8cwWqRFbxJDPUjiJlAjMRkPGWJWXBITrLeF5HmNjY7BYLJIOlooXYl8vxm63U2t8ktuJ9zpSDgXieR6jo6NYXV1FZ2cnzGYzXC6XJM8dDVfzCWUzNBpNSHmy2+2GxWKBy+VCV1dXiHuyUswCgdQ4oSQa8WdJrH6Ik3hWVhYVGHEvk/D/dzgcKCgoSPj9r4fiBSVcbwkZ3FRUVLShq20yCBfympubw9TUVNzW+JtdJ1a8Xi+6u7tRVFSEffv20fc5GQs7E5TIycrKwtatW7G0tITdu3fTBD+xwyHTK+OxdZcKpZ9Qko3Y6kfcy5Sbm4uioqI1v3mHwxF1DvbcuXO47777EAgEcOLECTzwwAMhf89xXAGA/wZQgysa8a88zz8ayXMrWlDC9ZaQ4364wU2bkYijtzDkRaY/BgKBuENcYqQSFDKLfufOnXS3JOXzMxKDSqWiXd+1tbVrbN1Jd38y57YzIkNoEUNGLdjtdlitVjpYz2azYWpqClarNao8bCAQwD333IMXX3wRer0enZ2dOHz4MHbv3i182D0ABniev4XjuDIAwxzHPc7zvHez51ekoIRLvAcCgZDZ5NEORJI6RLTRdQKBAA1x6fX6mKc/bnYdKSxe5ufnwzousxPKWpS6yw63URLH7MVz27Oysujfy9UzwZAGYhGTm5uLpaUl7Nu3D0NDQ/jVr36FX//613jhhRfw6quv4h3veAcOHjy4YRSETHmtr68HANx22204ffq0WFB4AHnclS9FLgALAH8k96o4QQnXW0IW58rKSuzatSumLz8pHU6EoDidTvT09GDPnj3Iz8+X7TqxLrxCi5fOzs6w7wkTlNQhkvdLOLdd6Lor7JmQeqwuQ1pILkqlUmH37t148MEH8alPfQp33nknvF4vXnrpJbzwwgv43ve+t+5zzM3Nobq6mv5vvV6P8+fPix/2EIBfAJgHkAfgQzzPR7R7VZSgkMS7cMdF8g/xLs6J6EUJBAIYHR2Fx+PBwYMHZY1bR2IvHw6Hw4Genh5UV1dDr9ev+zgW8kototlkiV13xWN1g8FgiHtyvJswpW8QUuV7Hm5DbLfbUVZWhubmZtx4442bPke4zyLMd+cmAF0A3g5gO4AXOY77H57nVzd7fkUIynr2Kf39/bSLPN7FWW5BIQt1ZWUl7Ha77EnQWBb8xcVFjI+PRyTOiTopiL/M7IQSPfHmBsVjdYkporCklYTHEuVZlUgSEQqXgnCCEm0fil6vD7Ghmp2dDecdeBeAb/BXfohjHMdNAGgCcGGz50+6oITrLSHjbqU0SpRTUMhC3dzcjPz8fMzPz8tyHSHRCAoZDOV0OtHZ2RlRl24iFvZgMIjR0VG43W66YDFBiR6pi03Epohut3uNZxX5vLKysjZ9PqULUKqUNa8nKNEk5Ts7OzE6OoqJiQlUVVXhySefxBNPPCF+2DSAdwD4H47jKgA0AhiP5PmTJiji3hLypZuYmMDS0pLks0A2ajiMlWAwiKGhIXg8nogXaqmIVFA8Hg+6u7tRWlqKxsbGiH/ccoe8gsEgXn/9dZSUlKC4uBhWqxV9fX1wOBwIBoOorKxMyrAqxlqysrJCph46HA5YLBYMDQ3B6/WGuCcnuzw5FlJZUMjUyUjRaDR46KGHcNNNNyEQCOD48eNobm7Gww8/DAC4++67AeCrAB7jOK4XAAfg73meN0X0/BHfiYTwPI/l5WV4PB4UFhaC4zhqj56Tk4MDBw5I/gGv13AYKyTxHk+hQDxEsuCTnoRYJlTKeVJYWVmB0+lEU1MTioqK4PP5UFhYiG3btmFwcBA6nY6WhwurkSI1S7waSdT3j+M45ObmIjc3FzU1NSGWIlNTU+A4jpYnJ2tmSLSksqD4/f6oN7KHDh3CoUOHQv7sz0ICAOB5fh7A5gmZMCRcUEhvyerqKux2O4qKimA2mzE0NLSmF0JKpAx5LS4u4vLly9izZ0/SulQ3qvLieR6Tk5MwGAzYt29fRGEJMbEm/TeDuBfrdDqUlpau+UzUajUKCgpQW1sbthqJNOul6m443RCXJ/t8Plit1pCZIV6vFw6HAzqdTpHhr1QSFKXfZ0J/kaQkGLhy9PL7/RgZGcHKykrMC1+kSCEowWAQw8PDcLlcOHDgQFJnTq+34Pv9fvT29iIzMxOdnZ0xfwHjKUsORzAYxODgIPx+Pw4cOBCuVJFCrhuuGok0601NTdFZIqRZT4mLVSJQUs4pIyODzgwh7snd3d0YHx+Hy+UKmV6plPLkVE3KC38nSiGhgkKS7sCVN2dhYQE1NTXYv3+/okfzAm+GuLZs2YKmpqakf4jhQl5STn2UMuTldrvR3d2NiooK1NbWbuo2vB7iWe7EamR2dhY2m40mi0tKShTlZXW1wnEcMjMzkZWVhZaWFvA8T92T+/r6EAgE6OeZzJntqbDzByIbApZsEh4zUKlUmJ+fx+joKHJzc7F9+/aEXFetVsfsyrq0tISxsTE0NzdT59fNkNvmRSwoZNyxVH5hUgmK1WrFwMBA2DzOejXxkV5XPEuEJIuFo3aJl5XSf4jxkOzNzUYIfwccx4UtTyYz24njbqJPnKkS8iJz7wk+n09xYd+E383Q0BAcDgdaW1sxPh5RJZokqNVquN3uqP6NuNw20iM6WRTlFpRAIEArzbxer6R+YVJau0TrBh2LkIVLFgsXq4yMDBoeE7u3MuRjo8VaXJ7s8XjCnjjJSN1k3KOS8Pv9ISdvpVnXA0kQlOrqamRlZcHn88Hvj8geRhKiLRt2uVzo6elBeXl5VOW25FpyH6OJxcvFixdRUVEheaVZPCcUYorJ83zU1i5SvQbxqF1i9U7cW8kkxOLi4qTmwqRASTkUMdFsrDIzM8OWJ4+MjMDj8YS4J0v5maWKoIQbrnXVC0pubi6NBSZqLC8QXdkwKVndvXs3jddHey257RwcDgfm5ubQ1tYWtetyJMQqKG63G11dXdi6dSuqq6vXXUzWO8XJVa5MrN7J/G8Sy5+dnQXP81RcUqXUNVWIdbEWnziFBRnT09MAIFl5cqrmUKJtakwESQvAJdorKhIBI53bdrs9Jkdj4bXkem08z2N8fBxLS0uorKyURUyA2D4f0vcSiRBvFBaUe8ctjuWLS11J70tJSYliBrdthJLDd1KFfsUFGeHKk4X9StFcM1WrvNgJRUCifwSbCQqpRCorK0NHR0fc3khynL58Ph9t/ty5cyeWl5clvwYhmpMCz/OYmpqi1tqR2nHIGfKKBnGpK+l9EYZaPB5PQk/U6YJcuUThZwaAfmbj4+NwOp0h7smbVfyJk91KRSwodrudnVCStZvaSFCMRiNGRkZiGtoVDjlOX6urq+jr60N9fT22bNkCs9ks6wkv0s8pEAiEWOFHGjrYSFCSmRNYr/eFVKsJjRKV0vui5BxKovIT2dnZqKqqogOpiHtyJBV/qZJDYScUBRFOUILBIMbGxugcdakaraQOeZHucqG/mRLs5V0uF7q7u1FVVRUyYyESNhIOJS2QJNRCStzVanVIJVJubq4i57grhURMSRXDcRzy8/ORn5+Purq6NRV/wu7+vLy8lBaUq/6EIiZRXzixoJC59CUlJXSOulRIFfISjhDu7OwMOZZL3ckeLcQuJ5reHCFKPaFshrj3xW63r9kJl5SUSDJHJFKUcEpaDyUs1uKKP4/HA6vVSjcFwWAQfr8fOTk5is6ZMUHZ7OIaDQKBQELil8IdvclkwvDwcEymidFeK1ZIZ/561VJyeW1tBvEJMxqN2L9/v+S7ciUvjmLIaFbhHHer1UrniJDel5KSEsX6WMlNMk4om5GZmRmyKRgYGADHcTRnRkrKi4qKFFVSLn4vHQ6HLOtXPCQ1h6JWq+H3+xMqKKOjo1heXpZlMSTEG/IiOZ2Ndv/JCHn5/X709fVBq9Vi//79ce08U/WEshFqtXrNHBFholiu3hclv19KOKFsBBnot2XLFhr+IiXlMzMz4HmelicrbZyCw+FAbW1tsm8jhKSeUIigJAK32w2n0wmO42T3Dos15MXzPMbGxrCysrJpTifRgkJM/mpqalBVVRX386VKDiUeNup9AaTro1AySjyhiBGKnkqlQmFhId3I+Xw+LC8v0960zMxM+rkl23GBhbzEF/9zyEtuSLw/MzMTDQ0Nsl8vlsXe6/Wip6cHBQUFEeV0Eiko5MQkpV2/UFCEr1Xpi0+sbNb7kp2dTU8v0cbxlfyepZqgiMnIyEBZWRkdq+FyuWC1WqnjQiKLMsTvIxMUrA15ySkoPM/j8uXLsFqt2LdvH9544w3ZriUk2pDXysoK+vr6sGPHDlpXvxmJEBTy/lksFkmr4ID0DHlFQzibd3HvS0lJCQoLC1OiR2I9lB7yAqK7x+zsbGRnZ9NTZ7iiDOKeLOXnRkakC3E6naxsOOTif56JIgcejwc9PT0oLCwMCXElYsekUqkicjYmBopzc3Nob2+PaiKh3FVefr8fLpcLPp8P+/btk3xRuBpCXpHCcRxycnKQk5OD6upq2vtiNpsxOTlJ576UlJQgNzc35Pur9PcqFU4osVqvhCvKWF5epicYUnJOwprxvA/h7pE1NoqQ64RCQlyNjY00QQq8uQgnQlA2Oz2QhkCO43DgwIGoS0zlrPJyOBzo7u5GRkYGmpqaZLnG1X5C2Yj15r5MT0/TRSRVjC1T5YQiRYm3uDyZfG7z8/MYGhqCTqejApOdnR23PYzD4ZBkVIWUJDXkJXUOhfhcmc3msBYgiXABBjYXFIfDgZ6eHlRXV0Ov18tyjVghyceWlhb09fXJJsBC4RDPzLjaBUXMZr0vxPY9kb0vkcLzvOIFRa7vuPhzczqdsFqtdJx1fn4+FZjNNgZ+vz+soLATioB4hl6JIUnt/Pz8dUtaSfWV3Lu6jU5eZFhXvAluqX8AJF+yvLxM8yWJmOsCrH0tTFDWRxxm8Xq9eOONN2AymTA2NgatVouSkpKYTBLlIFV8shLxHSdhTWLpI3a8Fk6vFK9f4U4oLIfyZ8hCFcvQq3AQl9udO3fSaoxwJMoyP9zpQSonYzkQmk4KK8zI65Bjh7mRfT0jclQqFbRaLXbu3Akg1CSRzHAvKSlJWpNeKuRQkoG4PNnv99OmWLIxIM2VwpEfQpQo1imdlOd5HhMTEzAajRFNBUyWoJACgeLi4ridjKXGbrejp6eHmk4KkTP8xHIo0iB+r4QmicFgEDabDWazGTMzMwAS3/uSCjkUJaDRaELKk0lTLMmbZWRk0A14VlZWzL+Rc+fO4b777kMgEMCJEyfwwAMPrHkMx3E3APgOgAwAJp7n/1fEryOmu5KIeBZ4r9eL3t5e5ObmRuxymyhBEV6HuNSKCwSUwOLiIsbHx9edQ88EJTVYb4OiUqlo7wvw5gyRhYWFuHtfIoWdUGJD3BQ7PT0Nq9WKoaEh/Pu//zuAK5+v3W6PODEfCARwzz334MUXX4Rer0dnZycOHz6M3bt308dwHFcI4AcAbuZ5fprjuMj6GP5MUkNesZ5QyCIdTd8GkNgTSiAQwOTkJJaWlqKeqS43PM9jdHQUNpsNnZ2d64ZC5Ox1YWXD0hDNgr1Z74vQ4l2qUAoTlPjhOI7mxqqrq/Gd73wHL730Er7whS/gXe96FzIzM/H2t78dd911F2pqatZ9ngsXLqChoQH19fUAgNtuuw2nT58OERQA/xvA0zzPTwMAz/OGaO41pU4oxJjQYDDEtEjLOUlRCOkjyMrKimpGSCLw+Xy0eGGz8FuiTijChCNbfKIj1gU7XO8LsXjfrPclGpQe8kqVzYswh5KTk4ObbroJ3/3ud/Haa6/BZDLh5Zdfhtfr3fA55ubmQsZM6PV6nD9/XvywnQAyOI57FUAegO/yPP9fkd5n0nMokQoKCXHl5OTEvEgn4oRit9vR3d0NjUYjVv6kY7PZ0Nvbi+3bt6OiomLTx8stKKQXx2w2IysrCyUlJYqY83I1QgSEDJgjpcjhel+isRhR+glF6YJHEFen2u122ghdWlqKD37wg5s+R4QTUjUA9gF4B4BsAH/kOO41nudHIrnPpIW8gMjNIZeXl9Hf34+GhoaIFsL1kGs0L2FhYQETExNobm7G0NCQbNcREukPltxba2trxLXrcjZPBgIBjI6OoqqqCg0NDfD5fDCbzVhYWIDT6YTX66Xlr0qrZFESci3YmZmZqKysRGVlJe19MZvN6O/vRyAQoMn9cCWuQpS+YCv9/ghSzELR6/W0OAO4Mrhv69at4ofN4koi3gHAwXHcbwG0AVCuoBA2242SWeWLi4tRW5OEQ64TSjAYxPDwMNxuNx2ElYhddiR9IqRc2eFw4MCBA1EtznLZu6yurmJhYQE1NTWoq6uD1+tFZmYmtm7diszMTFitVpSWltIdslThl3QkUX1CpPelrq4Ofr8fy8vLtMQ1MzOTnl7EvS/shCINUghKZ2cnRkdHMTExgaqqKjz55JN44oknxA87DeAhjuM0ALQArgHwfyK9RlIFZaMvms/nQ19fH7KysnDgwAFJPnS1Wr1pnDFayOTHsrIyNDU1JfTHs1mfiNfrRXd3N4qLi9He3h71vckR8iInJTJ/Itw1AdAa/fr6eni9XpjNZhp+Ib0VqWA9IjfJyAFoNJqQuS8b9b4ofcEO19+hRKSYJ6/RaPDQQw/hpptuQiAQwPHjx9Hc3IyHH34YAHD33XeD5/lBjuPOAegBEATwCM/zfRFfI6o7kojNFjbivrt9+/Y1vRHxIPUJhXiG7dq1i8aeE8lGJ7zV1VX09vZu2uy5EVIKCqkss9vtOHDgAKampiKu8tJqtSHhF9JbQeaKkNNLXl6eonfDcpHs1yzufREOqHI6ndBoNHQDoTRxUbrgEcSCYrfbY+qSP3ToEA4dOhTyZ3fffXfI/+Z5/lsAvhXLfSoqOE3qrRcWFiQJcYmRSlCEY3DDeYYlivUEZW5uDtPT09i7d29c1gxSJcj9fj+6u7uRl5dHT0ob9aFsBMdxyM/PR35+Pp0rQuwrbDYbcnNz6elFSW4EcqG0kJK4A7y7uxs5OTmYn5/H6uoqdDqd7L0v0ZCqgqJEHy9AIYLC8zwdL5uZmSlZiEuMFGXDwlBcvGNw40W84JNcjsfjobmceJDihEKci7dt24bKyspNnzvaa2ZkZKCiogIVFRUhyeO+vj4Eg8GQ00sqLBzRkgplr2VlZdDr9Rv2vhQVFSUl9MQERVqSHvJSq9W0A1S86EhNvCcUUnYbzqYkGQirsDweD7q7uyXN5cR7QjGZTBgeHkZLSwvy8/ND/k6OTnlx8ph0hpPdcU5ODhUYuafrJRIlnVDECAdDiXtfAoEAVlZWYLFYMDExAY1GQ08viSq+YIIiLUk9ofA8D5/Ph8HBwbjDM5EQT9nw/Pw8Jicnoyq7lTscQaqwSM5JanuXWBd3Up1nMBiwf//+sIt3IixWxJ3hDocDZrOZ2r4XFRWhpKQEBQUFKbGohENpIS8xG9nXq9XqTXtf5A5fpoqgiD9nh8MRVwuFXCRNUEiIKxAIoKOjIyE2zLGcUILBIAYHB+Hz+aIqu03EMC+VSoXFxUWYTCZZck6xLPqkWVGlUm0YEky0lxfHccjNzUVubi5qa2upuyuZ/0J8rUpKSpKWE4sFpYe8ovkNrNf7QsKXQmsYqUQgEfORpCCcoLATyp9xOp24ePEi6urqoNFoEvajiFZQXC4Xuru7UVlZiZqamqjEgZyG5PqyEqsMknOSI/4cbcjL7XaHvF+bkUxzSKG7qzC2PzQ0BJ/Ph8LCQjrTXekLjpJPKLGeANbrfTEajZv2vkR7f6lQNiyGCYqA7OxstLW1IScnBysrK7LNlRcTjaCQ+P/u3bvpKNZokNNChCzcGRkZ2LZtm2w/iGgWd+Jm0NTUREegSvXcchMuti9u3PN4PHC73YqoTBKi9JAXII3gbdb7kp+fT5P70fQmpUrIS/wextKHkgiSIigqlYq+GYlyACbX3WwRI5MLrVbruvH/SJDrdRGn5aamJpjNZlkX5UgXfVKmHE3YbT1bFyUIjXg2uNPpRF9fHyYmJjA6Ohpyekn27jYVBEUONup9AUBPL/n5+Zs6SSjd2ifc78HpdLITStgbiHPIlpQIZ6zs27cvrp2L1CcUnucxMzOD+fl56rRssVhkF5TNrHGGh4fhcrmiLlNOpXkoOp0OOp0O27dvh1arxcrKCsxmM8bHx5GRkZHUkbtKe6+Sgbj3hfQmzc/PY2hoCDqdjn5G4vxYIBBQfL9SuFMUC3kJEJcNJ+qEshErKyvo7++P2Il3M6QUlEAggMHBQfA8j87OTrorltuZd6MTnc/nQ3d3NwoLC7F3715JbV2UvEiKK5NI6OXy5ctwu90oKCigp5dE7XyvxhPKRoh7k5xOJ3W18Pl8KCgooOGxVMihhLOHiWawViJJ2glFOFc+2SeU2dlZzMzM0LyOFEgllG63G11dXdi6dSuqq6tDFg+5BWW9RZ+MDY5HfGPtlFca4tALOb2QvgqyM87JyZHltV2tIa9IEebHampqaO8L+Yy8Xi88Hg8yMzMVazwaTlBYyGu9G9Bo4PF4knLtQCCAgYEB8DwveaWUFIu9xWLB4ODguoUBiRAUsSgajUaMjIygtbU1rh2SUFCEP2IlhrwiRaVSoaioiH5WHo8HZrMZk5OTcDgcyM/Pl9ySnwlKdIhPmIODg9BqtSHGo+TvlRIKW09QpG4TkIKkC0qiQ14kL0AqpfR6PfR6veQ/yngWe9IYuLS0tKFXWCJDXjzPY2JiAmazGZ2dnXH/2DZ6v1NVUMQQS/6tW7eGJI6ZJb9y4DgOZWVlyM/Pp8ajFouF9r6QuS/JbH4NJygkuqM0kh7ySnRSXq1WY2lpCePj49izZw8KCgpku04siz1pDFSr1ZtOplSpVPD5fPHc5oaQzygQCKCvrw8ZGRlxFyuInzvcn6cjwsSxlJb87IQSH8KEt9B4lPS+CJtfSe9LSUkJsrOzE/a+iwVFyRuuq+qEwvM83G43ZmZmJNllb0QsNi8ulwtdXV3Q6/Uhs5/XQ+7wkEqlgsfjwcWLF1FVVRXRPUVDqlR5yYFUlvxMUOJjoz4UYfMrANr8OjY2BrfbTXtf5J4qut7MFiV+7kkXlGjmyscDGTalUqnQ3Nwse3w02nAUqUJpbm6m5Y9SXyNaHA4HHSUQS3PnRgiFQ7woXg2CIiQeS34lv1dKvjdCNAO2SPm4Xq+nIUxyyuQ4LuLel3jvUcnva9IFJRFVXqSLe+fOnVhYWEjIeN5IQ17C2SrRNlLKKSizs7OYm5vDli1bJBcT4OoLeUVDNJb8gHLfs1Q4PcXaKS/uffF6vdTZemhoiDpbh+t9iRaxoHg8HsX6zSU1hwLIG/ISNgOSLm6DwZCQE1Ek+Q1ikKnVamOarSKHoASDQQwNDcHr9aKxsREWi0XS5yekUmNjMtnMkl+tVlNrGKVZ8qeCrYlU96jVakM2AQ6HY403HDG2jDaZLm6+tNvtiqzwAhRyQpFjgff7/SHJbfIhJipns9li73Q60d3djZqaGlRVVclyjWghYcGSkhLs2rULVqtVtsVdLBzC3SwTlPURW/JPTU1hZWWFWvKTXbESLPnT+YSyEUJna9L7sry8TL3HyNyXkpKSiPqTUmUWCqAAQZHjC+dwONDT04Pq6mro9fqQv0uUoGwU8iK9HPFWmUm5myfDwxoaGlBeXi7584shzy0eB6z0BUhJcByHzMxMFBUVoaamRnGW/KlwQgHk/86JveHE/Umb9b4wQYkAuT7ExcVFWhIsnhIIJPaEIr4Oz/MYHx+HxWKRpMpMqhPK0tISLl++vGZ4mNxJ/3CwkFf0kN+S0iz5U+GEkgyE/UmR9L6EExQW8pKZYDCIkZEROJ1OdHZ2rlvLn6yQl9/vR29vL7KzsyXr5Yh3wSfOysvLy2HfMzkX9418wpigRM56i3Y4S36r1RpiyU92zXJZ8qfCCSXZ37XNel+ysrLg8XhQXFxMP+tYTijnzp3Dfffdh0AggBMnTuCBBx5Y7346AbwG4EM8z5+M9vUoRlDi2c2QeeqlpaVobGzc8HlibTiMFuF1HA4Huru7UVdXh61bt0p2jXgERShwHR0dYX/4cp8WWJVX/ET6+ajV6pB5IuT0MjIyAo/HI4slPzuhRE+43pe+vj7Mzs5iaGgIP/rRj1BbWxtV82sgEMA999yDF198EXq9Hp2dnTh8+DB279695nEAHgTwQsz3H+s/jJdwjsOxNAcRv6tIBzvJ3V0uvE4gEKA7jZaWlrAhuHivEYugRFoQIGfIi4iVw+FAX18ftRgvLCxM+q4x1Yhl0Rb2VAgNE6W05E8FQVH6/el0OmRmZqKpqYmeTh5//HFcunQJ58+fx7ve9S7ceOONOHDgwLqv5cKFC2hoaEB9fT0A4LbbbsPp06fXCMr3v/99ADgFoDPW+1XECSUWQSH9GwaDYUO/q/WuJTccx2F1dRU+n0+2rvxIBoaJIQIcSQOl3El54qTc2NgIv98Pi8WCiYkJOJ1OzM7OyhqOSRd4no87rCSXJb/SQ16psnEhORSNRoObb74Zs7OzeMc73oHbbrsNv/71r3Hy5Elcc8016/77ubm5EJcLvV6P8+fPr3nMM888AwAPI9UFhfh5RVpHT8I1mZmZm/pdiUmEoPh8PgwODiIYDGLfvn2y7YI2G4AlZnp6GgsLCxELsJyCsri4CJvNhre85S30OmRBe+211wAAIyMj8Hq9KTXfPdHI8flIZcmv9BOK0u+PIE7KO51OVFVVoaysDLfffjtuv/32Df99JKHl+++/Hw8++CCuvfbaQDzviaJCXpFAyltjzUfILShkVkhNTQ3m5+dl/cJGGpIKBoMYHBxEIBDA/v37I46Ry9U4OTw8DKfTiaKiImi1WrjdbnodlUoFlUpFXaDF892zsrJQWlqKkpISxTXyJQu5v2PrWfI7nc4N/ayUfkIJBAKKvj+CWPicTmdUc5v0ej0djQxcccEQr52vv/46brvtNkxNTU0CKAVwiOM4P8/zz0Zzr4o5oUSyyM/Pz2NychItLS0xz+KQU1BIyXJLSwt0Oh01+pOLSBZ8UrBQXl6O2traqBYfqU8oZMoj6ZsYHh6G3++HWq2mn8vq6io4joPX64VarQbHcbQaSTh9b2BgAIFAAEVFRSgpKUF+fn5KLA5Sk+hddjSW/Eo/AaTCtMZwRDutsbOzE6Ojo5iYmEBVVRWefPJJPPHEEyGPmZiYIP9vHcdxjwF4LloxARQiKJv5eQntQA4cOBCXs6ccgsLzPEZHR2Gz2Wj5Lc/zsleTbbbgr66uore3F42NjbS6J9rnl+o1OJ1OdHV1ob6+HhUVFfB4PHA4HOjv76cnjuXlZdpDpNFoEAwGEQwG6eelVquRnZ2NmpqakEa+xcVFDA8PIycnh4qPUoYjyU0y8wCbWfJrtVpoNBr4fL6oqpIShdJPUASxKDscjqhOKBqNBg899BBuuukmBAIBHD9+HM3NzXj44YcBAHfffbdk96oYQVlvkSeDsCoqKrBr1664dzxSlw37fD709PQgPz8fHR0d9P4SsTPb6BoLCwuYmJjA3r17Yx5rHEvSPxykEIA0m5Kd4bXXXgu73Q6j0YixsTH4/X5UV1fTOTlE0ITCQna9JDQmbOQTmiiSnEwkFvCpjJJOAWJL/unpaZjNZvT09ACI3JI/UaSKoIiJZfzvoUOHcOjQoZA/W09IeJ6/M9Z7U0QOZb0hW8TSfdeuXTRhGy+xzClZD5LPiWe2utQIT0vxnuakCHnNzs5idnYW+/btQ2ZmZogoEM+jmZkZlJSUoL6+HlarFZOTk7Db7SgoKEBpaSm1pAgGg/TkJxQZIjBiE0WhBXxeXh5KS0tRVFSkyN1yPChhcRbDcRy1fgn3eWxkyZ8oUkFQgsFg2BNKPOO35SSpJxSyYIlPKMSixGw2R23pvhlShbzICUBsV5JM/H4/enp6kJubG3JaipV4BIXneQwPD8PtdtNKPLJpID9ir9eLnp4elJeXo7q6GhzH0R0uic8bjUZaXUQa80hvBBEU8n/J56pSqaBWq0PcX202G0wmE431k9BYJOZ8SkZJJxQxwgU7Ukt+qWeJRHp/SiXcvJZoQ16JRBEhL41GA6/XC+BKCKm3txc5OTkxWbpvRryVS0KLl3hPAFJCmhXr6upQWVkpyXPG+sP2+/3o7u5GQUEB2traEAwG4ff76akEuPKjIKc70hUsRDxvwu12w2QyYXR0FG63G4WFhSEnDnJ6IQIDIOT0QuwthLF+YaUSea5US9IquZci3O4a2NiSn8wSIacXOSv5ohmulSzWExR2QtkAkpRfXV1FX18f6uvrsWXLFlmuFc/uh9i7FxcXo729XTE7Q5PJhOHh4bjdi6VAKGxbtmyhoSmhmJjNZoyOjmLPnj0Rn+6ysrJoKXEwGFzjS1VWVobS0lLaXyPMuQQCAXo6Ig1iwpOQsM+CdImXlJQo1oBPjFK+h2IibboUW/I7HI41lXxyWPKn6gnF5XIptuFXMSEv8qNua2tT5HGOVEzt3Lkz7I56PeQMSfA8D4/Hg8uXL0seGowFq9WKgYGBkOQ7qfUn78HMzAwWFxfR0dERc+xcGLICruzYTCYT+vv76UyQ0tJSugCFO734/X6a2Bf2WbjdbpjNZjo3nDRVKvUkoOSQVyxd/MJZIrW1tbJa8qeqoABQ7H0n/YQSCAQwNTUFp9OJt7zlLYoJIQmZm5vD9PR01BVTpEpKjh98MBhEf38/gsFg1G4BcrBe8p2ICQkV+nw+dHR0SBpqIK66ZAGyWCxYWFig4ROSeyECtlHuRavV0i5x4nFlMplgtVoxNDSE8vLypM0XCYeSBSUYDMb9e5bTkj8VBUWpGxtCUldv0ptAdppKExPS1e3xeNDZ2Rn1/ZGKMqm/tKSUesuWLbQRMFnwPE9zSqQLX1zJ5fP50NfXh4KCgk3doONFo9GEhE/sdjtMJhO6u7vB8zwVF1K6ul7uhQgh6QL3er3YunUrHA4HXcxIU2UypyMqeYGRWuw2s+TPysqip5dIQkKpKCgEpW4ikrqCz83NoampCdnZ2ejr60votTf7Mgkt8YnTZ7TIYZW/srKCvr4+6q68sLCQtI5fUlWWl5eHvXv3hiTfyXvrcrnQ09ODurq6hJdWC5O/27Zto8n4qakp2O12mowXNkIKTy/Cpkqe55GZmYni4uKQxYyEYohbcjKaKpW6uMi9YMdryZ8qSXnhe6jkDQSQZEHZuXMnTZhu1CkvNWShX+/LThbtWDvMCVL2vABXrGempqbQ3t5OE8YkR5DoH4bL5UJXVxdqa2tpclucfCdhot27dye9WABY23hHwlmTk5PU9LC0tJSWEpPX5HQ64XA46GmLnF7IYiZMJCe6DFbJIa9E31sklvzk9EI+X6U7KohFT8kJeUABORQgcZby4uuFC2HNzs5iZmYmZNGOFanMFYVhJXHoLRljeknyvbm5GQUFBWHFZH5+HrOzs2hvb1dMvkEIx3G0LLmhoYEm4y9fvkyNK0tLS6HRaDA4OIjdu3dDp9OFnFqEZcnCRDIpg52bm8PQ0BDy8vJoGazUTZVKFpT1yoYTwXqW/KTYoqCgICqH82QhFhS73a7IoiWCIgQl0V+6cAJGHHn9fj8OHDggyY5fipAXsXYpKCjA3r1717xXiRYUckrq6OhAVlbWmnwJGSvscDiwb98+xYcUCFlZWSGW7VarFTMzMzCbzSgoKIDdbkdWVhays7Np34uwLFncVCnM49hsNpjNZszOztK8DDFQVKoYSIEUs1qkIpwl/+XLlzExMYGFhYWILfkTTSAQCDlFxTL+N5EkvWw4GYgFRegXFq0j70bEG/IiVvgb9eVI5be1GcTSxW63o7OzM2zyPRAI0OmLra2tivphRoNKpYLL5YLf78db3/pW+P1+mEwmDA4Owuv10tAJqSyKtKmS5HGIO68wj1NUVBRTUYqSTyhKvTdSKk6sfXQ6XcSW/IlGHM5Wcpc8oJATSqIRnhxI+CbSEcLREM/pwWg0YmRkBK2trRt2xUrpCLwepEpLp9Ohvb2dJt+BN+vh3W43enp6oNfrY5pToxR4nsfY2BhcLhfa29uhVquh1Wqpw3EgEIDFYglxOBbPZwl3eiH9VhqNBlu2bKFNn6urq7RQQK1WhzRVRrIYK3XRBpRfRUXuL5wlP3FNFlvyJ/q9Foe8mKBEQaJ+HGSu/PT0NObn59HR0SFLoiuWkBcZbWwymSIaHSx3yIvnebz++uuoqamhPzhxvmR1dRX9/f1oamqiDYKpSDAYRF9fH7Kzs9HS0hL2u6hWq9c4HJtMJvT29iIYDNLEPknGb9ZUKbSXIcOrxsfH4XK5Nq1SApRd9aNksQPCV3mJPxOxJb+c+bBI7pGFvDZAPLUxUdVKHMdhYmICWVlZNHwjB9GGvEjIKCMjA/v27YtodyenoCwvL8PpdKK9vR0lJSVhxWRpaQmTk5Noa2tLGauScJDhXxUVFSHztzdCXJbs8/lgNpsxMzNDHY7LyspCHHU3aqrMyMgI2SkvLy9TgdFqtSFVSuL7UCKpckLZCHFloDAfBshvyc8EJUaIn5fcguJ2u7G4uIjS0lI0NzcrYkQvua+uri5UVVVFvKBFe41oWFhYwOTkJHJzc5Gfnx82+T45OQmr1YqOjo6UtoR3Op3o6elZ16gyUjIyMmg4i+d5rK6uwmQyYWpqCiqVipYZi8uSyX/CWS/CpkrgSpWS2WzGyMgIvF4vbapMZiXVZij9hBKt4HEcF5IPS4QlP6vyipFElA6TQU/l5eUoKCiQ/cse6WJP8ji7d++OOmQktaCQHMLq6io6Oztx6dIl+P1+ZGRkUDEJBoMYGBiARqPB3r17Fb0L3YyVlRVaAp2fny/Z83Ich4KCAhQUFGD79u3weDwwmUy0LJm4JQtnvQAI21RJJlUSc0xhU6XFYoHP50NFRUVIHkcJpJugiEmEJT87oUSBeMiWXILC8zympqawtLSEffv2wWg0JqTvJRKRJB5YseZxpKzyCgQC6O3tRVZWFjo6OgAABQUFuHTpEs0N6HQ69Pb2RhUaUioGg4FOtZS7WSwzMzOkbHV5eZkKjFarpW7J5D6EoiIOjQmbKoWWMEJ3XmEeJ1mkQ8grUuSy5BcLitPpjKvZWm4UdUKRo1s+EAigv78fKpWKmiiq1Wp4PB7JryWGJP/DIfYJizXUJ1WVFwm56fV6uugFg0HU19ejrq6OlrqaTCYUFBQoelZ4JExPT8NoNCYlXEcqh4ThLGFZMnFLLiwshEajCZlUKW6q5Hme2r7U1NRQc0yykCVzMqKSw3HAWlsTKZHKkl8seqzKK0LkCHmR2Rx6vT5kNy2Hx1Y41gtHCeeqxOoTttk1ooFYzezatQtFRUVrku9qtRocx8HlcuHAgQPgeR5GoxGXLl2iuYGysrKIS12TCXEd8Pl8aG9vV8QOOjs7G9XV1dQjzGKxYGlpCcPDw9DpdPQ0kpmZGdJU6fF44HQ6wfM8vF4v1Go1VCrVGnNMs9mM3t5eAKBilYgSWCU1Nq5HIu5PSkt+u93OQl7rEclc+Vgh8+ibm5tpCSBBOI5WTsKJJJlD39DQgPLy8rivEa+gLC4uYnx8HO3t7cjOzg5byTU9PQ2DwRAywyQ/Pz8kN0B6N0i4paioSHGLCamiy8nJwc6dOxUpfuKyZDLrhZQlFxcXo6ysDGq1mpqE6nS6dZsqxWEYYQlsfn4+Pb3I0cCn9BxKskquo7HkDzdPPlpBOXfuHO677z4EAgGcOHECDzzwQMjfP/7443jwwQcBAL29vX8A8Eme57tjem2x/CM5kOqEQqqPjEbjukOnknVCWVpawuXLlyWdQx+roPA8j/HxcSwvL1N/MHElFwnLBQIBdHR0hBUIYW6AJItJU6ZOp6O5gWSb8JFT4datW1FVVZXUe4kU4c6WCILFYsH4+DgsFguKi4vh8/lCTA43s4QRV6EJG/iEeTIphEDpORQlsJklv8vlwuzsLAoLC5Gbmwun0xnV2hEIBHDPPffgxRdfhF6vR2dnJw4fPozdu3fTx2zbtg2/+c1vUFRUBI7jvgrgPwFcE8vrSbqgkDiwFCcUv9+Pvr4+aLXaDefRJ8qMUmjLMT4+DqvVis7OTklj9rHYu5CdularpaOMxZ3vPp8Pvb29KCoqQl1dXUQLjNBOnOyujUYjuruvbHbI3yW645jMr9+xY4fkbgiJJCMjAxqNBh6PB9dddx18Ph9MJhOmp6dDEvXk/V2vqZKEMEkVWn19/ZqmyoKCApSUlKCoqCiuUn4ln1CUiPg3dOHCBQDAo48+ikcffRSFhYV44403sHPnzogKSS5cuICGhgbU19cDAG677TacPn06RFCuv/564T95DYA+1vtPuqAQ1Go1vF5vzP+e5Etqamo23YEmSlBIoUF3dzetnJJ6x0YEOVI8Hg+6urpQWVmJ6upquqMVhrhIX0Z9fX3MYTnh7lo4i2RiYgIOhwOFhYUoKyuLe8HajOXlZQwODmLPnj0bWtikAgsLC5iZmQkJPQrLkoXvb0FBwYZNleKyZHFTJbF+n5iYCLF+T+Xm1VQjEAggIyMDer0en/rUp3D8+HHceuutOH/+PP793/8dZWVluO+++3DzzTev+xxzc3Mh+WO9Xo/z589vdNmPA3g+1ntWjKDEUzZMQix79uyJaO5GogTF6/XCaDSiqalJtjBLNCGv1dVV9Pb2oqmpCcXFxRvOMJG6L0PYcUzKZo1GI520R3ZlUlrdky5+pVroRwOpsOvo6Aib7xD7UZFZL6Qsmby/RBCEDZXCUCcpSy4qKqI9UaSpcnR0FB6PhzZVxjJ2Vyko2bKGIC4ZzsnJgd/vx7/9279Bp9Nhampq02rVcK9zvVPjK6+8AlwRlL+I9Z6TLihkhx1L2TAJJVksloh8rwiJbKLMy8uTNWYfqaCQ/M3evXvpXA+xmMzNzWFubk72BVhcNksSz/39/QgEAmv8sKKF9B1ZLBbs27cv6Y6x8UAaTd1ud8RNpMRNt6ioCDt27KBlyaRMXVg4IS5LFooMERhxUyXpoSGbAXJ6SSXRToUKtHBlzR6Ph+aFa2trN30OvV6PmZkZ+r9nZ2fDmrf29PTgxIkTAHCE53lzrPesmF9atIu83+9Hb28vsrOzI/a9El5LzqQ8MZ1sa2vD6OiobNcBNhcUnucxMTFBRTdc8p0sWk6nMykzTEhSkgynEvph5efno6ysDCUlJRHdF8/ztJAg1bv4yYwejUaDPXv2xJyPEJcliwsniIATQdjIb4zjOCogPM/T0wuZJSTsDlcyqVAwEM68Mloh7OzsxOjoKCYmJlBVVYUnn3wSTzzxRMhjpqencezYMfzkJz/B9ddfPxLPPStGUKJJyjscDnR3d6Ouri4mq3SpR/MSyAIQCATQ2dkZEqeWi40EhbjnajQadHR0hE2+k0KG3NxcRcwwEfthrayswGg00li+uKNcCOn0J15LyX4t8RAIBNDT04OioiJJZ/SIk75OpxNGoxH9/f3w+/1UXIg10UZNlSqVio7dra6upv0VxNrf4/HQ4VXJrvITkwqCIjbLjSVMp9Fo8NBDD+Gmm25CIBDA8ePH0dzcjIcffhgAcPfdd+Of/umfYDab8dd//dfo7u7uAuDneX5/LPfMbXKTsgca/X4/AoEA3G43+vv7sW/fvg0fT5qBWlpa4toF/eEPfxBXN8SF1+tFV1cXysrKaFVUMBjEhQsXcO2110p2HTHkB7xr166QPyfJ9y1btqC2tpbuOIUhLjLDpLq6GpWVlbLdo1SQ0I3RaITP56M9GQUFBfB6vejp6UFVVVVKz2MB3ixxTvRr8fv9MJvNMJlMWF1dRW5uLhUfUpkozLkQsQFAK8fIIs3zPM6fP48tW7bAYrFQa385nXmjwe1203lDSsViscBisaChoQHAlff04MGD6OrqkvOycX0wKXNCIaNlSd+EknY8JNm9c+fOELfaRAy/CncNm82Gnp4eNDY2oqSkJKyYEFPEXbt2rWn8VCrC0A2xGJmbm0NfXx98Ph+qq6vjcgtWAmR6aH19fcJfi0ajCTE7tNlsMJlMdAGLtCyZzHpRq9Woq6sL6aEhzryJnisiRk7bFakQh7yUbmUDKEhQNsqhkJ6InJwc7Nu3T1FvKuk037t37xqPnUQNCxMKisFgwNjYGNra2pCTkxM2+b64uIipqamEmCLKhUajQXl5OTQaDVZXV9HQ0ACHw4FLly6FhHWU7Hskxm63o7e3VxEiL7Rqr6+vX1P2TcbnrleWTCxhfD4fFRexWJFcGSnSEFr7y00qhLzCGUMqvWw76YJCvjzrfYkimaueDMQ278kySRQ2T5JJj/v370dGRkbY5PvExARWVlZSvvoJCO3LIJUv27dvh9vthslkwujoKNxuN4qKilBWVqboMlfSL9PS0qJIryZx2TcpS56YmIBGowkRcKfTiYGBAezcuTNkw0NOBeK5IkSsJicnqViRpkq5vqOJGuYXD6k2CwVQgKBsBNn9t7S0yNKUFqvXkN/vR09PD3JycmiyO1mQAoO+vj5wHEdPcERMyAIaCAQwMDAArVaLtrY2xS6skUDEc3l5OWxfRlZWVkiZq9BsMScnh1aNKSVsSmz0U6VfRliWDCBEwB0OB3w+H+rr60P83DZqqtRoNCFiRSxhJicnqVhJ3VSZKicU4Xc0WtuVZKBIQeF5HqOjo7DZbLLt/kmILdodEOnIr62tVUTyl1TWbN++HbW1tWt6CIArCfqenh5s2bIl5WeYBINBDA0NAUBEwrjeDHihHUxZWVnCQi1i5ubmsLCwkNJTL4mA5+Xlob+/Hw0NDbDb7Th//jyys7PXNK2K+12A0NMLmelOTptms5n24giNE+M5YaSKoLATSpx4vV5a+inn7j8WU0XiYBxpR77ckHBgdnY2FRNxvsRms6G/vz/lfayAN3uPYi2lFc+A93q9IVMUSWgsEU7J5JS1srKC9vZ2xYdfNsNqtWJ4eJi6VhOETaukT4WUJW8260WlUiErK2vNYDKz2YzLly8jMzOT5nGizQWmoqAofRYKoABBES4KPM/j4sWLaGhoQEVFhazXjaaRUjjxcT0H483+vdTCaDQaMTo6iubmZoyMjIQVE6PRiMuXL6OlpUXxX8TNICXONTU1kuXStFptiF0JcXklDX/COSRSQpovg8EgWltbFb+wbYbRaKQjEMTvlbBplVTmLSws0OFfJJyl1WpDZr2s11QpdFhwOp0wm80YHh6Gz+ejljCbDa0CwjcNKo1UG/8LKEBQCAsLC3A6ndi/f3/Uc9VjIVJBIfPTAdCJj9FATkJSfXmJuBkMBuqo7PF4sLKyQuv7eZ4P8X5SSq4gVkj1U1NTk2zfDWLfTjrAScMfmUNCxCXeHgrSbKrT6bB9+3ZFVSzGwuLiImZmZtDe3r7p94xU5gmHfwndqElTJXmPw5Ulr9dUSRwAhEOryOcZbkOQqicUJiibQGLiTqcz6nnL8RCJoAibA2tqamL68UspKETceJ7H/v37afK9oaEBU1NT1MXX5XJRa3ql/2g2g5gStra2JuyUJZxRQXooTCYTpqamYLfbQ5x8o/lcifN0eXl5yueygCv5n8XFRbS3t0edixSGH4VlyeQ9zs/PDzm9AOFnvRAfQKF9v3Dkbn9/Px1MRixhiFgp/bfBQl4x4Ha7odVq0djYiN7e3oS4AAObCwoZi9vU1BRX7kEq3zDSPV1aWoq6urqQ5Dup7/d4PLQPg0yGVMqAq1iYn5+nZpWJ2miEIyMjI2zJrDCOX1ZWtmGFlsfjocUccodzEwEx39y7d68kmyVhWTKx3DGZTCGVXsLhX5s1VYpH7losFszPz9NQGwBF5EE3IlxSnp1QNiEnJ4cOf5F6DPBGbCQo8/PzmJqaQnt7e9ylilL4hpHke0NDA8rKysLmS8gQKTLDRDzgiuzgSEWTkiEu0jabDR0dHYqKdYudfJ1OZ0jSuaSkBGVlZSFOyWS+zM6dO2n8P1Uhn43D4ZCt/FxY6dXQ0BBSliweMx2uqVKce1GpVGtCbWNjY5idncXS0hINjSV68NtmhGtsVFIvXjiSLihCEjWnZL1r8TyPkZEROJ1O6swbL/HOfCe242RscDgxsVgsGB4eDhkiJR5wRWa/k2Y/Eq8ON7c6mRCDTbVajba2NkXdWzh0Oh1qampQU1NDvbBmZ2exurqK/Px85OTkYH5+Hnv27FG8A+9mkN9HIBBAS0tLwj4bYV+RsHhibGyMnhCFhqHr5V5IYj8vLw/5+fkoKChAfn4+LBYLpqenaaiNWMIku/FXHJZjIa8IEH4p4xmyFS3ihd7n86Gnpwf5+fnYu3evpO6usQrK1NQUFhcXsX//fmi12jWd78CV+Qakj2GjsJB49ruw2iYvL482+yXzR0QsdkpKSmLOWSUTsRfWzMwMJiYmoNVqMTo6SsOPSrfPCAfP8xgYGEBGRgZ27dqVtM9GWDwBgJ4QBwcHqWEoKUvWarUhgiLsffH7/eA4DlqtNsTdenV1lY5VVqvVIZMqk/GahddkSfkoiWXIVjzXIuJF7PDlsHeJJeRFChX8fj86OztDOt+FNiojIyPweDxRh4XEzX6rq6swGo2YnJykFvGb5QSkhpgi1tXVpUWOYWlpCYuLi7j22muRmZkJt9sNo9FIbd2FTslKTw6TyjRy2lWS0AtPiIFAAGazmW6UcnJy6OlFGBqz2WxYXl5GVVUVfD4fgDfLkgsKCmhuhYxVHh8fh8vlCrGESUQYVuwEz3IoESKc2pjIkJfP56ODhuK1w1+PaENePp8P3d3dKC4uxrZt28DzfNgZJqT5c+fOnXH9wIU/ooaGBrhcLjofg0xPLCsrk9Vy3Gazoa+vTxGmiFIQblxvVlZWyJCrcP0YQpt4pUDmspBTo5JRq9VrciXEFYHneXrSmJiYQGtrK3Q63YZNleuNVSanTnJ6SZTBKrNeiRKNRgO3252Qa6lUKphMJhgMBlnt8KMJeZGT0vbt21FeXh42X+JyuWiDnxwzTLKzs+mOj0xPJKWchYWFtFxWql01iYW3tbWlZChICBmx4HQ6N5wWKT4hEpv4S5cuQaVS0eKJZIVZCH6/H11dXXRRTSXErgg+nw+zs7MYGhqCVqvF1NQU7bLPyMgIaapcryxZ6F9GJlWSKAFpqpTKgDRcMzQLeUVJokJegUAAMzMz8Pv9uPbaa2UNOUQa8iK2LsQIM5yYEEfaRO3khdMTie2F0WjE2NgYsrOz4y5JFuZ/UrGsWYiwmCCahLXYJp4UT4yNja2paEpkaIwMjEuXMmeXy4XFxUVcc801yM7OpqeNqakpKuJC+/zNmiqzs7NDDEiXl5fp57ZZU2UkMEGJA2HIS25Bcbvd6OrqQmFhYdTzmWMhkpDX9PQ0FhYWaPI9nJgsLCxgeno6aTNMyMyK4uLiuEuShTt5pZUFxwIZPVxQUECndcaKsHiCVDQJ578noq+I5LO2b9+O0tJS2a6TKFZWVjA4OBjy2yFlyQCoiJPvZGFh4bqzXoSnF+DN3IvYZcFsNmNgYACBQICeXvLz8yNeb8JZwzidTllc16VEEYJCkLvKa3l5Gf39/di1axfUajVmZmZkuxZho5BXMBjE8PAwvF4vtVEJl3wfHx/H6uqqYmaYbFaSTBLO4UqSg8Eg+vv7kZmZmdDSU7nw+Xw0LFRVVSXpc4vtYIQiDqydoCgFpGemsbExIRZIcrO8vIyhoSG0tbWtuxETizg5bQgbVzcrSxY2VRKXBVJKbrVasbCwgOHhYeTm5tKy5I02BeEExev1JrXBNxKSvzoJkDMpPzc3h+npaXR0dCA7Oxs2m0328bzA+iEvknwvKipCU1MTAKxJvgcCAfT39yMrK0vSUmapibQkmed59PT0pI31SCLH9YpFXDxBUbirjvXERzzTmpubU75nBnjTAXnv3r0RVywKT+JAaFmy1+ulZcmFhYURN1WKxyeYzWb09vYCALWEERe9hBMUcdWXElGEoJA3Uo5OeXIK8Hg8Ic2KiaooU6lUtDSR4HQ60dXVhfr6elRUVIQNcZEZJpWVldDr9bLfp1SsV5JMSi+3bNmS8nPfgeSP6xVPUBTuqrOystbMINmMlZUVDAwMKHZiZLRYLBaMjIxEJSbhEJclC4e1kRCkMFcinPVCTjIk7yIsFCAecRaLBTMzM7Db7cjLy6ObArGgpIKYAAoRFILUi7zX60VPTw89BQh3AIkSFHHIy2KxYHBwkHZOrzfDpK+vD42NjSlt1UFKkoErFud79uyB2+1OaEmyHChtXK94Vy2cQULe59LS0hA7GCHCxTcZ+TmpIQO5pPaAE2+WyPtMHKmF77N41gv5Txgay8jICGmEJZMqp6enqaGscKiWcI2IhHPnzuG+++5DIBDAiRMn8MADD4T8Pc/zuO+++3D27FnodDo89thj6OjoiOs9SltBEfpflZeXy3qtjRCGvGZnZzE7O4t9+/YhMzMzrJgYDAaMj48n1F1XTshMFuFilaiSZDkQzv5Q6rhe4QwS4S7YZrMhPz+f7qrVavWGs0xSEXJKi8ROPx6EIUhy2jCbzfR9JqeNkpIS2lsk7NQPl9gn/WD19fVYWFiAwWDA5OQkHnzwQVq6HKn9SiAQwD333IMXX3wRer0enZ2dOHz4MHbv3k0f8/zzz2N0dBSjo6M4f/48PvnJT+L8+fNxvS+KEBSymKpUKkmOdgaDAWNjYxvOok+0oAwNDcHtdtOZKuGS71NTUzCbzdi3b5/iGtxiYWZmBgaDIezrkbskWQ7m5uYwPz+fUuN6xbvglZUVGI1GTExM0MWtra0tLcSEvC65xSQcwu+z2MKFFFeQKshIJlWqVCoUFhaitrYWjzzyCJ5//nl85StfwQ033IDi4mIcOnQId95557qOyRcuXEBDQwM13r3ttttw+vTpEEE5ffo07rjjDnAch2uvvRbLy8vgOK6S5/mFWN8HRQiKVJCKKIvFQktw1yNRIRae57G0tAS9Xo+2tjYAa5PvpIeB47i0mGHC8zxGR0fh8Xgiej0blSQDoGGGZJ3YyLje5eXllC5zFrr4zs7OYn5+HpWVlRgdHYXX66WLXkFBQUqFIIErm0jiEJ5ssReeNrZv3x5i4SIuoFivqdLlcgG4IjIZGRno7OxEQ0MDzp07h+npaTz//PMbFhXNzc2FFL7o9fo1p49wjxkZGakCwASF9AJkZmZi3759iliUnU4nhoaGkJ2djYaGBloJIgxxeb1e9Pb2orS0NCUNEcWQyrTs7Gzs2bMnprnv4momMu5YWJKcKA8s4pnm9/tls2tPNGSWyb59+6BWq1FXV0dnhszNzWFwcDBsyEapLC0t0R4tJd6r2MJFWECh1WpDZr0AV3JaJOdIRMNoNNLinpqaGnziE5/Y8JrhIj3i3+I60aC4QkSKEJRwLzSahcjlcqGrqwvV1dWKqYiyWq0YGBjA9u3bYTAYwooJmWGyffv2tKh8IkUQW7Zskexz0Gq1SXNJJj0z2dnZcXumKYGNZpmIx/OKXXeFneRKYmFhgQ5hU0KP1maICyhcLhcdUeHxeKDT6bC6uoqOjg4qMC6XC//4j/+InTt3RnwdvV4f0mc3Ozu7xj4n3GMAzMf+6hQiKEJIHiXSHy+pmtq9e7diGrHm5uYwMzODffv2IRgMUpsXklgD3hxt29zcrPju10ggDXENDQ2ydVcn0iXZ7/ejp6eHnhxTnWhmmYhDNsIBV263G0VFRbRxNZkntvn5eSwsLGDv3r0pISbhyM7OpqahVqsV/f39KCwsRFdXF7773e/immuuwe9//3scPXoUn/70pyN+3s7OToyOjmJiYgJVVVV48skn8cQTT4Q85vDhw3jooYdw22234fz58ygoKEA8+RNAgYJC7FciSarNzMxgbm4O+/bti3kRifY0tNlzkQFd+/fvp47Gubm5uHjxInJyclBWVgaPxwOj0ZgWHlbAm2W0iWyIW88leWBgAD6fj1rBxFKSTHysampqFD8hLxLinWUiHHAVCARgtVppLwb5TgtnvycCMs9eqhHEyWZ1dRXDw8PYt28fsrOzEQwGkZGRga9+9auYmprCT3/6U5hMJrznPe/BNddcs+lnqNFo8NBDD+Gmm25CIBDA8ePH0dzcjIcffhgAcPfdd+PQoUM4e/YsGhoaoNPp8Oijj8b9OrhNqqoS0k0TDAZpfPDSpUtoamrasB5eOC+kubk55i/Ua6+9hs7OTkm+kGRHm5ubix07dtA/A948da2urmJoaAgulwt5eXkoLy9P+NwRqVlaWsLk5CRaW1sV08NAJicajUbYbDZakhzJHAty0tqxYwcd4pTKBINB9Pb2UtddKcN2Qot4k8kEACGebnKFCGdnZ2EwGNDW1pYWYkL6zoSO236/Hx//+Mexd+9efO5zn4PNZsOLL76I3//+9/j2t78tZ/g1ridWhKDwPA+v1wsA6OnpwbZt29YNA3m9XnR3d6O0tDRuI76LFy+ira0t7p0VyeHU1tZi69atYfMlZIZJQUEBtm3bRgcuGY1GBAIBlJaWory8XNYfotRMTU3BZDKhtbVVkclQACElyVardcOSZPLDThfrkUTPMvF6vTCZTDAajXA6ndQpWcreounpaZjNZrS2tqaFmBDHBWHfWSAQwN13343t27fjK1/5SqLXg/QSlP7+flRVVYW1s7DZbOjp6cHOnTslSWK/8cYb2LVrV1w7a2I42dzcjIKCgg1nmKw3jdDn89HZLC6Xa0NzRSXA8zyGh4fh9/uxe/fulKl8Ik6wRMiBN0uS3W43RkdH6eClVId4xSVrlolw9jsRcpLYj7XnZWpqClarFa2trSnzndsIh8OBnp6eNWLy6U9/GhUVFfj617+ejNeZXoIyPDxMyxWFLC4u0g5yqewuurq60NDQEPPzzc/PY2pqivoFkVpy0vkKvJlf2L1797pNSEJIJZPRaMTKygry8/NRXl4el+mflJDy7Ly8PNTX1ytS8CKF7KiJl9LWrVtRUVGR9GRzvChtlolQyE0mE4LBIBWXSHNck5OTWFlZQUtLS0p/NgQiJkL7nmAwiL/5m79BTk4Ovv3tbyfrdaa+oABXzBABYGxsDHl5efSHwPM8xsbGsLq6Knlopa+vDzU1NVGHN8g92Ww2evQWd74DV0oaZ2Zm0NraGlOeRNjZbDabk95BTsKNVVVVKTfBbz1IN/+ePXtgs9lgNBqxvLwse0myXBAH5IaGBsXmgIhNidFohN1uR0FBAbXdCbdpGh8fh91ux549e9JCTJxOJ7q7u7Fnzx4a2g8Gg3jggQfA8zy+//3vJ/N1poegeL1e2pGs1WqxdetWmnfQ6XSy9AEMDAygsrIyqnJj8T0BCGujcvnyZfojkGJBEnaQm0wmcBxHk/qJSIaTnpl0SVYLh3yJFyoylpe816QkWTgTQ4mQgoKmpqakOCDHgnBWu9lspvNHysrKkJmZSV2qUym0uhEulwvd3d3YvXs33cgGg0F86UtfwurqKv7v//2/yX6d6SUoMzMz4HkepaWl6O7upoluORgeHqYOoZFApj1WV1fTRjtxviQQCKCvrw86nQ4NDQ2yhYRIb4DBYIi7THYzrFYrhoaGQnZUqQypElSpVGhsbNz0/SLNZ6RbmViUrOfemwzSZZYJmT9CTi9arRa7du1KSTsYMWT9IK8HuLJ5+drXvob5+Xn86Ec/UkJYO70EZX5+HlarFcvLy9izZ09EeYdYEYfXNmJlZQV9fX3YvXs3CgsLwybf3W43enp6oNfrExoS8vv9IT/CoqIilJeXS5ILWFxcxPT0dMxhO6UR77hecUnyZuGaREBmmaSLQzXxgvN6vSgrK4PJZMLq6iry8/NpfjWVwpDAm2IiPD3yPI9vfvObGBsbw49//GOlvKb0ERRidWEymXDdddfJ7oA6MTFBfXY2YmFhAZOTk3SMaDgxWV1dRX9/P5qampLasU+qawwGQ1y5ABJ+JFU1CvmyxwWpfJLKGoaEa4xGIywWC61kKisrS1iOi8wy2WjEbSpBmoODwWDIDCOhM4LZbIZGo6FhSKVX5Xk8Hly6dClkrDLP8/jOd76DS5cu4ac//amSyu7TQ1A8Hg/6+vrgdruRk5ODXbt2yX7N6elpcBy37jhaEmdfWVmhTVThku9LS0uYmJhQXMmp+Eeo1WppmexGYk2mXPI8j6ampmTHdCWBJKu3bdsWdj6OFJAcFylJlrvJj8wy2bt3b1rYz/M8T0ORm+VMScjXaDTC4/Ek3DQ0UrxeLy5duoQdO3ZQ/y6e5/GDH/wAv/vd7/DUU08pzS0jPQTl9ddfR15eHgoLCzE3N4fm5mbZrzk7Owu/34+6uro1f0dCI1lZWWhsbKR/Jk6+k118S0uLknYZYRH2YPA8H9YWnhQdFBYWxt04qhRIiWYix/UKm/xcLpfk/leLi4uYmZlRrMNutPA8j8HBQWg0GuzYsSOq75241D43N5eWJSfzvSFiIqy443kejzzyCH71q1/h6aefVuJGID0Exe12g+M42O12XL58mc4OkZPFxUU4nU46hEZ4L11dXSH+RcRGWjjDZGBgAGq1Go2NjYraFUUCsYU3Go1wu90oKSlBYWEhxsfHUVNTg8rKymTfoiQoYVY68b8iJcm5ubk0DBnLgjc7O4ulpSW0tbWlRSiSeI1ptdq4C1mIHQyp0FOpVPSkqNPpErZB8vl8uHTpEurr60OKfh577DGcPn0azz77rFJDlOkhKH6/nw6WGRwcjHu2cSQYDAasrKxQ7y3gzeT7rl27UFRUFDZfQmzay8vL08KJNhAIYG5uDpcvX0ZGRgaKi4tpM2WqCaUQk8mEsbExReUXhCXJZrM5xEE5knsU5rUUUBEUNzzP0xEBcjTKejwe6jVGToqlpaUoKiqS7bvt8/nQ1dWFurq6EEePxx9/HE8++SR++ctfxh0aP378OJ577jmUl5ejr69vzd/HMS8+rg9AcdsbjUZDTRXlRjwGmHTjt7e3r5t8t9vt6Ovrk9WmPdGsrKxgfn4enZ2d0Ol01PtqdHSUuskmO3wQLfPz85ibm1OcozPHccjPz0d+fj61hjcajRgcHNywJJnk81wuV9oM+goGg+jr60Nubu6aKIFUZGZm0nk6pGDFaDRiZGQEOp1O8kZhv99PXQqEYvLzn/8c//3f/40zZ85Ikme98847ce+99+KOO+4I+/dyzIuPBMUJSqJmvQuvRQYPWa1WdHZ2QqPRhE2+kx3vnj17khY+kRrSzd/e3k7jucJxvHa7HQaDAZcuXaKVNUp2SOZ5nno+pcK43qysLDoPg5Qkz8zMhJQkFxUVYWxsDMFgMKYpmEqEuCDn5+dj27ZtCbkmme1eUlISdtQ0ybvk5ubG9B4TMamurg4p/Hj22WfxyCOP4MyZM5KtGwcPHsTk5OS6fx9uXvzCwoLsoWzFCAr5AInVeyIgs1d6enqg1WrR0dEBjuPCisn09DQMBoPidryxwvM8JiYmsLKyQkfBiuE4Dnl5ecjLy8P27dvpzJH+/n5FOiSTklOfz5eSu3iNRoOKigpUVFTQkmSDwYD+/n5oNBrU1dXB6/UqMZEbFcFgED09PSgqKkJtbW1S7iHcqGmz2YyJiYk1c98j2ZQEAgFqSyScoXPmzBk89NBDOHPmjKw9dWLCzYufm5u7egQlGQQCAZhMJuzYsQM1NTXgeT5khgnwZgltIBBAR0dHyi1S4QgGgxgcHIRKpYpq4c3OzkZNTQ1qamqoQzIJwyTbIZn0MGVlZaG5uVkRAhcPKpUKBQUFmJ6epuETk8mE3t5e6iQh99wROUi0pX6kaLVaVFZWorKycs3c96ysLHp6CXcyJ2JC/j3hV7/6Ff71X/8VZ8+eTXhvWiQz5eXgqhWU1dVVapFSXV0d1kbF5/Oht7cXRUVFaVNCS05kxcXFqK2tjfk1ZWRk0B+QeNZ7fn4+rWJKRMiJvKaSkpKk7XilhixSwhHEubm59JQiFHOljOTdDPKaysrK1u39UgLiue8OhwMmk4mezIldU35+PoLBILq7u1FRURHSIP3KK6/ga1/7Gs6ePZsU77tIZsrLgWIEJZGL9dLSEi5fvozW1lYMDg6GTb4To71t27YpwgJcCog1jNSjbcWz3kn3+Pj4OLKyslBeXi6bQzJxQNbr9WlT6rzZLBNinkqGuQlH8sZbkiwXREwqKipQVVWV7NuJipycHOTk5KC2thY+nw8Wi4XmuYiPnnCN+J//+R988YtfxJkzZ2Rrot2McPPiE/H7UIygCOE4DsFgUPLdFskbmM1mdHZ2QqVSwePxwOFwhNSoEzPEVDfaE0KmEcrd3MdxHAoLC1FYWIgdO3bA4XDAYDCgu7sbHMehrKwM5eXlkpTxEufWdHFABt6cZVJXVxfRYqRWq2k4RliSPD09HXVJslz4/X4aEkr1sQcZGRmoqKhAWVkZPemrVCo8+uijeOKJJ7B//368+uqreOGFF2RdwG+//Xa8+uqrMJlM0Ov1+MpXvkLHqMs1Lz4SFNOHIpwrf/HiRck7gEmJokajoR5BgUAA8/PzWFpaojsNjuNgNBrR1tam2EqmaDGbzRgdHUVLS0tSzQOldEgmAhnp4LJUQOpZJsIx08lySSaVT1VVVWlzgiRrSUFBQUiI9fTp0/jWt76F/Px82O12vOtd78IHPvCBhPTUSUh6NDYKpzZKMZpXCLFA2LJlC2pra8Mm330+H/r6+mCz2ZCRkYGSkhKUl5envG323Nwc5ufn0dbWpqjqtHAOyaREdrOTKTFETLZASoncs0yS4ZJMGvxqamrSJmzM8zztnRGWO1+6dAmf/OQn8eyzz6K+vh52ux0vvfQSPB4PPvjBDybxjqMm/QSlp6cH9fX1ktRs22w29Pb2YufOnSgpKQmbLyEzTHJycrB9+3YEg0GYzWYYDAbYbDYUFhaivLxc1u5aqSGNcA6HA3v27FF0P4aw4cxqtW7okLy0tISpqSm0tbWlfPksIdGzTHiep82rFosFWVlZtMFPqvc0XcWkv78fOp0upBGzt7cXf/mXf4mTJ0/SoXspTPoJSn9/P6qqquLeqRkMBoyNjdE5EbHMMAm32JWXlyesgikWiM9YRkaGLJMu5WQjh2SDwQCDwSD5KOhkooRZJsJJoFKUJJM80LZt20K6xVMZ4jeWmZmJhoYG+ucDAwM4fvw4nnzySezevTuJdygZ6ScoQ0NDdIca63NNTk7CZDKhra0NGRkZYcWE/JgjTVSTxc5gMNAZ76SCSSkLnM/nQ09PD8rKyhRV5x8rTqcTBoMBMzMzCAQCdMebDqEuJc4yEbokO53OkP6iSE7nJLy8ffv2tLEmIrb6Go0mxLxyZGQEd9xxBx5//HG0tLQk+S4lIz0EBbhi5AZcmaSYn58fU8kd2Z0DwO7du9ftfF9aWsLk5CRaW1tj+jET6waDwQCTyQSNRkNnvCcrFONyuWipc7LKFaWG2JpzHIf6+nq62BGH5PLyckWN4o2UVJhlEgwGqS18JC7JHo8HXV1dkhUVKAGe5zE8PAyO40JO+xMTE7j99tvx4x//GO3t7Um+S0lJH0EhY4AjnaQY7t93dXWhvLycVl+Ik+9Cy5GWlhbJ7L9dLhcMBgOdNVJeXi5ZeWwkkImR6VT1RHJbeXl52LZtW4hoBAKBkDxXQUFByjgkp+IsE6GvWziXZDKVcOfOnbQhMNUhVj48z6OxsTHEhulDH/oQHnnkEXR2dib5LiUn/QRls0mK4bDb7ejp6cGOHTtQVlYWtvM9EAiE5BbkWng8Hg+MRmNIeSwJ08g1uY80aippYmQ8RDOul1hlkCSzkh2S02WWibAk2ePxwOv1or6+Hnq9PuVOi+HgeR5jY2Pw+/0ho4jn5ubwgQ98AD/4wQ9w/fXXJ/kuZSH9BGV+fh4ejydiF1JitU6GKK03w4QsUIm0fSCeVwaDAS6Xi/YCSFWOPDMzg6WlJbS2tiqqLDge4hnXKx6wpFaraSgy2X1Fk5OTWF5eRktLi2ILOqLF5XKhq6sLW7ZsgdPpxOrqakJKkuVmbGwMHo+Hhs2BKyfLW2+9Fd/5znfwv/7X/0ryHcpG+giKz+dDMBjE0tISbDZbSDVFOIhVucFgoOGDjWaYJLujWhymiaccmeygXC4XmpubU/aHK8bhcKC3txeNjY2SGOoRh2Sj0Zg0h2ThLJPm5mbFh+QihTgV7Nq1i4ZZhdY7ZrMZmZmZNDSm1FyRmPHxcTidzhCTUYPBgGPHjuGb3/wm3vnOdyb5DmUl/QSFNGA1NTWt+1jimBsMBukHHy75TsJBSmuCi6ccORAI0Cl38Y5MVRKk6m7Pnj3Iy8uT/PmFp0VSwVReXi6rQzJJ6vI8HxI6SXWcTie6u7s3zdkJS5KDwSAVF6W6JE9MTMBut4fMnTGZTDh27Bi+9rWv4eabb07yHcpO+gnK8vIy5ubm0NzcHPZxJHxVWlqKuro6AKBiIky+T09Pw2QyoaWlRdHhoGjKkUluoaKiQtGOrdGS6HG9xCHZaDRiZWVFFodksumRYla6knA4HOjp6Yla+MnMEaPRCIfDEZU7QiKYmprCysoK9uzZQ+/HarXi2LFj+MIXvoBbbrklyXeYENJPUOx2O8bHx9Ha2rrmMST53tDQgPLy8rDJ92AwiKGhIfA8j127diniyxopwnJko9GIjIwMmgMgsyS2b9+eNg1jwJWpkbOzs0mzhxGHaaRwSE7GRMJEQLr64z1Fik/oyS6kmJ6ehtVqRUtLC10vVlZWcOutt+Kzn/0sjh07lvB7ShLpIyh+vx+BQAAulwuDg4NrTNVMJhOGh4fR2tq6bvKdNPaR2Ripvisk5cgLCwtwOBzQ6/WoqalRTCNcvExOTsJqtaK1tVUxeSBhfxFxSC4rK4u4gi5V5n5ECxETUvwiFeEKKUi3fiKqFmdmZmgTNBETm82G97///bj33nvxoQ99SJLrnDt3Dvfddx8CgQBOnDiBBx54IOTvV1ZW8JGPfATT09Pw+/347Gc/i7vuukuSa0dB+gkKCWkJa7ynp6exsLCAvXv3QqvVhhUTktCtr69Pm8Y+4EpCcGJiAk1NTbDZbLQcmVjBKzUevRE8z2N0dBRerxe7d+9W7ClSXAJOminXc0jebJZJqkLcnRNhEUNcqUlJstSVkULm5uawtLSEvXv30u+gw+HABz/4QRw/fhwf/ehHJblOIBDAzp078eKLL0Kv16OzsxM//elPQ+xavv71r2NlZQUPPvggjEYjGhsbsbi4mOhTe1xvsCIL4TUaDW1IJOErv9+Pzs7OdZPvFosFw8PDsiV0k8X09DSMRiM6OjqQkZGBgoIC6PX6NSN4U6lrnLgZaLVaxY/rzczMhF6vh16vpw7JU1NTYR2So51lkiqQptm2traEnBiysrJC3nOLxYK5uTkMDg5KmusioyuEJxOXy4XbbrsNH/nIRyQTEwC4cOECGhoaqKnkbbfdhtOnT4cICsdxsNls9MRWXFyccr1KirpbsrCoVCrwPE93e8XFxTQOHU5MZmdnsbCwgI6OjpQpTdwM0qXr8/nQ3t6+ZgcvHsFrNpvpFDmlJTuFpPK4Xo1Ggy1btmDLli0hOYCRkRFkZ2fDZrOhsbExrcRkZWUFg4OD2Lt3b1LCrMTSqLy8fM000HhKkhcWFmjEgwiT2+3Ghz/8YXzgAx/A8ePHJX0dc3NzIeFPvV6P8+fPhzzm3nvvxeHDh7F161bYbDb87Gc/U9zvdzMUJShCgsEgLl68iO3bt6OioiIk+S6s5BodHYXb7UZHR4diYvDxIrTTj8QtmDTvlZeX04XOYDBgZGREUe7I6TSuV6VSoaSkBCUlJXA4HOjq6kJRUREmJiYwNzeXcr0X4VheXsbQ0JBizCvDTQM1mUzo7e1FMBikeZfc3NwNfzNLS0uYm5sLEROPx4M77rgDhw4dwic+8QnJT83hUgvia7zwwgvYu3cvXn75ZVy+fBnvete78Na3vjWlpsYqUlDMZjOcTieuvfZa5OXlha3k8vv9tIqmpaVF0WGTaCCL7tatW2OavS1c6MQ7umS6I5MmuIaGhrRxoQXeTFS3trbSUKvT6YTRaERvby+1gye5rlTBarVieHgYe/fuTbrDwHqIZ72bTCZMTExsWJJsMBgwPT2N9vZ2Gk7y+Xw4fvw4brjhBnzqU5+SZS3R6/WYmZmh/3t2dnZNju3RRx/FAw88AI7j0NDQgG3btmFoaAgHDhyQ/H7kQlFJ+WAwiPHxcczPz8Pn8+Etb3lL2OS7y+VCb28vqqurU36nK4QUFcix6JJy5KWlJZhMppByZLl30ek4rheIbJaJ1+ulnfrEIVmuBLNUEFt9JYvJRqxXkgxcqehqb2+nGyq/34+Pf/zj2Lt3Lz73uc/J9pn4/X7s3LkTL730EqqqqtDZ2YknnngipNfuk5/8JCoqKvDlL38ZS0tL6OjooP12CSR9qrxMJhPGx8exZ88eXLhwAfv27aNCEusMk1RheXkZg4ODCSsqILtog8EAALK5I5OdrtKcCuIlllkmJNdlNBqp55XSHJLNZjPGxsYUbasfDSTBTSya8vLyoFKpkJWVhaamJtx9991oaGjAl7/8ZdkF/uzZs7j//vsRCARw/PhxfP7zn8fDDz8MALj77rsxPz+PO++8EwsLC+B5Hg888AA+8pGPyHpPYUgfQQkGg3TIVldXFziOQ0VFBY3/Ly4uYmpqKuYZJkqFzGZpa2tLyo5QXBorVTkyKXdO1uuSC6PRSF9XrIuuEh2SSdVge3u7op0looWIZHt7O4LBIH7729/im9/8JmZnZ1FVVYVvf/vbuPbaaxUj6kkmfQSF53m43W5ayWW327G0tASz2UxtVdrb29Ni5wS8aW5psVjQ0tKiCKv1cO7IsZQjE5v2dBrXC8gzy0QJDslEJEmfV7pgsVgwOjoaIpLBYBD/3//3/yErKwtve9vb8Nxzz+H111/Hxz72MXzmM59J8h0nnfQRlF/+8pcYGhrCkSNHUFVVBY7jYLfbcfHiRZSWliIrKyvE66qsrCzl6rQJxDQwEAgo1h5G7I5cVFREzRTXu1+e5zE+Pk4N9pJdWSYliZplEs4hOZLqpVgxGAyYnJwMyS2kAyTcKtyEBoNB/P3f/z04jsP3vvc9+j0OBAIwmUyoqKhI5i0rgfQRlIWFBfz85z/HM888A5/Ph7e97W149tlncffdd9O6cHFyWavVoqKiAmVlZSnzYwgEAiE+T0pNzgoRliMvLy+HbTAjs7eJh1oqvK5ISdYsE3JiJIaKUjskLy0tYXp6OqWmR0YCKXkWFhYEg0H84z/+I+x2Ox5++GFFbuIUQPoICr0oz+Pll1/Gxz72MezcuRMOhwPvec97cOTIkTWurUIjRWETlFKP7R6Ph/ZipKo1BylHNhgMsFgsyM7ORmlpKQwGA/Lz81FfX582YqKkWSZSOyQvLCzQfoxUPemHQ9iMScSE53l89atfxeLiIn74wx+m1clZYtJPUJ577jl86UtfwpNPPokdO3bAZDLh2WefxalTp2AymfDud78bhw8fXrMLdrlcWFpagtFohEqlUsykPgIZ9JVuc7eXl5fR398PnueRk5OjeFGPFCXPMonXIXl+fh4LCwspP4pYzOrqKgYGBkKq73iex4MPPojLly/jxz/+cVq9XhlIP0H53e9+h+bm5rAT+6xWK37xi1/g1KlTmJ2dxY033oijR4+GzDAArtgoGAwGGAwG8DwvW1lspBCvMamdWpMNOXHV1taioqICTqeTnhiJU28y3/dYIX5jmZmZKTHLJBqH5Lm5OSwuLoZ0iqcDpN9J6DnG8zy+853voKurC0888URahfVkIv0EJVJWV1fx3HPP4dSpU7h8+TLe8Y534OjRo2u8rzweDxWXQCBAxSURRnfAldDCzMxMXGWmSoQ0Yq534hKWI/v9/qSM342FVJ9lspFD8tzcHAwGA9ra2tJKTISOBaTfied5/OAHP8Dvf/97/PznP0/5E3OCuHoFRYjdbsfzzz+PU6dOYWBgADfccAOOHj2Kzs7OkB8O6Vw2GAzwer10By3HqYHn+ZBkbjodtaMd1ytVObLcpNssE7/fTyv1rFYrOI5DU1MTSkpK0iYpTSZIisXkkUcewa9+9Ss8/fTTabWRkxkmKGLcbjdeeOEFnDx5El1dXfiLv/gLHD16FNddd13Iok4WuaWlJbjdbpSWlqKiokKS8kxiuw8ATU1NafPjBa40io2OjsZsGhhLOXIiIO7WVVVVaWXpA4D2O1VXV8NkMsFqtSI3N5cah6bqZoeIiTiU/Nhjj+H06dM4ffq0YnKoKQITlI3weDx46aWXcPLkSVy4cAHXXXcdjh49ir/4i78IiaeSWRcGgwFOpzOuHTQxriwsLERdXZ2iduDxIvW43nDlyMSOJJEhmXSdZQJcKXleWVkJGW/L8zwd1mY2m6HValPOIdnpdKK7u3vNKfm///u/8bOf/Qy//OUvExbWTiOYoESKz+fDb37zG5w8eRK/+93vsH//fhw5cgQ33HBDyI+I7KCXlpbooJuKioqIDP3cbjd6enpQU1ODLVu2yP2SEsrU1BTMZjNaW1tl2dEKy5HNZjO1I5G7gdXtdqOrqws7duxASUmJbNdJBsIm041Of8TbzWg0poRDssvlQldXF5qbm0Ps3X/+85/j0UcfxZkzZ9Kq+CWBMEGJBb/fj9/97nc4efIkfvOb36C1tRVHjhzBO97xjpAwTjAYpOGZ1dVVGp4pKipaIy4kMdjU1BS2Qi1V4XkeY2NjcLvdCevFIHYkpHKJuCNLXY7sdDrR09ODpqamtDIbJY4FTqcz6s/M6/XS07oSHZLJBmDXrl0h7tXPPPMMHn74YTz33HNp5WqdYJigxEsgEMBrr72GU6dO4de//jUaGxtx9OhR3HjjjSE7NBKeWVpawsrKCgoKClBRUYGioiJYrVaMjo6mnasuKZ/NyMiIaNiXXMhRjkzKTNNtbDRpxvR4PNi9e3dcn5nSHJKJmIg3AGfOnMH/+T//B2fOnEmrzVwSYIIiJcFgEH/6059w8uRJvPDCC9i2bRuOHDmCd7/73SGLDs/zNPZPel127NiBLVu2pE0CPhAIoKenB0VFRairq0v27VCkKEcm3dTptgEgU0z9fr/k9jfBYDDEISHRDskejweXLl1CY2NjiGj86le/wr/8y7/g7NmzkoUsz507h/vuuw+BQAAnTpzAAw88sOYxr776Ku6//374fD6UlpbiN7/5jSTXTjJMUOSC9CM89dRTeP7551FZWYkjR47gPe95DwoLCxEMBvHzn/8cjY2NqK2tpVbkubm5Ibb7qQiZHFlVVaVoi5hYypFjmWWSCvA8j5GREQSDQdk7+8M5JJNToxxVVV6vF5cuXcKOHTtCep5efvllfOUrX8GZM2ckK6YIBALYuXMnXnzxRej1enR2duKnP/0pdu/eTR+zvLyM66+/HufOnUNNTQ0MBkO6FHMwQUkEPM9jYGAAJ0+exJkzZ1BQUACPx4OKigo8+uijIdUzq6urNLGs0+moJUaqlGam6rjeSMqRpZhlokSITQwANDY2Jjw0KadDMhGThoaGkBPIb3/7W3z+85/HmTNnJC2A+eMf/4gvf/nLeOGFFwAA//Iv/wIA+Id/+Af6mB/84AeYn5/H1772NcmuqxCYoCSa5eVl3HLLLSgsLITRaIROp8ORI0dw+PBhlJeX0x+QOLGcmZlJ/cWUagFBCgtSfVxvuHJkjUaDlZWVtLNp53keg4OD0Gg02LFjR9IT5+s5JBcUFEQdDvb5fLh06RLq6+tDNjd//OMf8dnPfhbPPfccqqqqJL3/kydP4ty5c3jkkUcAAD/5yU9w/vx5PPTQQ/QxJNTV398Pm82G++67D3fccYek95Ek4vrypMaWWUEsLS3h8OHD+Lu/+zvceuut4HkeExMTOHXqFD760Y9CrVbjlltuwdGjR1FZWYm8vDzk5eVh+/bt1Hb/0qVL0Gg01HZfKZYQZH7ERjPSUwWVSoWSkhKUlJTQvMLS0hI0Gg0GBwcTUo6cCMjJWavVKsZzLCMjA5WVlaisrEQgEIDVasXCwgKGhoaickgmYrJt27YQMbl48SI+85nP4Be/+IXkYgJceU/FiN9Xv9+PP/3pT3jppZfgcrlw3XXX4dprr8XOnTslv59UIrV/TUmgqKgI//mf/4m2tjYAV75o9fX1+Nu//Vt89rOfxdzcHE6dOoWPf/zj8Pv9uOWWW3DkyBHU1NQgJycH9fX1qK+vp1VLXV1ddEJfeXl50sIwZFyv0PI7XZiamoLT6cT1118PlUpFT41/+tOfZCtHTgQ8z6O/vx/Z2dmKHRmgVqtRWlqK0tLSEIfk8fHxDR2S/X4/bTQtKyujf37p0iV86lOfwrPPPouamhpZ7lmv12NmZob+79nZ2TV5RL1ej9LSUuTk5CAnJwcHDx5Ed3f3VS8oCQt5bVY1wfM87rvvPpw9exY6nQ6PPfYYOjo6pLp8wuF5HktLS3j66afx9NNPw26305ku27dvX2O7T6rFOI6jC1yiFvbZ2VksLi6ira0t7UJBly9fhtvtxu7du8OGW1LVHTkYDKK/v59uUlKR9RyStVoturq6UF1dHTJBsbe3F3/5l3+JkydPyrpw+/1+7Ny5Ey+99BKqqqrQ2dmJJ554As3NzfQxg4ODuPfee/HCCy/A6/XiwIEDePLJJ7Fnzx7Z7itBKD+HEknVxNmzZ/H9738fZ8+exfnz53Hffffh/PnzUlxeERiNRjrTxWw249ChQzh8+PCaahyhM3IwGERZWRkqKipkWeBIuG51dTXhkwjlJpZZJuS9NxqNinZHTnU35HAIS8GXl5dRXFyMbdu2IS8vDyqVCgMDAzh+/DiefPLJkHVDLs6ePYv7778fgUAAx48fx+c//3k8/PDDAIC7774bAPCtb32LFuScOHEC999/v+z3lQCULyiRVE184hOfwA033IDbb78dwJVKlVdffTXtTPqAK2WrZKbL3NwcbrrpJhw9enRNR7PX66Xi4vf76e5ZivwGWXBJiWm69M4A0swyUao7MhGTwsJC1NbWJu0+5CAQCKCrq4uGHwcGBvDpT38aLS0t6Ovrw89//vOUjlqkCMpPys/NzYVYgev1+jWnj3CPmZubS0tBKS4uxp133ok777wTKysreO655/DNb34Tly9fxjvf+U4cPXoUe/fuhVarhV6vh16vh8/ng9FoxMjICLxeL3VGjmX3HAwG0dfXB51Otyb8lupItXsXJ5bNZjNmZmaS6o4cDAbR3d2NkpIS2fIHyYKMDdiyZQtNtFdUVOCpp57CPffcg/379+Ouu+7Cnj17cPToUbz//e9Pq+9tupAQQYmkaiKSx6QjBQUF+PCHP4wPf/jDsNvtNPQ3ODiIt73tbXSmS0ZGBrZu3YqtW7fC7/fDaDTSWeckNJOXl7fpe+b3+9Hd3Y3y8vK0mPchRK5ZJsKiCaH9zvDwcMLckYlrQWlpadp9bsFgED09PSgrKwup2pqamsJdd92FRx55BJ2dneB5HpcuXcJvfvObq2JtSEUSIiiRVk1s9ph0Jzc3Fx/84AfxwQ9+EC6XC7/61a/wox/9CJ/+9Kfx1re+NWSmi3D3bDKZMDU1BbvdTkMz4Yz8xON60wmfz4euri7o9XpZT7XicmRiRXL58mXZmliJUJaXl0Ov10v2vEqAiElJSUmIUM7NzeH222/Hf/zHf6CzsxPAlQ1mR0cHC3spmITkUCKpmjhz5gweeughmpT/9Kc/jQsXLkhx+ZSHzHR56qmncPHiRVx//fU4evQo3vKWt4RUZQUCAVgsFiwtLdHQTEVFBQoLC+FyudDT07PuuN5URgmzTMRNrGS+SLzlyOREWVlZmXYbrPXyQQsLC3j/+9+P7373uzh48GAS7/CqRPlJeWDzqgme53Hvvffi3Llz0Ol0ePTRR7F//36pLp82+Hw+vPrqqzh58iR+//vfo7Ozk850ES5cwWAQFouFGvn5fD7s2LEDW7duTasEvFJnmYjLkYlDQjTVeqQXIx0nSPI8j76+PuTm5obkupaWlnDrrbfiW9/6Ft7xjnck8Q6vWlJDUBjSI57p0tbWRme6kB6WCxcuwO/3o66uDisrK9SGpKKiImkW5FLhcDjo/BklzzJZrxx5owFQJIRXXV2ddoPaSEOmTqcL6aExmUw4duwYvva1r+Hmm29O4h1e1TBBYVwJd/3xj3/EqVOn8NJLL6GpqQmVlZV46aWX8MILL1C7b57nsby8TE8ueXl5dK54KvWhpOosE1KtZzQa1y1HJmJSU1OTdrkuYhVDSroJFosFx44dwxe/+EXccsstSbzDqx4mKIxQgsEg/u7v/g7PPPMMjU8fOXIEN99885qZLsQZ2WQyIScnBxUVFSgtLVW0uKTLLJNw7sjFxcWYmJjAtm3b0sUOnUJMLDMyMkL6g1ZWVnDs2DH87d/+LY4dO5bku7zqYYLCeBOe5/H5z38eExMT+PGPfwyNRoOenh4606WqqgpHjhzBoUOHQsJEJKm8tLQEk8mE7OxsGvdXkoFius4yCQaDMBgMGB4ehkqlog69cpcjJwrSSMtxXMjkT5vNhve///2499578aEPfSjJd8kAExSGkGAwiJ/85Cf46Ec/uiY/QmLXJ0+exNmzZ1FcXIyjR4/iPe95z5qEtlBctFotdUZOptdXus4yAUJnfhQXF9NyZLPZjJycnJSbqSOEDP7ieT5kVovD4cAHPvABfPzjH8dHP/rRJN8l488wQZGCzcwrH3/8cTz44IMArvSL/Md//Ad1HE5FyI/85MmTeO655+hMl1tuuSVkpgvwpomf0WiERqNJijvvwsICZmdnsXfv3rQysASuJO1JpZq4pDtcOTI5OaaCOzLP8xgbG4Pf7w/xVHM6nfjQhz6ED3/4wzh+/HiS75IhgAlKvERiXvmHP/wBu3btQlFREZ5//nl8+ctfThvzSuFMl2effRYZGRnUdr+ysnJdZ2SVSkUXNzmdkWdnZ2EwGNDa2pqSO/SNIGXP4jnp6yFFOXIiGRsbg9frDZlv73a7cfvtt+N973sfPvGJT7Cud2XBBCVeIjGvFGK1WrFnzx7Mzc0l7B4TBc/zmJ2dxalTp/DMM88gEAjgve99L44ePYrq6uqQH7/b7abiwvM8PblIubhNTk5ieXk57dyQgTdHLcda9ux2u6lDbyAQoPbvG5UjJxJiDdTc3Ey/Nx6PBx/96Edx44034lOf+hQTE+UR1weSuk0IErKeMeV6/PCHP8S73/3uRNxawuE4DtXV1bj//vvx6quv4qmnnkJeXh7uuecevPOd78S//du/4fLly+B5HllZWaipqcH+/fvR1tYGtVqNwcFBXLhwARMTE3A6nTHfBwmV2O12tLa2MjEJQ1ZWFqqrq7Fv3z60t7cjMzMTY2NjeO211zA6OoqVlZWwHnmJgHz+QjHx+Xy46667cMMNN0gmJufOnUNjYyMaGhrwjW98Y93HXbx4EWq1GidPnoz7moz1Sa/4QYxEY0z5yiuv4Ic//CF+97vfyX1bSYfjOFRWVuKee+7BPffcA6PRiGeeeQaf/exnYbFYcOjQIRw5cgSNjY0hzsherxdGoxHDw8Pwer3UgiTSnbNwlolwQUoXnE4nuru7sXv3bhQUFEjynELzUOLvlix35MnJSdhsNuzZs4d+dn6/Hx//+Mdx4MABfOYzn5HkMw0EArjnnntCQtWHDx9eMy8lEAjg7//+73HTTTfFfU3GxjBBQeTGlD09PThx4gSef/55Rdl8JIqysjL81V/9Ff7qr/4KFosFp0+fxpe+9CXMz8/TmS67d++GVqtFVVUVqqqq6FwRoTNyRUUFcnNzwy4qUswyUTIOhwM9PT1obm5Gfn6+LNdQq9WoqKhARUVFwt2Rp6ensbKygpaWFipegUAAd999N5qbm/EP//APkn2mFy5cQENDA+22v+2223D69Ok1gvL9738ft956Ky5evCjJdRnrwwQFQGdnJ0ZHRzExMYGqqio8+eSTeOKJJ0IeMz09jWPHjuEnP/nJVT83Grgy0+Wuu+7CXXfdhZWVFfzyl7/EN77xDUxMTNCZLmSkMHFG9vv9MJvNNBwi7hJPx0mEQux2O3p7exPa3b+eO/LY2Jjk5cgzMzMwm81oa2sLEZNPfepTqK2txZe//GVJNwiRzll65pln8PLLLzNBSQBMUABoNBo89NBDuOmmm6h5ZXNzc4h55T/90z/BbDbjr//6r+m/ef3115N524qhoKAAH/nIR/CRj3yEznT57ne/i+HhYTrTZf/+/dBoNHTnHG5o1crKCiorK9NueBTwppi0tLQkLWnOcRwKCwtRWFgYUo48NTUVdzny7OwsjEYj9u7dS8UkGAzib/7mb1BcXIx//ud/lvy0GUmo+v7778eDDz6Ydjk4pcKqvBiy4XK58MILL+DkyZPo7u7GwYMHcfToUVx77bUhP3C324033ngDGRkZ8Pv9NOZfVFSUFiEv4jvW2tqqWKuYeMqR5+fnsbi4SAszgCti8vd///fgOA7f+973ZMndRFKduW3bNio8JpMJOp0O//mf/4mjR49Kfj9pAisbZigfj8eDX//613jqqafw+uuv05kudXV1uPPOO/HYY4+hrq6OxvwNBgOWl5dRUFCAiooKFBUVpaQz8urqKvr7+xUtJmKiKUdeWFjA/Pw89u7dGyImX/ziF+FwOPDwww/L9rlFMmdJyJ133on3vve9eP/73y/L/aQJyp8pz2BkZmbiPe95D97znvfA5/PhlVdewWOPPYYXXngB73znOzE6OoqtW7dCq9WGxPyXl5extLSEkZERmlAuKSlJCXEhJpZtbW3Q6XTJvp2IIeXI1dXV1B15bGwMbrc7JO+1tLSEubm5EDHheR5f/epXYbVa8cMf/lDWzymSUDUjsbATCiMpjI6O4oMf/CC+//3vw+fz4eTJk/jtb3+LvXv34siRI3j7298e0n0vTCibzWbk5ubShLIS4+PLy8sYGhpKKxNLUo5sNBphtVoRDAaxe/dulJaWguM48DyPBx98EJcvX8Z//dd/KfJzYWwKC3kxUgue53HLLbfg61//OlpbW+mfBwIB/OEPf8CpU6fw8ssvY9euXTh69Cje9a53hezweZ6HzWbD0tISzGYzsrOzqe2+EqxZrFYrhoeHsXfvXlktaZIFMemsra2F2WzGgw8+CI/Hg7KyMiwvL+NnP/uZIj4HRkwwQbka2My8knDx4kVce+21+NnPfqboWDHP8xsm3IPBIF5//XU89dRTePHFF7F9+3YcPXoUN95445qZLg6HgzojZ2Zm0oRyMkwkib1+uoqJyWTC+Pg42tvb6fvr9/vxpS99CS+99BI4jsOuXbvwvve9D4cOHUqp4WcMAExQ0p9IzCvJ4971rnchKysLx48fV7SgREMwGER3dzeeeuopnDt3Dnq9ns50EXeai52Rie1+Ipx5zWYzxsbGsHfv3rSz1wfefH3t7e30/eR5Hv/v//0//PrXv8apU6eg1WrR09ODZ555Bq2trWxgVurBBCXdidS88jvf+Q4yMjJw8eLFtK1m4XkefX19dKZLaWkpjhw5Enami7AUljgjl5eXy7LYEzcA4WKbTlgsFoyOjq55fY8++ih+8Ytf4PTp02l5IrsKYVVe6Q7rCH4TjuPQ0tKClpYWfPnLX8bw8DBOnjyJD3zgA8jLy8Phw4dxyy23oKysDDqdDnV1dairq6POyL29vQBAxUWKRZDkFNJVTKxWK0ZGRta8vv/+7//G008/jV/+8pdMTBgAmKCkBKwjODwcx6GpqQlf+MIX8PnPfx7j4+M4deoU/vf//t/IzMykM122bNlCnZFramrg8XhgMBjQ39+PYDCIsrIyVFRUxFSNZTAYMDk5GZJTSCeWl5dpgYHwZPfzn/8cjz/+OM6cOZNSJdEMeWEhrxSAdQRHB8/zmJ6extNPP41nnnkGPM/TmS56vT5EjIkz8tLSEvx+P3VGjqQJcWlpCdPT02k5RRJ4s49GXGDwzDPP4OGHH8Zzzz0nmVsyQzGwHEq6wzqCY4fneSwsLFBxcTqdeO9734sjR45g27ZtIeJCmvgMBgM8Hg91Rs7JyVlzIlxcXKQjidOxRHZ1dRUDAwNr+miee+45fOc738GZM2cimjDJSDlYDiXdYR3BscNxHLZu3Yp77703ZKbLZz7zGVitVrz73e/G0aNHsXPnzpCZIn6/P8R2v6SkBBUVFcjLy8Pi4iLtEE9HMbHZbOjv718jJi+88AK+/e1v4+zZs0xMGGFhJxTGVYvZbMbp06fx9NNPY3FxETfeeCPe9773YdeuXSGWIaRD3GAwwGq1guM4NDc3p415pRDiiiz2Hnv55Zfxla98BWfOnEF5eXkS75AhMyzkxWDEy/LyMn75y1/i6aefxuTkJN75znfife97H1pbW6m4/PGPf6STKU0mE1ZXV1FUVISKigoUFhamvLisJya//e1v8fnPfx5nzpzBli1bkniHjATABIXBkBKbzYazZ8/i5MmTGBkZwdvf/nb4/X50dXXhzJkzNMwlnIa4srKCwsJCarufCuaVQsgkSfG8lj/84Q/427/9Wzz33HOoqqpK4h0yEgQTFAZDLlwuF+6991688soryM3NpTNdrrnmmpAS7WAwiOXlZRoWy8/PR0VFBYqLixUvLmTGvXiS5MWLF3HffffhF7/4RVoOPWOEhQkKgyEX//qv/4rXXnsNTzzxBHiex4svvoiTJ0/iT3/6E53p8pa3vCUkOU+ckZeWlmCxWJCXl0dt95XWJ+RyudDV1bVmxv2lS5fw13/913j22WfTchwzY12YoDAYcuD1evHP//zP+MIXvrCmz8Tr9eKVV17BqVOn8Ic//AEHDhzA0aNHcfDgwZBucp7nsbq6Sm33dToddUZOtri43W50dXVh165dIf0kvb29+Mu//EucPHkSO3fulORam5mbPv7443jwwQcBALm5ufiP//gPtLW1SXJtRlQwQWEog0gckV999VXcf//98Pl8KC0txW9+85sk3Km0+P1+/M///A+eeuop/Pa3v0V7ezuOHj2Kt73tbWtmutjtduqMnJ2dTWe6JLoxkohJU1MTCgsL6Z8PDAzg+PHj+NnPfoZdu3ZJcq1IzE3/8Ic/YNeuXSgqKsLzzz+PL3/5y2vshRgJgQkKI/lEsmgsLy/j+uuvx7lz51BTUwODwZB2JaiBQAC///3v6UyX5uZmHD16FO985zvXWJTY7XZqXqnVaqm/mNzi4vF4cOnSJTQ2Nob0kwwPD+NjH/sYHn/8cbS0tEh2vUjNTQlWqxV79uzB3NycZPfAiBjW2MhIPhcuXEBDQwPq6+sBALfddhtOnz4dIihPPPEEjh07RhO86SYmAKBWq3Hw4EEcPHgQwWAQFy9exFNPPYVvfOMb2L59O973vvfhxhtvRG5uLv2vvr4eTqcTS0tLuHTpEjQaDRUXqc0mvV4vLl26hJ07d4aIyeXLl/Gxj30MP/7xjyUVEyAyc1MhP/zhD/Hud79b0ntgJAYmKAxJiGTRGBkZgc/nww033ACbzYb77rsPd9xxR6JvNWGoVCpcc801uOaaaxAMBtHV1YWnnnoK//Zv/4bq6mocPnyYznTR6XTYtm0btm3bBpfLBYPBgO7ubnAcJ5kzMhGTHTt2oLi4mP751NQUPvKRj+CHP/wh2tvb433Za4jE3JTwyiuv4Ic//CF+97vfSX4fDPlhgsKQhEgWDb/fjz/96U946aWX4HK5cN111+Haa6+VLPGrZFQqFTo6OtDR0YGvf/3rdKbLLbfcgvLychw+fBjvfe97UVxcjOzsbNTW1qK2thZutxtGo5E6IxNxidYZ2efzoaurC9u3bw+ZGzM7O4vbb78dDz/8MDo7O6V+2QCubC5mZmZCrrl169Y1j+vp6cGJEyfw/PPPr5ltw0gNmKAwJCGSRUOv16O0tBQ5OTnIycnBwYMH0d3dfVUIipD1Zrq8//3vR35+Pp3pUlpaiqysLFRXV6O6uhperxcGgwGDg4NROSP7fD5cunQJ27ZtQ2lpKf3zhYUFfOhDH8L3vvc9XHfddbK93s7OToyOjmJiYgJVVVV48skn8cQTT4Q8Znp6GseOHcNPfvKTq+77kE6wpDxDEiJxRB4cHMS9996LF154AV6vFwcOHMCTTz6JPXv2JPHOlQPP87h8+TJOnTqF06dPIzMzE4cPH8aRI0dQUVER1hl5aWkJXq+XOiMLu9yBK5/LpUuXUFtbG5KzWlpawq233op//dd/xdvf/nbZX9vZs2dx//33U3PTz3/+8yHmpidOnMCpU6dQW1sL4Ioh6uuvvy77fTHWwKq8GMpgs0UDAL71rW/h0UcfhUqlwokTJ3D//fcn8Y6VC5npcurUKTz77LPgeR633HILjh49iqqqqhBx8fv91Hbf5XJRccnKykJ3dzeqq6tRUVFBH28ymXDs2DF87Wtfw80335yMl8dQLkxQGIx0hsx0OXXqFJ555hm43W4606Wuri5EXIgz8uLiIsxmM4qKilBfX4+8vDyoVCpYLBYcO3YM//iP/4j3vve9SXxVDIXCBIXBuFrgeR4GgwHPPPMMnn76aSwvL+PQoUM4evQoduzYAY7jYLfb8eKLL6KzsxNarRbT09M4ceIEOjo6MDo6is997nO49dZbk/1SGMqECQqDcbVCZrqcOnUKS0tLeMc73oGXX34ZH/jAB3DvvffSxxkMBhw/fhzAlZDXW9/6Vtx66604ePBgWg4JY8QMExQGg3El0X7zzTdDp9PB6XTiXe96F973vvehvr4eH/rQh3DixAl85CMfgd/vx29+8xucOnUKn/vc56DX65N96wzlwASFwbja8fl8+OAHP4h3vvOduOeee2Cz2XDmzBmcOnUKL730Ev7pn/4p5MTCYKwDExQG42pnYmICv/rVr/CJT3xizd9ZrVY2A54RKXEJirIn/zAYMnDu3Dk0NjaioaEB3/jGN9b8/crKCm655Ra0tbWhubkZjz76aBLuMjq2bdsWVkwAMDFhJAx2QmFcVUTiivz1r38dKysrePDBB2E0GtHY2IjFxUXJjRoZDAXCTigMRqQIXZG1Wi11RRbCcRxsNhudX1JcXMwqoRiMCGCCwriqCOeKLJ67ce+992JwcBBbt25FS0sLvvvd7yp+LjyDoQTYr4RxVRGJK/ILL7yAvXv3Yn5+Hl1dXbj33nuxurqaqFtkMFIWJiiMq4pIXJEfffRRHDt2DBzHoaGhAdu2bcPQ0FCib5XBSDmYoDCuKoRW6l6vF08++SQOHz4c8piamhq89NJLAK40Cw4PD9NJlAwGY31YppFxVaHRaPDQQw/hpptuoq7Izc3NIa7IX/ziF3HnnXeipaUFPM/jwQcfDJkjwmAwwsPKhhkMBoNBYGXDDAaDwUg+TFAYDAaAzR0EeJ7Hpz/9aTQ0NKC1tRVvvPFGEu6SoWSYoDAYDAQCAdxzzz14/vnnMTAwgJ/+9KcYGBgIeczzzz+P0dFRjI6O4j//8z/xyU9+Mkl3y1AqTFAYDEZEDgKnT5/GHXfcAY7jcO2112J5eRkLCwtJumOGEmGCwmAwInIQiOQxjKsbJigMhkI5fvw4ysvLsWfPnrB/L2VOIxIHgUgew7i6YYLCYCiUO++8E+fOnVv376XMaUTiIBDJYxhXN0xQGAyFcvDgQRQXF6/791LmNCJxEDh8+DD+67/+CzzP47XXXkNBQQEqKytjuh4jPWGd8gxGirJeTiOWRT4SB4FDhw7h7NmzaGhogE6nS4nBY4zEwgSFwUhRpM5pHDp0CIcOHQr5s7vvvjvkuf/93/895udnpD8s5MVgpCgsp8FQGkxQGIwUheU0GEqDhbwYDIVy++2349VXX4XJZIJer8dXvvIV+Hw+ACynwVAmzG2YwWAwGIS4Gos2O6GwriUGg8FgRATLoTAYDAZDEpigMBgMBkMSmKAwGAwGQxKYoDAYDAZDEpigMBgMBkMSmKAwGAwGQxKYoDAYDAZDEv5/11RVMCJxSgYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,7))\n",
    "ax = plt.axes(projection='3d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "dc23000d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T17:03:55.352755Z",
     "start_time": "2022-10-14T17:03:55.119434Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='DistanceFromHome', ylabel='Count'>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns \n",
    "fig = plt.figure(figsize=(10, 12))\n",
    "sns.set(style='white')\n",
    "sns.histplot(dataset['DistanceFromHome'], kde=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "073e73b4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T17:19:30.966181Z",
     "start_time": "2022-10-14T17:19:30.954702Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Y    2940\n",
       "Name: Over18, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.Over18.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "09ec8de7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T17:21:11.005725Z",
     "start_time": "2022-10-14T17:21:11.000262Z"
    }
   },
   "outputs": [],
   "source": [
    "dataset.drop(columns = 'Over18', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7766e869",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-10-14T17:21:26.272463Z",
     "start_time": "2022-10-14T17:21:26.264205Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c2636c8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}