in-vehicle coupon recommendation Data Set _Data_Preprocessing_06.05.2022.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c678bc4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import seaborn as sns\n",
    "import scipy\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder , LabelEncoder\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV, KFold\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6380b39a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>destination</th>\n",
       "      <th>passanger</th>\n",
       "      <th>weather</th>\n",
       "      <th>temperature</th>\n",
       "      <th>time</th>\n",
       "      <th>coupon</th>\n",
       "      <th>expiration</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>maritalStatus</th>\n",
       "      <th>...</th>\n",
       "      <th>occupation</th>\n",
       "      <th>income</th>\n",
       "      <th>Bar</th>\n",
       "      <th>CoffeeHouse</th>\n",
       "      <th>CarryAway</th>\n",
       "      <th>RestaurantLessThan20</th>\n",
       "      <th>Restaurant20To50</th>\n",
       "      <th>direction_opp</th>\n",
       "      <th>Y</th>\n",
       "      <th>driving_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Urgent Place</td>\n",
       "      <td>Alone</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Low</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Restaurant(&lt;20)</td>\n",
       "      <td>24h</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Unmarried partner</td>\n",
       "      <td>...</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>$37500 - $49999</td>\n",
       "      <td>never</td>\n",
       "      <td>never</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>4 to 8</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>within 5mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No Urgent Place</td>\n",
       "      <td>Friend(s)</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>High</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Coffee House</td>\n",
       "      <td>2h</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Unmarried partner</td>\n",
       "      <td>...</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>$37500 - $49999</td>\n",
       "      <td>never</td>\n",
       "      <td>never</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>4 to 8</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>within 5mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No Urgent Place</td>\n",
       "      <td>Friend(s)</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>High</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Carry out &amp; Take away</td>\n",
       "      <td>2h</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Unmarried partner</td>\n",
       "      <td>...</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>$37500 - $49999</td>\n",
       "      <td>never</td>\n",
       "      <td>never</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>4 to 8</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>within 15mins</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       destination  passanger weather temperature     time  \\\n",
       "0  No Urgent Place      Alone   Sunny         Low  Evening   \n",
       "1  No Urgent Place  Friend(s)   Sunny        High  Morning   \n",
       "2  No Urgent Place  Friend(s)   Sunny        High  Morning   \n",
       "\n",
       "                  coupon expiration  gender age      maritalStatus  ...  \\\n",
       "0        Restaurant(<20)        24h  Female  21  Unmarried partner  ...   \n",
       "1           Coffee House         2h  Female  21  Unmarried partner  ...   \n",
       "2  Carry out & Take away         2h  Female  21  Unmarried partner  ...   \n",
       "\n",
       "   occupation           income    Bar CoffeeHouse CarryAway  \\\n",
       "0  Unemployed  $37500 - $49999  never       never    1 to 3   \n",
       "1  Unemployed  $37500 - $49999  never       never    1 to 3   \n",
       "2  Unemployed  $37500 - $49999  never       never    1 to 3   \n",
       "\n",
       "  RestaurantLessThan20 Restaurant20To50 direction_opp  Y  driving_distance  \n",
       "0               4 to 8           1 to 3             1  1      within 5mins  \n",
       "1               4 to 8           1 to 3             1  0      within 5mins  \n",
       "2               4 to 8           1 to 3             1  1     within 15mins  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"Preprocessed Data.csv\")\n",
    "df.drop('car',axis=1,inplace=True)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c7007406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12684 entries, 0 to 12683\n",
      "Data columns (total 22 columns):\n",
      " #   Column                Non-Null Count  Dtype \n",
      "---  ------                --------------  ----- \n",
      " 0   destination           12684 non-null  object\n",
      " 1   passanger             12684 non-null  object\n",
      " 2   weather               12684 non-null  object\n",
      " 3   temperature           12684 non-null  object\n",
      " 4   time                  12684 non-null  object\n",
      " 5   coupon                12684 non-null  object\n",
      " 6   expiration            12684 non-null  object\n",
      " 7   gender                12684 non-null  object\n",
      " 8   age                   12684 non-null  object\n",
      " 9   maritalStatus         12684 non-null  object\n",
      " 10  has_children          12684 non-null  int64 \n",
      " 11  education             12684 non-null  object\n",
      " 12  occupation            12684 non-null  object\n",
      " 13  income                12684 non-null  object\n",
      " 14  Bar                   12684 non-null  object\n",
      " 15  CoffeeHouse           12684 non-null  object\n",
      " 16  CarryAway             12684 non-null  object\n",
      " 17  RestaurantLessThan20  12684 non-null  object\n",
      " 18  Restaurant20To50      12684 non-null  object\n",
      " 19  direction_opp         12684 non-null  int64 \n",
      " 20  Y                     12684 non-null  int64 \n",
      " 21  driving_distance      12684 non-null  object\n",
      "dtypes: int64(3), object(19)\n",
      "memory usage: 2.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2c76ee03",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>destination</th>\n",
       "      <th>passanger</th>\n",
       "      <th>weather</th>\n",
       "      <th>temperature</th>\n",
       "      <th>time</th>\n",
       "      <th>coupon</th>\n",
       "      <th>expiration</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>maritalStatus</th>\n",
       "      <th>...</th>\n",
       "      <th>education</th>\n",
       "      <th>occupation</th>\n",
       "      <th>income</th>\n",
       "      <th>Bar</th>\n",
       "      <th>CoffeeHouse</th>\n",
       "      <th>CarryAway</th>\n",
       "      <th>RestaurantLessThan20</th>\n",
       "      <th>Restaurant20To50</th>\n",
       "      <th>direction_opp</th>\n",
       "      <th>driving_distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No Urgent Place</td>\n",
       "      <td>Alone</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Low</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Restaurant(&lt;20)</td>\n",
       "      <td>24h</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Unmarried partner</td>\n",
       "      <td>...</td>\n",
       "      <td>Some college - no degree</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>$37500 - $49999</td>\n",
       "      <td>never</td>\n",
       "      <td>never</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>4 to 8</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>1</td>\n",
       "      <td>within 5mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>No Urgent Place</td>\n",
       "      <td>Friend(s)</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>High</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Coffee House</td>\n",
       "      <td>2h</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Unmarried partner</td>\n",
       "      <td>...</td>\n",
       "      <td>Some college - no degree</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>$37500 - $49999</td>\n",
       "      <td>never</td>\n",
       "      <td>never</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>4 to 8</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>1</td>\n",
       "      <td>within 5mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No Urgent Place</td>\n",
       "      <td>Friend(s)</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>High</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Carry out &amp; Take away</td>\n",
       "      <td>2h</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Unmarried partner</td>\n",
       "      <td>...</td>\n",
       "      <td>Some college - no degree</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>$37500 - $49999</td>\n",
       "      <td>never</td>\n",
       "      <td>never</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>4 to 8</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>1</td>\n",
       "      <td>within 15mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No Urgent Place</td>\n",
       "      <td>Friend(s)</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>High</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Coffee House</td>\n",
       "      <td>2h</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Unmarried partner</td>\n",
       "      <td>...</td>\n",
       "      <td>Some college - no degree</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>$37500 - $49999</td>\n",
       "      <td>never</td>\n",
       "      <td>never</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>4 to 8</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>1</td>\n",
       "      <td>within 15mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>No Urgent Place</td>\n",
       "      <td>Friend(s)</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>High</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Coffee House</td>\n",
       "      <td>24h</td>\n",
       "      <td>Female</td>\n",
       "      <td>21</td>\n",
       "      <td>Unmarried partner</td>\n",
       "      <td>...</td>\n",
       "      <td>Some college - no degree</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>$37500 - $49999</td>\n",
       "      <td>never</td>\n",
       "      <td>never</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>4 to 8</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>1</td>\n",
       "      <td>within 15mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12679</th>\n",
       "      <td>Home</td>\n",
       "      <td>Partner</td>\n",
       "      <td>Rainy</td>\n",
       "      <td>Low</td>\n",
       "      <td>Evening</td>\n",
       "      <td>Carry out &amp; Take away</td>\n",
       "      <td>24h</td>\n",
       "      <td>Male</td>\n",
       "      <td>26</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>Bachelors degree</td>\n",
       "      <td>Sales &amp; Related</td>\n",
       "      <td>$75000 - $87499</td>\n",
       "      <td>never</td>\n",
       "      <td>never</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>4 to 8</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>0</td>\n",
       "      <td>within 5mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12680</th>\n",
       "      <td>Work</td>\n",
       "      <td>Alone</td>\n",
       "      <td>Rainy</td>\n",
       "      <td>Low</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Carry out &amp; Take away</td>\n",
       "      <td>24h</td>\n",
       "      <td>Male</td>\n",
       "      <td>26</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>Bachelors degree</td>\n",
       "      <td>Sales &amp; Related</td>\n",
       "      <td>$75000 - $87499</td>\n",
       "      <td>never</td>\n",
       "      <td>never</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>4 to 8</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>1</td>\n",
       "      <td>within 5mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12681</th>\n",
       "      <td>Work</td>\n",
       "      <td>Alone</td>\n",
       "      <td>Snowy</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Coffee House</td>\n",
       "      <td>24h</td>\n",
       "      <td>Male</td>\n",
       "      <td>26</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>Bachelors degree</td>\n",
       "      <td>Sales &amp; Related</td>\n",
       "      <td>$75000 - $87499</td>\n",
       "      <td>never</td>\n",
       "      <td>never</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>4 to 8</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>0</td>\n",
       "      <td>within 5mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12682</th>\n",
       "      <td>Work</td>\n",
       "      <td>Alone</td>\n",
       "      <td>Snowy</td>\n",
       "      <td>Medium</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Bar</td>\n",
       "      <td>24h</td>\n",
       "      <td>Male</td>\n",
       "      <td>26</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>Bachelors degree</td>\n",
       "      <td>Sales &amp; Related</td>\n",
       "      <td>$75000 - $87499</td>\n",
       "      <td>never</td>\n",
       "      <td>never</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>4 to 8</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>1</td>\n",
       "      <td>within 25mins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12683</th>\n",
       "      <td>Work</td>\n",
       "      <td>Alone</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>High</td>\n",
       "      <td>Morning</td>\n",
       "      <td>Restaurant(20-50)</td>\n",
       "      <td>2h</td>\n",
       "      <td>Male</td>\n",
       "      <td>26</td>\n",
       "      <td>Single</td>\n",
       "      <td>...</td>\n",
       "      <td>Bachelors degree</td>\n",
       "      <td>Sales &amp; Related</td>\n",
       "      <td>$75000 - $87499</td>\n",
       "      <td>never</td>\n",
       "      <td>never</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>4 to 8</td>\n",
       "      <td>1 to 3</td>\n",
       "      <td>0</td>\n",
       "      <td>within 5mins</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12684 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           destination  passanger weather temperature     time  \\\n",
       "0      No Urgent Place      Alone   Sunny         Low  Evening   \n",
       "1      No Urgent Place  Friend(s)   Sunny        High  Morning   \n",
       "2      No Urgent Place  Friend(s)   Sunny        High  Morning   \n",
       "3      No Urgent Place  Friend(s)   Sunny        High  Evening   \n",
       "4      No Urgent Place  Friend(s)   Sunny        High  Evening   \n",
       "...                ...        ...     ...         ...      ...   \n",
       "12679             Home    Partner   Rainy         Low  Evening   \n",
       "12680             Work      Alone   Rainy         Low  Morning   \n",
       "12681             Work      Alone   Snowy      Medium  Morning   \n",
       "12682             Work      Alone   Snowy      Medium  Morning   \n",
       "12683             Work      Alone   Sunny        High  Morning   \n",
       "\n",
       "                      coupon expiration  gender age      maritalStatus  ...  \\\n",
       "0            Restaurant(<20)        24h  Female  21  Unmarried partner  ...   \n",
       "1               Coffee House         2h  Female  21  Unmarried partner  ...   \n",
       "2      Carry out & Take away         2h  Female  21  Unmarried partner  ...   \n",
       "3               Coffee House         2h  Female  21  Unmarried partner  ...   \n",
       "4               Coffee House        24h  Female  21  Unmarried partner  ...   \n",
       "...                      ...        ...     ...  ..                ...  ...   \n",
       "12679  Carry out & Take away        24h    Male  26             Single  ...   \n",
       "12680  Carry out & Take away        24h    Male  26             Single  ...   \n",
       "12681           Coffee House        24h    Male  26             Single  ...   \n",
       "12682                    Bar        24h    Male  26             Single  ...   \n",
       "12683      Restaurant(20-50)         2h    Male  26             Single  ...   \n",
       "\n",
       "                      education       occupation           income    Bar  \\\n",
       "0      Some college - no degree       Unemployed  $37500 - $49999  never   \n",
       "1      Some college - no degree       Unemployed  $37500 - $49999  never   \n",
       "2      Some college - no degree       Unemployed  $37500 - $49999  never   \n",
       "3      Some college - no degree       Unemployed  $37500 - $49999  never   \n",
       "4      Some college - no degree       Unemployed  $37500 - $49999  never   \n",
       "...                         ...              ...              ...    ...   \n",
       "12679          Bachelors degree  Sales & Related  $75000 - $87499  never   \n",
       "12680          Bachelors degree  Sales & Related  $75000 - $87499  never   \n",
       "12681          Bachelors degree  Sales & Related  $75000 - $87499  never   \n",
       "12682          Bachelors degree  Sales & Related  $75000 - $87499  never   \n",
       "12683          Bachelors degree  Sales & Related  $75000 - $87499  never   \n",
       "\n",
       "      CoffeeHouse CarryAway RestaurantLessThan20 Restaurant20To50  \\\n",
       "0           never    1 to 3               4 to 8           1 to 3   \n",
       "1           never    1 to 3               4 to 8           1 to 3   \n",
       "2           never    1 to 3               4 to 8           1 to 3   \n",
       "3           never    1 to 3               4 to 8           1 to 3   \n",
       "4           never    1 to 3               4 to 8           1 to 3   \n",
       "...           ...       ...                  ...              ...   \n",
       "12679       never    1 to 3               4 to 8           1 to 3   \n",
       "12680       never    1 to 3               4 to 8           1 to 3   \n",
       "12681       never    1 to 3               4 to 8           1 to 3   \n",
       "12682       never    1 to 3               4 to 8           1 to 3   \n",
       "12683       never    1 to 3               4 to 8           1 to 3   \n",
       "\n",
       "      direction_opp  driving_distance  \n",
       "0                 1      within 5mins  \n",
       "1                 1      within 5mins  \n",
       "2                 1     within 15mins  \n",
       "3                 1     within 15mins  \n",
       "4                 1     within 15mins  \n",
       "...             ...               ...  \n",
       "12679             0      within 5mins  \n",
       "12680             1      within 5mins  \n",
       "12681             0      within 5mins  \n",
       "12682             1     within 25mins  \n",
       "12683             0      within 5mins  \n",
       "\n",
       "[12684 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=df.drop(\"Y\",axis=1)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5529d6f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        0\n",
       "2        1\n",
       "3        0\n",
       "4        0\n",
       "        ..\n",
       "12679    1\n",
       "12680    1\n",
       "12681    0\n",
       "12682    0\n",
       "12683    0\n",
       "Name: Y, Length: 12684, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=df[\"Y\"]\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab645e6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['destination', 'passanger', 'weather', 'temperature', 'time', 'coupon',\n",
       "       'expiration', 'gender', 'age', 'maritalStatus', 'has_children',\n",
       "       'education', 'occupation', 'income', 'Bar', 'CoffeeHouse', 'CarryAway',\n",
       "       'RestaurantLessThan20', 'Restaurant20To50', 'direction_opp', 'Y',\n",
       "       'driving_distance'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "52bc0dd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()\n",
    "X[\"gender\"]=le.fit_transform(X[\"gender\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ca31f964",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        0\n",
       "2        0\n",
       "3        0\n",
       "4        0\n",
       "        ..\n",
       "12679    1\n",
       "12680    1\n",
       "12681    1\n",
       "12682    1\n",
       "12683    1\n",
       "Name: gender, Length: 12684, dtype: int32"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"gender\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9ac7862e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['gender'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes[X.dtypes==\"int\"].index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "38b93e7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "X[\"has_children\"]=le.fit_transform(X[\"has_children\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "359e55c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        1\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "        ..\n",
       "12679    0\n",
       "12680    0\n",
       "12681    0\n",
       "12682    0\n",
       "12683    0\n",
       "Name: has_children, Length: 12684, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"has_children\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c388d000",
   "metadata": {},
   "outputs": [],
   "source": [
    "X[\"direction_opp\"]=le.fit_transform(X[\"direction_opp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1c23efb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        1\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "        ..\n",
       "12679    0\n",
       "12680    1\n",
       "12681    0\n",
       "12682    1\n",
       "12683    0\n",
       "Name: direction_opp, Length: 12684, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[\"direction_opp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c6efb4ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Encoding ordinal attributes\n",
    "income_order = ['Less than $12500', '$12500 - $24999', \n",
    "                '$25000 - $37499', '$37500 - $49999', \n",
    "                '$50000 - $62499', '$62500 - $74999', \n",
    "                '$75000 - $87499', '$87500 - $99999', \n",
    "                '$100000 or More']\n",
    "edu_order = ['Some High School', 'High School Graduate', \n",
    "             'Some college - no degree', 'Associates degree', \n",
    "             'Bachelors degree', \n",
    "             'Graduate degree (Masters or Doctorate)']\n",
    "age_order = ['below21', '21', '26', '31', '36', '41', '46', '50plus']\n",
    "time_order = [\"Morning\",\"Evening\",\"Night\"]\n",
    "expiration_order = ['2h', '24h']\n",
    "driving_distance_order = [\"within 5mins\",\"within 15mins\",\"within 25mins\"]\n",
    "\n",
    "def reorder_encoding(col,order):\n",
    "    df[col] = df[col].astype(\"category\")\n",
    "    df[col] = df[col].cat.reorder_categories(new_categories = order,ordered = True)\n",
    "    return pd.Series(df[col].cat.codes,name=col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8d4b7f76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        3\n",
       "1        3\n",
       "2        3\n",
       "3        3\n",
       "4        3\n",
       "        ..\n",
       "12679    6\n",
       "12680    6\n",
       "12681    6\n",
       "12682    6\n",
       "12683    6\n",
       "Name: income, Length: 12684, dtype: int8"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_income = reorder_encoding(\"income\",income_order)\n",
    "X_income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8fd9338f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        2\n",
       "1        2\n",
       "2        2\n",
       "3        2\n",
       "4        2\n",
       "        ..\n",
       "12679    4\n",
       "12680    4\n",
       "12681    4\n",
       "12682    4\n",
       "12683    4\n",
       "Name: education, Length: 12684, dtype: int8"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_education = reorder_encoding(\"education\",edu_order)\n",
    "X_education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "38961604",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        1\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "        ..\n",
       "12679    2\n",
       "12680    2\n",
       "12681    2\n",
       "12682    2\n",
       "12683    2\n",
       "Name: age, Length: 12684, dtype: int8"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_age = reorder_encoding(\"age\",age_order)\n",
    "X_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc3bae7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        0\n",
       "2        0\n",
       "3        1\n",
       "4        1\n",
       "        ..\n",
       "12679    1\n",
       "12680    0\n",
       "12681    0\n",
       "12682    0\n",
       "12683    0\n",
       "Name: time, Length: 12684, dtype: int8"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_time = reorder_encoding(\"time\",time_order)\n",
    "X_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "30290f5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        0\n",
       "2        0\n",
       "3        0\n",
       "4        1\n",
       "        ..\n",
       "12679    1\n",
       "12680    1\n",
       "12681    1\n",
       "12682    1\n",
       "12683    0\n",
       "Name: expiration, Length: 12684, dtype: int8"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_expiration = reorder_encoding(\"expiration\",expiration_order)\n",
    "X_expiration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7bba7ffc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0\n",
       "1        0\n",
       "2        1\n",
       "3        1\n",
       "4        1\n",
       "        ..\n",
       "12679    0\n",
       "12680    0\n",
       "12681    0\n",
       "12682    2\n",
       "12683    0\n",
       "Name: driving_distance, Length: 12684, dtype: int8"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_driving_distance = reorder_encoding(\"driving_distance\",driving_distance_order)\n",
    "X_driving_distance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b04bbce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "frequency_cols = ['Bar', 'CoffeeHouse', 'CarryAway', \n",
    "                   'RestaurantLessThan20', 'Restaurant20To50']\n",
    "freq_order = {\n",
    "    'never': 0, \n",
    "    'Atmost 1': 1,\n",
    "    '1 to 3': 2, \n",
    "    '4 to 8': 3, \n",
    "    'Greater than 8': 4\n",
    "}\n",
    "\n",
    "for col in frequency_cols:\n",
    "    X[col] = X[col].map(freq_order)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "431bfcb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "nominal_col = ['destination', 'passanger', 'weather', 'coupon', \n",
    "                 'maritalStatus', 'occupation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a8e13539",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_nominal = pd.get_dummies(X[nominal_col])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cf6322db",
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_df = pd.concat(\n",
    "    [X_nominal,\n",
    "    X[frequency_cols],\n",
    "    X_income,\n",
    "    X_education,\n",
    "    X_age,\n",
    "    X_time,\n",
    "    X_expiration,\n",
    "    X_driving_distance,\n",
    "    X[\"gender\"],\n",
    "    X[\"has_children\"],\n",
    "    X[\"direction_opp\"],y],axis=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d04e8c7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12684, 60)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e235055d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>destination_Home</th>\n",
       "      <th>destination_No Urgent Place</th>\n",
       "      <th>destination_Work</th>\n",
       "      <th>passanger_Alone</th>\n",
       "      <th>passanger_Friend(s)</th>\n",
       "      <th>passanger_Kid(s)</th>\n",
       "      <th>passanger_Partner</th>\n",
       "      <th>weather_Rainy</th>\n",
       "      <th>weather_Snowy</th>\n",
       "      <th>weather_Sunny</th>\n",
       "      <th>...</th>\n",
       "      <th>income</th>\n",
       "      <th>education</th>\n",
       "      <th>age</th>\n",
       "      <th>time</th>\n",
       "      <th>expiration</th>\n",
       "      <th>driving_distance</th>\n",
       "      <th>gender</th>\n",
       "      <th>has_children</th>\n",
       "      <th>direction_opp</th>\n",
       "      <th>Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12679</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12680</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12681</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12682</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12683</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12684 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       destination_Home  destination_No Urgent Place  destination_Work  \\\n",
       "0                     0                            1                 0   \n",
       "1                     0                            1                 0   \n",
       "2                     0                            1                 0   \n",
       "3                     0                            1                 0   \n",
       "4                     0                            1                 0   \n",
       "...                 ...                          ...               ...   \n",
       "12679                 1                            0                 0   \n",
       "12680                 0                            0                 1   \n",
       "12681                 0                            0                 1   \n",
       "12682                 0                            0                 1   \n",
       "12683                 0                            0                 1   \n",
       "\n",
       "       passanger_Alone  passanger_Friend(s)  passanger_Kid(s)  \\\n",
       "0                    1                    0                 0   \n",
       "1                    0                    1                 0   \n",
       "2                    0                    1                 0   \n",
       "3                    0                    1                 0   \n",
       "4                    0                    1                 0   \n",
       "...                ...                  ...               ...   \n",
       "12679                0                    0                 0   \n",
       "12680                1                    0                 0   \n",
       "12681                1                    0                 0   \n",
       "12682                1                    0                 0   \n",
       "12683                1                    0                 0   \n",
       "\n",
       "       passanger_Partner  weather_Rainy  weather_Snowy  weather_Sunny  ...  \\\n",
       "0                      0              0              0              1  ...   \n",
       "1                      0              0              0              1  ...   \n",
       "2                      0              0              0              1  ...   \n",
       "3                      0              0              0              1  ...   \n",
       "4                      0              0              0              1  ...   \n",
       "...                  ...            ...            ...            ...  ...   \n",
       "12679                  1              1              0              0  ...   \n",
       "12680                  0              1              0              0  ...   \n",
       "12681                  0              0              1              0  ...   \n",
       "12682                  0              0              1              0  ...   \n",
       "12683                  0              0              0              1  ...   \n",
       "\n",
       "       income  education  age  time  expiration  driving_distance  gender  \\\n",
       "0           3          2    1     1           1                 0       0   \n",
       "1           3          2    1     0           0                 0       0   \n",
       "2           3          2    1     0           0                 1       0   \n",
       "3           3          2    1     1           0                 1       0   \n",
       "4           3          2    1     1           1                 1       0   \n",
       "...       ...        ...  ...   ...         ...               ...     ...   \n",
       "12679       6          4    2     1           1                 0       1   \n",
       "12680       6          4    2     0           1                 0       1   \n",
       "12681       6          4    2     0           1                 0       1   \n",
       "12682       6          4    2     0           1                 2       1   \n",
       "12683       6          4    2     0           0                 0       1   \n",
       "\n",
       "       has_children  direction_opp  Y  \n",
       "0                 1              1  1  \n",
       "1                 1              1  0  \n",
       "2                 1              1  1  \n",
       "3                 1              1  0  \n",
       "4                 1              1  0  \n",
       "...             ...            ... ..  \n",
       "12679             0              0  1  \n",
       "12680             0              1  1  \n",
       "12681             0              0  0  \n",
       "12682             0              1  0  \n",
       "12683             0              0  0  \n",
       "\n",
       "[12684 rows x 60 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1ba43dee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12684 entries, 0 to 12683\n",
      "Data columns (total 60 columns):\n",
      " #   Column                                                Non-Null Count  Dtype\n",
      "---  ------                                                --------------  -----\n",
      " 0   destination_Home                                      12684 non-null  uint8\n",
      " 1   destination_No Urgent Place                           12684 non-null  uint8\n",
      " 2   destination_Work                                      12684 non-null  uint8\n",
      " 3   passanger_Alone                                       12684 non-null  uint8\n",
      " 4   passanger_Friend(s)                                   12684 non-null  uint8\n",
      " 5   passanger_Kid(s)                                      12684 non-null  uint8\n",
      " 6   passanger_Partner                                     12684 non-null  uint8\n",
      " 7   weather_Rainy                                         12684 non-null  uint8\n",
      " 8   weather_Snowy                                         12684 non-null  uint8\n",
      " 9   weather_Sunny                                         12684 non-null  uint8\n",
      " 10  coupon_Bar                                            12684 non-null  uint8\n",
      " 11  coupon_Carry out & Take away                          12684 non-null  uint8\n",
      " 12  coupon_Coffee House                                   12684 non-null  uint8\n",
      " 13  coupon_Restaurant(20-50)                              12684 non-null  uint8\n",
      " 14  coupon_Restaurant(<20)                                12684 non-null  uint8\n",
      " 15  maritalStatus_Divorced                                12684 non-null  uint8\n",
      " 16  maritalStatus_Married partner                         12684 non-null  uint8\n",
      " 17  maritalStatus_Single                                  12684 non-null  uint8\n",
      " 18  maritalStatus_Unmarried partner                       12684 non-null  uint8\n",
      " 19  maritalStatus_Widowed                                 12684 non-null  uint8\n",
      " 20  occupation_Architecture & Engineering                 12684 non-null  uint8\n",
      " 21  occupation_Arts Design Entertainment Sports & Media   12684 non-null  uint8\n",
      " 22  occupation_Building & Grounds Cleaning & Maintenance  12684 non-null  uint8\n",
      " 23  occupation_Business & Financial                       12684 non-null  uint8\n",
      " 24  occupation_Community & Social Services                12684 non-null  uint8\n",
      " 25  occupation_Computer & Mathematical                    12684 non-null  uint8\n",
      " 26  occupation_Construction & Extraction                  12684 non-null  uint8\n",
      " 27  occupation_Education&Training&Library                 12684 non-null  uint8\n",
      " 28  occupation_Farming Fishing & Forestry                 12684 non-null  uint8\n",
      " 29  occupation_Food Preparation & Serving Related         12684 non-null  uint8\n",
      " 30  occupation_Healthcare Practitioners & Technical       12684 non-null  uint8\n",
      " 31  occupation_Healthcare Support                         12684 non-null  uint8\n",
      " 32  occupation_Installation Maintenance & Repair          12684 non-null  uint8\n",
      " 33  occupation_Legal                                      12684 non-null  uint8\n",
      " 34  occupation_Life Physical Social Science               12684 non-null  uint8\n",
      " 35  occupation_Management                                 12684 non-null  uint8\n",
      " 36  occupation_Office & Administrative Support            12684 non-null  uint8\n",
      " 37  occupation_Personal Care & Service                    12684 non-null  uint8\n",
      " 38  occupation_Production Occupations                     12684 non-null  uint8\n",
      " 39  occupation_Protective Service                         12684 non-null  uint8\n",
      " 40  occupation_Retired                                    12684 non-null  uint8\n",
      " 41  occupation_Sales & Related                            12684 non-null  uint8\n",
      " 42  occupation_Student                                    12684 non-null  uint8\n",
      " 43  occupation_Transportation & Material Moving           12684 non-null  uint8\n",
      " 44  occupation_Unemployed                                 12684 non-null  uint8\n",
      " 45  Bar                                                   12684 non-null  int64\n",
      " 46  CoffeeHouse                                           12684 non-null  int64\n",
      " 47  CarryAway                                             12684 non-null  int64\n",
      " 48  RestaurantLessThan20                                  12684 non-null  int64\n",
      " 49  Restaurant20To50                                      12684 non-null  int64\n",
      " 50  income                                                12684 non-null  int8 \n",
      " 51  education                                             12684 non-null  int8 \n",
      " 52  age                                                   12684 non-null  int8 \n",
      " 53  time                                                  12684 non-null  int8 \n",
      " 54  expiration                                            12684 non-null  int8 \n",
      " 55  driving_distance                                      12684 non-null  int8 \n",
      " 56  gender                                                12684 non-null  int32\n",
      " 57  has_children                                          12684 non-null  int64\n",
      " 58  direction_opp                                         12684 non-null  int64\n",
      " 59  Y                                                     12684 non-null  int64\n",
      "dtypes: int32(1), int64(8), int8(6), uint8(45)\n",
      "memory usage: 1.4 MB\n"
     ]
    }
   ],
   "source": [
    "one_hot_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c9e804b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>destination_Home</th>\n",
       "      <th>destination_No Urgent Place</th>\n",
       "      <th>destination_Work</th>\n",
       "      <th>passanger_Alone</th>\n",
       "      <th>passanger_Friend(s)</th>\n",
       "      <th>passanger_Kid(s)</th>\n",
       "      <th>passanger_Partner</th>\n",
       "      <th>weather_Rainy</th>\n",
       "      <th>weather_Snowy</th>\n",
       "      <th>weather_Sunny</th>\n",
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       "      <th>education</th>\n",
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       "      <th>expiration</th>\n",
       "      <th>driving_distance</th>\n",
       "      <th>gender</th>\n",
       "      <th>has_children</th>\n",
       "      <th>direction_opp</th>\n",
       "      <th>Y</th>\n",
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       "  </thead>\n",
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       "      <th>98</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11939</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11942</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11943</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12182</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       destination_Home  destination_No Urgent Place  destination_Work  \\\n",
       "32                    0                            1                 0   \n",
       "98                    0                            1                 0   \n",
       "120                   0                            1                 0   \n",
       "164                   0                            1                 0   \n",
       "186                   0                            1                 0   \n",
       "...                 ...                          ...               ...   \n",
       "11815                 1                            0                 0   \n",
       "11939                 0                            1                 0   \n",
       "11942                 1                            0                 0   \n",
       "11943                 1                            0                 0   \n",
       "12182                 1                            0                 0   \n",
       "\n",
       "       passanger_Alone  passanger_Friend(s)  passanger_Kid(s)  \\\n",
       "32                   0                    1                 0   \n",
       "98                   0                    1                 0   \n",
       "120                  0                    1                 0   \n",
       "164                  0                    1                 0   \n",
       "186                  0                    1                 0   \n",
       "...                ...                  ...               ...   \n",
       "11815                1                    0                 0   \n",
       "11939                0                    0                 0   \n",
       "11942                1                    0                 0   \n",
       "11943                1                    0                 0   \n",
       "12182                1                    0                 0   \n",
       "\n",
       "       passanger_Partner  weather_Rainy  weather_Snowy  weather_Sunny  ...  \\\n",
       "32                     0              0              0              1  ...   \n",
       "98                     0              0              0              1  ...   \n",
       "120                    0              0              0              1  ...   \n",
       "164                    0              0              0              1  ...   \n",
       "186                    0              0              0              1  ...   \n",
       "...                  ...            ...            ...            ...  ...   \n",
       "11815                  0              0              0              1  ...   \n",
       "11939                  1              0              0              1  ...   \n",
       "11942                  0              0              0              1  ...   \n",
       "11943                  0              0              0              1  ...   \n",
       "12182                  0              0              0              1  ...   \n",
       "\n",
       "       income  education  age  time  expiration  driving_distance  gender  \\\n",
       "32          5          4    1     1           0                 0       1   \n",
       "98          1          3    1     1           0                 0       1   \n",
       "120         4          4    2     1           0                 0       1   \n",
       "164         3          2    2     1           0                 0       1   \n",
       "186         2          3    2     1           0                 0       0   \n",
       "...       ...        ...  ...   ...         ...               ...     ...   \n",
       "11815       8          3    4     1           1                 0       1   \n",
       "11939       8          2    3     0           1                 0       0   \n",
       "11942       8          2    3     1           0                 1       0   \n",
       "11943       8          2    3     1           1                 0       0   \n",
       "12182       3          1    2     1           0                 1       1   \n",
       "\n",
       "       has_children  direction_opp  Y  \n",
       "32                0              1  0  \n",
       "98                0              1  1  \n",
       "120               0              1  0  \n",
       "164               0              1  0  \n",
       "186               1              1  1  \n",
       "...             ...            ... ..  \n",
       "11815             1              1  1  \n",
       "11939             0              1  1  \n",
       "11942             0              0  0  \n",
       "11943             0              1  0  \n",
       "12182             0              0  0  \n",
       "\n",
       "[155 rows x 60 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_df[one_hot_df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ce9874a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_df.drop_duplicates(keep=\"first\",inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9a52e579",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12529, 60)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "21125169",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "destination_Home                                        0\n",
       "destination_No Urgent Place                             0\n",
       "destination_Work                                        0\n",
       "passanger_Alone                                         0\n",
       "passanger_Friend(s)                                     0\n",
       "passanger_Kid(s)                                        0\n",
       "passanger_Partner                                       0\n",
       "weather_Rainy                                           0\n",
       "weather_Snowy                                           0\n",
       "weather_Sunny                                           0\n",
       "coupon_Bar                                              0\n",
       "coupon_Carry out & Take away                            0\n",
       "coupon_Coffee House                                     0\n",
       "coupon_Restaurant(20-50)                                0\n",
       "coupon_Restaurant(<20)                                  0\n",
       "maritalStatus_Divorced                                  0\n",
       "maritalStatus_Married partner                           0\n",
       "maritalStatus_Single                                    0\n",
       "maritalStatus_Unmarried partner                         0\n",
       "maritalStatus_Widowed                                   0\n",
       "occupation_Architecture & Engineering                   0\n",
       "occupation_Arts Design Entertainment Sports & Media     0\n",
       "occupation_Building & Grounds Cleaning & Maintenance    0\n",
       "occupation_Business & Financial                         0\n",
       "occupation_Community & Social Services                  0\n",
       "occupation_Computer & Mathematical                      0\n",
       "occupation_Construction & Extraction                    0\n",
       "occupation_Education&Training&Library                   0\n",
       "occupation_Farming Fishing & Forestry                   0\n",
       "occupation_Food Preparation & Serving Related           0\n",
       "occupation_Healthcare Practitioners & Technical         0\n",
       "occupation_Healthcare Support                           0\n",
       "occupation_Installation Maintenance & Repair            0\n",
       "occupation_Legal                                        0\n",
       "occupation_Life Physical Social Science                 0\n",
       "occupation_Management                                   0\n",
       "occupation_Office & Administrative Support              0\n",
       "occupation_Personal Care & Service                      0\n",
       "occupation_Production Occupations                       0\n",
       "occupation_Protective Service                           0\n",
       "occupation_Retired                                      0\n",
       "occupation_Sales & Related                              0\n",
       "occupation_Student                                      0\n",
       "occupation_Transportation & Material Moving             0\n",
       "occupation_Unemployed                                   0\n",
       "Bar                                                     0\n",
       "CoffeeHouse                                             0\n",
       "CarryAway                                               0\n",
       "RestaurantLessThan20                                    0\n",
       "Restaurant20To50                                        0\n",
       "income                                                  0\n",
       "education                                               0\n",
       "age                                                     0\n",
       "time                                                    0\n",
       "expiration                                              0\n",
       "driving_distance                                        0\n",
       "gender                                                  0\n",
       "has_children                                            0\n",
       "direction_opp                                           0\n",
       "Y                                                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_hot_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "537799f6",
   "metadata": {},
   "source": [
    "# Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08e96db8",
   "metadata": {},
   "source": [
    "### chi- square method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8c0b2ead",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                              feature       score\n",
      "11                       coupon_Carry out & Take away  262.485560\n",
      "14                             coupon_Restaurant(<20)  220.250253\n",
      "10                                         coupon_Bar  204.519858\n",
      "4                                 passanger_Friend(s)  157.295517\n",
      "46                                        CoffeeHouse  156.289192\n",
      "1                         destination_No Urgent Place  113.883455\n",
      "55                                   driving_distance  108.986736\n",
      "13                           coupon_Restaurant(20-50)   97.442167\n",
      "54                                         expiration   89.802489\n",
      "12                                coupon_Coffee House   75.197415\n",
      "45                                                Bar   73.465308\n",
      "2                                    destination_Work   61.116083\n",
      "52                                                age   59.472432\n",
      "3                                     passanger_Alone   56.385945\n",
      "7                                       weather_Rainy   53.562495\n",
      "8                                       weather_Snowy   52.727992\n",
      "0                                    destination_Home   50.580495\n",
      "49                                   Restaurant20To50   40.899136\n",
      "9                                       weather_Sunny   27.771807\n",
      "17                               maritalStatus_Single   27.139930\n",
      "40                                 occupation_Retired   23.628834\n",
      "51                                          education   18.623499\n",
      "31                      occupation_Healthcare Support   17.051627\n",
      "5                                    passanger_Kid(s)   16.431849\n",
      "50                                             income   15.771650\n",
      "57                                       has_children   15.543273\n",
      "16                      maritalStatus_Married partner   13.704385\n",
      "56                                             gender   13.047190\n",
      "30    occupation_Healthcare Practitioners & Technical   10.995912\n",
      "42                                 occupation_Student   10.831122\n",
      "26               occupation_Construction & Extraction    9.718839\n",
      "48                               RestaurantLessThan20    8.680679\n",
      "33                                   occupation_Legal    8.509077\n",
      "27              occupation_Education&Training&Library    8.315447\n",
      "21  occupation_Arts Design Entertainment Sports & ...    6.823945\n",
      "24             occupation_Community & Social Services    6.646409\n",
      "47                                          CarryAway    5.748519\n",
      "39                      occupation_Protective Service    4.448825\n",
      "44                              occupation_Unemployed    3.713806\n",
      "19                              maritalStatus_Widowed    3.644599\n",
      "15                             maritalStatus_Divorced    3.562136\n",
      "20              occupation_Architecture & Engineering    3.248143\n",
      "6                                   passanger_Partner    2.868159\n",
      "36         occupation_Office & Administrative Support    2.656162\n",
      "35                              occupation_Management    1.341078\n",
      "38                  occupation_Production Occupations    1.208867\n",
      "43        occupation_Transportation & Material Moving    0.621389\n",
      "32       occupation_Installation Maintenance & Repair    0.603112\n",
      "58                                      direction_opp    0.585456\n",
      "18                    maritalStatus_Unmarried partner    0.352031\n",
      "37                 occupation_Personal Care & Service    0.268159\n",
      "29      occupation_Food Preparation & Serving Related    0.255507\n",
      "28              occupation_Farming Fishing & Forestry    0.191744\n",
      "22  occupation_Building & Grounds Cleaning & Maint...    0.094418\n",
      "34            occupation_Life Physical Social Science    0.060736\n",
      "53                                               time    0.021739\n",
      "41                         occupation_Sales & Related    0.010877\n",
      "23                    occupation_Business & Financial    0.010195\n",
      "25                 occupation_Computer & Mathematical    0.000021\n",
      "59\n",
      "6\n",
      "number of original features: 59\n",
      "number of reduced features: 6\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import SelectPercentile\n",
    "from sklearn.feature_selection import chi2\n",
    "from sklearn.feature_selection import f_classif\n",
    "import numpy as np\n",
    "\n",
    "X = one_hot_df.iloc[:,0:-1]\n",
    "y = one_hot_df.iloc[:,-1]\n",
    "\n",
    "\n",
    "X = X.astype(int)\n",
    "\n",
    "\n",
    "chi2_selector = SelectPercentile(chi2)\n",
    "kBest = chi2_selector.fit_transform(X, y)\n",
    "\n",
    "chi2_scores = pd.DataFrame(list(zip(X.columns, chi2_selector.scores_)), columns=['feature', 'score'])\n",
    "print(chi2_scores.sort_values(by=\"score\",ascending=False))\n",
    "\n",
    "\n",
    "print(X.shape[1])\n",
    "print(kBest.shape[1])\n",
    "print('number of original features:', X.shape[1])\n",
    "print('number of reduced features:', kBest.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fa807d5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['destination_No Urgent Place', 'passanger_Friend(s)', 'coupon_Bar',\n",
       "       'coupon_Carry out & Take away', 'coupon_Restaurant(<20)',\n",
       "       'CoffeeHouse'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns[chi2_selector.get_support()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee1f637a",
   "metadata": {},
   "source": [
    "### Recurssive Feature Elimination (RFE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "082fb557",
   "metadata": {},
   "source": [
    "# Q.can we take LInear regression here?if so, in linearRegression fit, should we take total X, or X_train after splitting? why is it taking first 7 features?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "517fd189",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True  True  True  True  True  True  True False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False]\n",
      "[ 1  1  1  1  1  1  1 38 37 39 32 36 34 33 35  5  4  2  3  6 11 28 17 21\n",
      " 29 19  7 26 20 15  9  8 27 31 22 16 18 24 12 10 30 23 13 14 25 49 42 48\n",
      " 52 45 53 46 51 44 40 47 43 50 41]\n",
      "Num Features: 7\n",
      "Selected Features: [ True  True  True  True  True  True  True False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False False\n",
      " False False False False False False False False False False False]\n",
      "Feature Ranking: [ 1  1  1  1  1  1  1 38 37 39 32 36 34 33 35  5  4  2  3  6 11 28 17 21\n",
      " 29 19  7 26 20 15  9  8 27 31 22 16 18 24 12 10 30 23 13 14 25 49 42 48\n",
      " 52 45 53 46 51 44 40 47 43 50 41]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.feature_selection import RFECV\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "\n",
    "lin_reg = LinearRegression()\n",
    "\n",
    "rfe_mod = RFECV(lin_reg,cv=2) \n",
    "myvalues=rfe_mod.fit(X,y) \n",
    "print(myvalues.support_)\n",
    "print(myvalues.ranking_)\n",
    "\n",
    "print(\"Num Features: %s\" % (myvalues.n_features_))\n",
    "print(\"Selected Features: %s\" % (myvalues.support_))\n",
    "print(\"Feature Ranking: %s\" % (myvalues.ranking_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e7d2368e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['destination_Home', 'destination_No Urgent Place', 'destination_Work',\n",
       "       'passanger_Alone', 'passanger_Friend(s)', 'passanger_Kid(s)',\n",
       "       'passanger_Partner'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns[myvalues.get_support()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d1f29d",
   "metadata": {},
   "source": [
    "### Using Random Forest To select Features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735c65fa",
   "metadata": {},
   "source": [
    "# Q.Should we take encoded columns or normal data for random forest?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f97b4900",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.01143778 0.01536847 0.01021982 0.01148598 0.01272474 0.0076709\n",
      " 0.00744681 0.00854041 0.00855078 0.01341441 0.02369709 0.02647598\n",
      " 0.02310847 0.01371139 0.02303549 0.0049044  0.01526469 0.01531761\n",
      " 0.01332954 0.00178388 0.00410857 0.00800348 0.0008028  0.00698843\n",
      " 0.00341838 0.01267889 0.00232357 0.0106856  0.0008703  0.00397885\n",
      " 0.00411799 0.00398975 0.00283052 0.00437247 0.00366018 0.00944372\n",
      " 0.00774126 0.00275694 0.00174654 0.00248146 0.00619013 0.01299815\n",
      " 0.01263234 0.00343179 0.0143088  0.05211128 0.06701794 0.04429742\n",
      " 0.04357938 0.0427362  0.06773561 0.04822792 0.05818755 0.03596338\n",
      " 0.03394639 0.03749359 0.02182491 0.01703922 0.01578969]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators = 100,random_state=42)\n",
    "rf.fit(X, y)\n",
    "importances = rf.feature_importances_\n",
    "print(importances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "48f2f257",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Mean decrease in impurity')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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2nyzpjxFxtu1HjdxJrrO7myRttNFGHQ4TAAAAADDLhjZsbT86Ir5v+xmDXo+Ir7Vs+0pJGzYebyDpd5XveZakp9p+oqTVJK1p+4sR8fwBx3GApAMkaYsttuhvOAMAAAAAVnCjRmwfqcyG/JQBr4WktobtmZI2tX1XSb+VtJPmwpp7jpL0atuHKcOUr42Iq5Rhz2+SpDJi+7pBjVoAAAAAAIY2bCPireXfXbtsOCJusv1qScdLWiTp4Ii42PYryuv7SzpW0hMlXSrpBkmd9gUAAAAA+PdVkzyqs4g4Vtl4bT63f+P/IWn3lm2cJOmkKRweAAAAAGAFULPcDwAAAAAAt1g0bAEAAAAAM60qFNn2wyUtbr4/Ij4/pWMCAAAAAKBaa8PW9hckbSLpPEk3l6dDEg1bAAAAAMByVzNiu4WkzUqiJwAAAAAAblFq5theJOnO0z4QAAAAAAC6qBmxvYOkn9g+Q9I/ek9GxFOndlQAAAAAAFSqadjuM+2DAAAAAACgq9aGbUScvCwOBAAAAACALoY2bG2fEhHb2L5emQV5yUuSIiLWnPrRAQAAAADQYmjDNiK2Kf/edtkdDgAAAAAA46mZYwsAAAAAy9zivY8Z+frl733SMjoS3NLVLPcDAAAAAMAtFg1bAAAAAMBMq2rY2t7Y9mPL/1e3zbxbAAAAAMAtQmvD1vbLJB0p6dPlqQ0kfWOKxwQAAAAAQLWaEdvdJW0t6TpJiohfSLrjNA8KAAAAAIBaNQ3bf0TEjb0HtlfW/HVtAQAAAABYbmoatifbfrOk1W1vJ+kISUdP97AAAAAAAKhT07DdW9LVki6U9HJJx0r672keFAAAAAAAtVZue0NE/EvSgZIOtH07SRtEBKHIAAAAAFot3vuYoa9d/t4nLcMjwYqsJivySbbXLI3a8yQdYvvDUz8yAAAAAAAq1IQirxUR10l6hqRDIuLBkh473cMCAAAAAKBOTcN2ZdvrSXq2pG9N+XgAAAAAABhLTcP27ZKOl3RpRJxp+26SfjHdwwIAAAAAoE5N8qgjlEv89B5fJumZ0zwoAAAAAABqtTZsba8m6SWS7iNptd7zEfHiKR4XAAAAAABVakKRvyDpzpIeL+lkSRtIun6aBwUAAAAAQK2ahu3dI+Itkv4aEZ+T9CRJ95vuYQEAAAAAUKemYfvP8u81tu8raS1Ji6d2RAAAAAAAjKF1jq2kA2yvI+ktko6StIak/5nqUQEAAAAAUKkmK/Jnyn9PlnS36R4OAAAAAADjqcmKfCdJ75Z0l4jY3vZmkh4WEQdN/egAAAAALHeL9z5m6GuXv/dJy/BIgMFq5th+VtLxku5SHv9c0l5TOh4AAAAAAMZS07C9Q0QcLulfkhQRN0m6eapHBQAAAABApZqG7V9t315SSJLtrSRdO9WjAgAAAACgUk1W5NcqsyFvYvtHktaV9KypHhUAAAAAAJVGNmxtL5L0yPJzT0mWdElE/HNUOQAAAAAAlpWRocgRcbOkHSLipoi4OCIuolELAAAAALglqQlF/pHtT0j6iqS/9p6MiHOmdlQAAAAAAFSqadg+vPz79sZzIenRC384AAAAAACMp7VhGxHbLosDAQAAAACgi9blfmy/2/bajcfr2H7nVI8KAAAAAIBKNevYbh8R1/QeRMSfJT1xakcEAAAAAMAYahq2i2yv2ntge3VJq454PwAAAAAAy0xN8qgvSjrB9iHKpFEvlvS5qR4VAAAAAACVapJHvd/2BZIeK8mS3hERx0/9yAAAAAAAqFAzYitJP5V0U0R8z/atbd82Iq6f5oEBAAAAAFCjJivyyyQdKenT5an1JX1jiscEAAAAAEC1muRRu0vaWtJ1khQRv5B0x2keFAAAAAAAtWoatv+IiBt7D2yvrEwiBQAAAADAclfTsD3Z9pslrW57O0lHSDp6uocFAAAAAECdmobt3pKulnShpJdLOlbSf0/zoAAAAAAAqNXasI2If0XEgRGxY0Q8q/y/KhTZ9hNsX2L7Utt7D3jdtvctr19ge/Py/Gq2z7B9vu2Lbb9t/F8NAAAAAPDvYOhyP7Yv1Ii5tBFx/1Ebtr1I0iclbSfpSkln2j4qIn7SeNv2kjYtPw+VtF/59x+SHh0Rf7F9K0mn2D4uIk6r+7UAAAAAAP8uRq1j++Ty7+7l3y+Uf58n6YaKbW8p6dKIuEySbB8maQdJzYbtDpI+X0aAT7O9tu31IuIqSX8p77lV+SFhFQAAAABgKUNDkSPi1xHxa0lbR8QbIuLC8rO3pMdXbHt9SVc0Hl9Znqt6j+1Fts+T9EdJ342I0yv2CQAAAAD4N1OTPOo2trfpPbD9cEm3qSjnAc/1j7oOfU9E3BwRD5S0gaQtbd934E7s3WyfZfusq6++uuKwAAAAAAArklGhyD0vkXSw7bWUjc5rJb24otyVkjZsPN5A0u/GfU9EXGP7JElPkHRR/04i4gBJB0jSFltsQbgyAAAAAPybqcmKfHZEPEDS/SU9MCIeGBHnVGz7TEmb2r6r7VUk7STpqL73HCXpBSU78laSro2Iq2yva3ttSbK9uqTHSvpZ/a8FAAAAAPh3UTNiK0mKiOvG2XBE3GT71ZKOl7RI0sERcbHtV5TX91euiftESZcqE1LtWoqvJ+lzJbPySpIOj4hvjbN/AAAAAMC/h+qGbRcRcayy8dp8bv/G/0NzWZeb77lA0oOmeWwAAAAAgBVDTfIoAAAAAABusapGbEsm5MXN90fE56d0TAAAAAAAVGtt2Nr+gqRNJJ0n6ebydEiiYQsAAAAAWO5qRmy3kLRZmQ8LAAAAAMAtSs0c24sk3XnaBwIAAAAAQBc1I7Z3kPQT22dI+kfvyYh46tSOCgAAAACASjUN232mfRAAAAAAAHTV2rCNiJOXxYEAAAAAANBF6xxb21vZPtP2X2zfaPtm29cti4MDAAAAAKBNTfKoT0jaWdIvJK0u6aXlOQAAAAAAlruaObaKiEttL4qImyUdYvvUKR8XAAAAAABVahq2N9heRdJ5tt8v6SpJt5nuYQEAAAAAUKcmFHmX8r5XS/qrpA0lPXOaBwUAAAAAQK2arMi/tr26pPUi4m3L4JgAAAAAAKhWkxX5KZLOk/Tt8viBto+a8nEBAAAAAFClJhR5H0lbSrpGkiLiPEmLp3VAAAAAAACMoyZ51E0Rca3tqR8MAAAAgOlYvPcxQ1+7/L1PWoZHAiy8mobtRbafK2mR7U0l7SGJ5X4AAAAAALcINaHIr5F0H0n/kHSopOsk7TXFYwIAAAAAoFpNVuQbJP1X+QEAAAAA4BZlaMO2LfNxRDx14Q8HAAAAAIDxjBqxfZikK5Thx6dLInsUAAAAAOAWZ1TD9s6StpO0s6TnSjpG0qERcfGyODAAAAAAAGoMTR4VETdHxLcj4oWStpJ0qaSTbL9mmR0dAAAAAAAtRiaPsr2qpCcpR20XS9pX0temf1gAAAAAANQZlTzqc5LuK+k4SW+LiIuW2VEBAAAAAFBp1IjtLpL+Kukekvawl+SOsqSIiDWnfGwAAAAAALQa2rCNiKHzbwEAAAAAuKWg8QoAAAAAmGk0bAEAAAAAM42GLQAAAABgptGwBQAAAADMNBq2AAAAAICZNmq5HwAAAAC3EIv3Pmbk65e/90nL6EiAWx5GbAEAAAAAM42GLQAAAABgptGwBQAAAADMNObYAgAAAGOYZK4r82SB6WDEFgAAAAAw02jYAgAAAABmGg1bAAAAAMBMo2ELAAAAAJhpNGwBAAAAADONhi0AAAAAYKbRsAUAAAAAzDTWsQUAAMC/HdaTBVYsjNgCAAAAAGYaDVsAAAAAwEyjYQsAAAAAmGk0bAEAAAAAM42GLQAAAABgptGwBQAAAADMtKk2bG0/wfYlti+1vfeA12173/L6BbY3L89vaPtE2z+1fbHtPad5nAAAAACA2TW1hq3tRZI+KWl7SZtJ2tn2Zn1v217SpuVnN0n7ledvkvT/IuLekraStPuAsgAAAAAATHXEdktJl0bEZRFxo6TDJO3Q954dJH0+0mmS1ra9XkRcFRHnSFJEXC/pp5LWn+KxAgAAAABm1DQbtutLuqLx+Eot3ThtfY/txZIeJOn0hT9EAAAAAMCsm2bD1gOei3HeY3sNSV+VtFdEXDdwJ/Zuts+yfdbVV1/d+WABAAAAALNpmg3bKyVt2Hi8gaTf1b7H9q2UjdovRcTXhu0kIg6IiC0iYot11113QQ4cAAAAADA7ptmwPVPSprbvansVSTtJOqrvPUdJekHJjryVpGsj4irblnSQpJ9GxIeneIwAAAAAgBm38rQ2HBE32X61pOMlLZJ0cERcbPsV5fX9JR0r6YmSLpV0g6RdS/GtJe0i6ULb55Xn3hwRx07reAEAAAAAs2lqDVtJKg3RY/ue27/x/5C0+4Byp2jw/FsAAAAAAOaZZigyAAAAAABTR8MWAAAAADDTaNgCAAAAAGYaDVsAAAAAwEyjYQsAAAAAmGk0bAEAAAAAM42GLQAAAABgptGwBQAAAADMNBq2AAAAAICZRsMWAAAAADDTaNgCAAAAAGYaDVsAAAAAwEyjYQsAAAAAmGkrL+8DAAAAALpYvPcxQ1+7/L1PWoZHAmB5Y8QWAAAAADDTaNgCAAAAAGYaDVsAAAAAwEyjYQsAAAAAmGk0bAEAAAAAM42GLQAAAABgptGwBQAAAADMNNaxBQAAwHLDWrQAFgIjtgAAAACAmcaILQAAACYaOWXUFcDyRsMWAABgBTCqcSnRwASwYiMUGQAAAAAw02jYAgAAAABmGg1bAAAAAMBMo2ELAAAAAJhpNGwBAAAAADONrMgAAAC3EGQ2BoBuGLEFAAAAAMw0GrYAAAAAgJlGKDIAAMACIpwYAJY9RmwBAAAAADONEVsAAIA+jLoCwGyhYQsAAFZIoxqnNEwBYMVCKDIAAAAAYKYxYgsAAG6xGHUFANSgYQsAAKaKxikAYNpo2AIA8G9gkmRIJFICANzSMccWAAAAADDTaNgCAAAAAGYaDVsAAAAAwEyjYQsAAAAAmGk0bAEAAAAAM42GLQAAAABgprHcDwAAyxDL7gAAsPAYsQUAAAAAzDQatgAAAACAmUbDFgAAAAAw02jYAgAAAABm2lSTR9l+gqSPSVok6TMR8d6+111ef6KkGyS9KCLOKa8dLOnJkv4YEfed5nECADCOUUmcSOAEAMCyN7URW9uLJH1S0vaSNpO0s+3N+t62vaRNy89ukvZrvPZZSU+Y1vEBAAAAAFYM0wxF3lLSpRFxWUTcKOkwSTv0vWcHSZ+PdJqktW2vJ0kR8QNJ/zfF4wMAAAAArACmGYq8vqQrGo+vlPTQivesL+mqKR4XAACEEwMAsAKZ5oitBzwXHd4zeif2brbPsn3W1VdfPU5RAAAAAMAKYJoN2yslbdh4vIGk33V4z0gRcUBEbBERW6y77rqdDhQAAAAAMLum2bA9U9Kmtu9qexVJO0k6qu89R0l6gdNWkq6NCMKQAQAAAADVpjbHNiJusv1qSccrl/s5OCIutv2K8vr+ko5VLvVzqXK5n1175W0fKulRku5g+0pJb42Ig6Z1vACA2cM8WQAAIE15HduIOFbZeG0+t3/j/yFp9yFld57msQEAAAAAVgzTDEUGAAAAAGDqaNgCAAAAAGYaDVsAAAAAwEyjYQsAAAAAmGlTTR4FAFjxjcpMLLVnJyazMQAAmBQjtgAAAACAmUbDFgAAAAAw0whFBgBMHE4MAACwPDFiCwAAAACYaYzYAsAtyCQjp4y6AgCAf1eM2AIAAAAAZhojtgCwwFi+BgAAYNlixBYAAAAAMNMYsQWAARh1BQAAmB2M2AIAAAAAZhoNWwAAAADATKNhCwAAAACYaTRsAQAAAAAzjeRRAFZYJIACAAD498CILQAAAABgptGwBQAAAADMNBq2AAAAAICZRsMWAAAAADDTaNgCAAAAAGYaDVsAAAAAwEyjYQsAAAAAmGmsYwvgFmvUOrQSa9ECAAAgMWILAAAAAJhpNGwBAAAAADONUGQAU0U4MQAAAKaNEVsAAAAAwExjxBb4NzHJyCmjrgAAALglo2ELLGOjGoltDUQamAAAAMDSCEUGAAAAAMw0RmyBDiYZdQUAAACwsGjYYmZNGpZL4xQAAABYMRCKDAAAAACYaTRsAQAAAAAzjYYtAAAAAGCmMccWE2N9VAAAAADLEyO2AAAAAICZxogtJDFyCgAAAGB20bBdgbB8DQAAAIB/R4QiAwAAAABmGg1bAAAAAMBMIxR5CiYJCSacGAAAAADGw4gtAAAAAGCm0bAFAAAAAMw0GrYAAAAAgJlGwxYAAAAAMNNo2AIAAAAAZtoKmxV5VHZhaXSG4UnKAgAAAACWramO2Np+gu1LbF9qe+8Br9v2vuX1C2xvXlsWAAAAAABpig1b24skfVLS9pI2k7Sz7c363ra9pE3Lz26S9hujLAAAAAAAUx2x3VLSpRFxWUTcKOkwSTv0vWcHSZ+PdJqktW2vV1kWAAAAAICpNmzXl3RF4/GV5bma99SUBQAAAABAjojpbNjeUdLjI+Kl5fEukraMiNc03nOMpPdExCnl8QmS3iDpbm1lG9vYTRnGLEn3lHTJkEO6g6Q/dfx1llfZ5blvyi6bsstz35SdjX1TdtmUXZ77puxs7Juyy6bs8tw3ZZdN2eW5b8rOxr5Hld04ItYd+EpETOVH0sMkHd94/CZJb+p7z6cl7dx4fImk9WrKdjies2at7KweN2VnY9+UnY19U5a/MWVvGfumLH9jyvI3/ncqO4vHPc1Q5DMlbWr7rrZXkbSTpKP63nOUpBeU7MhbSbo2Iq6qLAsAAAAAwPTWsY2Im2y/WtLxkhZJOjgiLrb9ivL6/pKOlfRESZdKukHSrqPKTutYAQAAAACza2oNW0mKiGOVjdfmc/s3/h+Sdq8tO6EDZrDs8tw3ZZdN2eW5b8rOxr4pu2zKLs99U3Y29k3ZZVN2ee6bssum7PLcN2VnY9+dyk4teRQAAAAAAMvCNOfYAgAAAAAwdTRsAQAAgH8Dthct72MApmWFb9jaXt32PTuU+6Dt+3Tc5ya2Vy3/f5TtPWyv3WVbY+53VdvPtf1m2//T+xmj/Ma2H1v+v7rt207vaJfscyXbF01Qfk/ba5bM2gfZPsf24xbyGFv2f5tlta+yvx1rnsPyVc7rhy+H/e5Z81zLNtaxvaXtR/R+Fu4IZ0Pv+o1k+7Wjfpb38Q1je5Ht/1zex7Esld/5i8v5GJbpfXFW2b617bfYPrA83tT2k5fBri+1/QHbmy2DfWEZK/XhDZf3cSwvK/QcW9tPkfRBSatExF1tP1DS2yPiqRVlX6rM0ryypEMkHRoR11bu9zxJW0harMzsfJSke0bEEzv8GtVsf1vStZLOlnRz7/mI+FBF2ZdJ2k3S7SJiE9ubSto/Ih5TUfYekvaTdKeIuK/t+0t6akS8s/K4v6Rcp/g3Ne/vK3t+RDzA9uOVicjeIumQiNi8ouz1kvq/ANdKOkvS/4uIy0aUfbikz0haIyI2sv0ASS+PiFeNKPPxAftbIiL2qDjmc/p/t0HPDSm774Cnr1WuFfbNIWWO1uhjbv0uzZqF+DuV7fw4Ih62YAdWt89B58e5EfGgyvIvlbSnpA0knSdpK0k/johHV5R9sqRjI+JfHY77dhHxf+OWK2V3jIgj2p4bUf7giHhx4/Eakr5Zee17taQvRcSfOxz3nSS9W9JdImL7Usl8WEQcVFH21pL+n6SNIuJl5Xp9z4j4VkXZE/p/t0HP9b3+1lHbjIi3te23b3u3kfT3iLi55X0Lcc08KSIeNc7x9ZX/qqSDJR3X8dxeR9JdJP1N0uVdttFhn8dLekpE3NixfKfzq8t9sa/87SXtI2lr5d/9FGWd7X8ry68vaWM1EqNGxA9GvH+RpD0i4iM122/Zd9U53VfmK8r62gtK3Wl15fX2gS3lFkl6b0S8vuOx3la5jOauygGugyUdFhHXVZRdVdIzlfXb5uf89i7HMo6u10zb95K0g6T1lefV7yQdFRE/rdjnWpKe0Ff2+Ii4pvKY7xsRnQdvurJ9dkQ8eMJt3CYi/rqsy05qqlmRbwH2kbSlpJMkKSLOs724pmBEfEbSZ5yjvbtKusD2jyQdGBEnthT/V1my6OmSPhoRH7d9bts+hzS0mse0ZssmNoiIJ7TtZ4jdlZ/V6WVfv7B9x8qyB0p6vaRPl7IX2P6ypKqGraT1JF1s+wxJS74IlY0ml3+fqGzQnm/bowo0fFh5kfpy2c5Oku4s6RLlhf5RI8p+RNLjVdZXLvttG9k6q/y7taTNJH2lPN5ReXMbyvb2yt9x/b4G6pqSbmrZb89qku4lqVfhf6akiyW9xPa2EbHXgDIfrNz2QLYv1OBz2srE6PfvWF7KDQwsP2GF+KwRr43jO7afKelrMUYPou2HSXq+pP9Qfjf+JukiScdI+uKgDjbbO0t6rqS72W6u+X1bSVWVwmJPSQ+RdFpEbFsqBLWNlp0kfaw0BA6pqTQ0nF46BA9RNiDG6XF9k+bO6VHPDfNb2/tFxCtLI+QY5TWtxp0lnWn7HOU14/gxjv2zyt/3v8rjnyuvCa0N21LubEm9jpMrlb/v0IaH7dUk3VrSHcrv2btOrqlseA01bsN1wL5XUp4fz1OeX/+QtKrtq5WrHxwQEb8YULTzNbPhR7Y/Uco27y/nVJbfT1kH2Nf2EZI+GxE/G1WgVIZ3l7SzpFUkXa28/t7J9mmSPjWsHrEA9QBJulz5ex+l+b/zhyvKSh3Or6LLfbHpMEk/UN6bpDxfviLpsW0Fbb9P0nMk/URzHftRtjdQRNxse4dy3GOZ4Jxu2iQinlOu34qIv9XUX8pxP9i2x7xW9spfr7zGHVj+PodK+ojtIyW9IyIuHVH8m5obQPnHOPuddBBEHa6Ztt+o/B4eJumM8vQGkg61fVhEvHdE2RdIequk70j6bXl6W0nvtv22iPh8xTHvb3uVcuxfrm0Ql/3fQ1m37u+sae1olnSa7YdExJm1+2vsd0kHlaSxOqgmKVvKT3qOrPAN25si4tr6ds58pVfsXuXnT5LOl/Ra2y+PiJ1GFP1nuVC9UNJTynO3attfRNy27Pftkn4v6QvKysfzlBXUNqfavl9EXFjx3n7/iIgbe5+V7ZU14uba59YRcUbf51zb2JLqK86DnG37O5LuKulNpSeytkf8CRHx0MbjA2yfFhFvt/3mtsIRcUXf7zyypzYiPidJtl8kaduI+Gd5vL/ywjnK75SVvKdqfoXuekm1oXZ3l/ToiLip7He/st/tJA08ZyLi5MptDzNpWFWvfG9ZsC+Uf5+nXPt6mM4V4t7faQG8VtJtJN1s+2+aa8wPrZjaPk75t/6mpHdJ+qOyQnwP5Q31m7Y/HBFH9RU9VdJVku4gqRmhcb2kC8Y45r9HxN9ty/aqEfEzV07liIjn215TWYk4xHZoLtrl+pbi91BWXl8s6eNlJOOzEfHzYQUWqLNHEfEW2+8r38MHK0dCvlpZ9r9tv0XS45SNn0/YPlzSQRHxy5bid4iIw22/qWzrJtu1oz1dKsQvl7SXshF7tuYattdJ+mTNTkvj+CWS7qM8L1X2/+KhhdKJkr6n7HC4qDdqaft2yvP6vba/HhHzQmgnvGb29KYENEeUQlJN5VAR8T1J3yuN1Z0lfdf2FcqGwRd7x9TnSEmfl/Qf/RVZ2w+WtIvtuw0aaVqAeoCU15DfKUfjukwp6tTgKu8d677Y53YR8Y7G43faflpl2acpR5XHamype8dHp3O6z43OUdooZTdRfWPxXOX94Ii+4/5aW8FSt32S8pq1WHnP+JKyM/VY5fV4mEkGUCYdBOlyzXyJpPv0f09tf1jZsT+0YatsQD94wHd4HeUgUGvDNiK2cUY8vFjSWc4BnEMi4rttZZWdSfsrP7dxvkdSnoMvt/1r5flRNZhQTNJBNWnn1qTnyArfsL3I9nMlLSon1h7KCmCrctI/RdL3Jb07Ino9Pe+zfUlL8V0lvULSuyLiV7bvKmmcOS+P72tw7Wf7dEnvbym3jaQX2f6V8uI4zol8cmnMrW57O0mvknR05fH+qVyQexfnZykr2VUmbDy9RNIDJV0WETc4Q5l2rSz7L9vPVlZCJOlZzcNqKXtF6ZmK0hu3h6TaEaq7KCsbvdDLNdQ+YnK+cy7y4yZoeK2vbGj1Rvxuowzpudn2yJtp+f68R9lIbFZo79Zy3L/ueKzzytveOiK2bry0tzOCYmD400JUiG2vK+mNWvp3rq0Qd6lQ7hIRf+p77i+Szik/H7J9hwH7+rXtKyX9dcLv05XOfADfUFbg/6ysIFeJiOucI7arKxtRT5f0etv7RsTHR5QLSd8t+9xWeb18le3zJe0dET8eUGyizh7bz2g8PEM5jeEM5Xf6GTUVxN6x2/69shFyk6R1JB1p+7sR8YYRRf9arle96+ZWmvtuthm7QhwRH1OOqL9m1N+ixRck/UxZcXm7srFVc9177KAGYGT4+VclfdX2qM7fsa+ZjX1sW/O+Ucrf6fmSdlE2KL6kvN++UAMieyJiuxHHc7bqRpu71gOWjLC7ezhg1wbXJPdFSTrR9k6SDi+Pn6WMoKhxmXIAYdyGbdeOj0nPaSlHA78taUPnlKytJb2o5qAl3U4ZjdM8zpBUc936hbJh/oGIaNaJj6xohEwygDLpIEiXa+a/lNeK/rrIemofBLEG1wX/pbmOwVaREZD/rbxf7SvpQaWj6M0t95mbImK/2v302b5jOUmTdVBN2Lk16TkiRcQK+6MMu3qXpDPLzzslrVZRzspKzq2HvL7WlI/7VGWFYZGyx/V5kk6tKLfxoJ/Kfa4k6WXKHqIjy/9dWfZuyp7LG5ThGqdIWlxR7pTy7/XKUYPez/WSrqvct5UVjv8pjzeStOUYx320cjT+6vL/uysr5du0lL2DsnLzB+Wo2hcl3b5yv7sqL7KfLT+/kvTCyrLfVs4Z73JevaTs65Cy38skvVTZwP1A299K0mOUo38bK8P83zbGvrcq38G/SLpReaGr+huX8uc1/ybKysh5FeUuUY4C9B6vI+mSyn1+p3xmP5X0SGWo6fvGOObeufmW8njDMc7N20haqfz/HsrG260qyh2lBbo+ld/5qbXnm7Ij8OvlHHm9pDuW528t6dctZW+vDIM+S1mRfYay43ULSb9qKdv6uQwpd8iIn4Mrt7GHspFyvDIa4Fbl+ZUk/bKl7OaSfqSsmP1IGVZ3/8r9bifpZOV160vK0NNHjfG7P1wZuv6C3k9luXPLvxf0PntJ3x9jv5tIWrX8/1Hl81u7otwk18w7KUMVjyuPN5P0kjGO+WvK8NY3SVqv77WzRpSzpIeWc/np5f9V99RSvlM9oJR9WDnm35THD1CGP9fuu9P5pQnui6X89cpGw03l51/ludY6gbIxealypGff3k/tvrv+lPNrc0kPUoZPjlv+9srR0ycrRySnerxln2tMUPYnynv4Jcpr/YW960FF2ePKNeCc8vhZve9lZfmxr5nK+bGXln0fUH6+XZ57QkvZF0r6pTI09s3lZ//y3Isqj/n+ylHMnysjYzYvz99F7ffFfZSDTOspOzJup0Z9pnL/d1TWizdSzpmvKXOk8h5xjnIqxeuUc7CnWnYhzpGIWLGTR03CE0y8tr218oTcWFk5642cjhzdapRfLOljmkug8CNJe0XE5RVlH6AMJ5GkH0bE+ZX7nJf4oISqrBoRo8I9B21jpWgPO1wwznDafylDbO9dQkS+ExEPWVbH0IXtOysrOpJ0ekT8vrLcp5UX905zp2yvp5xLbUlnRETVaFzv+2D7woi4X3nuhxHxH21ly3vPUs5HOkLZWHmBpLtHxH+NLDhX/sHKhuVayu/EtZJeHC3hYrZ3VX4XTyxPPVLSPlEx6t34nS+IEvVg++SIeGTlMXc+N22frfweryPpNGWD74aIeF5LucOVnQjf1fzzY2SSHdtrRo623m7Q61GR2Mn25yV9JgYka7H9mIg4YUTZnytHAw+JiCv7XntjRLxvRNnO11tPmDzG9tuUjeClIhNs3zta5hk7p3zcsxzzJTE4rHVY2dsr/9ZWzonuH+kfVu4LyorDeWrMRWw7R0rZMyJiS9s/UFa4fq+8jtTe285Tx8SKE1wzj1OZlxeZaHBlZQP9fpXlHx0R3695b6PM4yR9Sjky1pubt4Gy4/RVEdEaNTJhPeB0ZYXwqCiJ42xfFBH3HeN36HR+LS+2Xzjo+bZrvbsnJHqQssGzlub/ja9R/o1b53CXa9d5EfFX289X3ts/Nuh6MqBs12kBk5bdeNDzlcd8N2XD8uGS/qzsoHp+zTnd2MbY10znfOgtlVFrVs4ZPzMqEn2V+/bj+8oeH5UJA8u18kBJR0bE3/pe2yUivjC4pFSiL/vV3tueqgwxv4uyk2ljST+NiNbVXkpU2MeU04Os7OTfMyqSuE1StpSf/BxZkRu2tr8racco8fHlBD0sIh5fUfaTyjleXSZe/0wZCtefnXicJC5jcy7r8TLNhaI8XZnAoDXszJnQ4rER8ZfyeA1lJbx1yRLb75b0/r7P+f9FxH+Pefx31PyLbGuWZJcssG5kfnXJlFxRdl3l57VY8yfm11zcP6f8sl5THq8j6UOVZXvzpe4WOZ93I0l3jrlw91Fl3zro+ahM7uIxs0Y2yv1I2dA6Uhme/1vlXMSq+Ze2z4qILfoaiafWnF9921lTed2qDdmcpEJ8WkRs5cwwuq8y9PXIiNiksvwk52av7GskrR4R73dFduMJKnffiognlxtpaH6YVXWnXFe2N432RCvDyk50vbV9YnQIVy2VpQvGaSz0le/Nc1us+d/H2k6qrt/ln0raLDrc/J1Zs78q6X7KkdM1lNEy+1eW753Xr1d2pH688rye5Jp5ZkQ8pO97eF60ZJ7t28bDtfTfaej8uvIZb99fGXNOSzo2Iu5du+8ubJ8eEQ8d99pje2R2/bbG2iT3xcY2nqEM8w5l5/w3xii7unJUqm26WLNMp46P0knz8og4ve/5rSR9uvI6f4FyNP3+yvmaB0t6RlR0njrn1v5MGXmxZFpAROw5zbKlfKcBlEb5ToMgtndXZqG/pjxeR9LOEfGpMbfTOQv/rHBO43m0pO9FxIOcU3x2jojdlvOhVel6jkgr/hzbO0Rj0ndE/Nn1mX4nmXh9bUQcN/bRFu6eFewlkh4aZU6NM0vgjyXVzKdardeolaSI+Isz5X+N7SNiSbKl8jk/UVJVw3ZYz5KyN7HNP0sFMcq21lV98qhvSvqhMox63In59x9wbo2snDV8SmUkT3lTuV5ZWWwdyYu5uVO3zYdzf7M2nssaebHmPqPQiKyRDXspQ0r3kPQO5fdjYCNqiBucc67Os/1+5Rzs6rUOJ+hVt7LncEmF2PaWNRViZeKStZTLXnxcmZSode5mwyTnpp3ZkZ+n/F5LFdfriPhc+Zx7yT+qerQj4snl37tWHt9SSoX0fcrQJ0vtybIaDioNtTOV5+MPo34O10TXW+WcsbGTx0TEv2yfb3ujmk64AY6W9HdlKN9YS8BM+F2+SJnNuToPQk/kagEq++nS2dEpsaImuGZqsrnMQ0e4NTpxzMrKkZ1+v1Xd7ztJPUDqPtd11NKAofZ5p5PcF2X7U8pR7UPLU6+wvV1E7D6iWK/skuUdJd3V9cs7dk3idpv+Rm0pf5rr1/G9KSLCmZl534g4aFjn5AB3j4gdbe9QrvtfVkZBTLWslx5A+aLt2gGUtZXRWoslrewyjzIql9CT9LKIWJLorpxfL1NeH4bt879735lSb/iGpFuVusFzBv0NG2U3lPQB5WjtccopW718Hd+IiKe1HbA75icpZW8l6ZWSevOeT1J2mtRE9vwzIv7X9kq2V4qIE8t9o5U7LA3ZKPt+5bTPvylDvh+gjDSpyjO0AOfICt+w/Vez0uEMoajtpZ5k4vWJtj+g/OIvSWTQVlFq6JoVzJrfQLtZqp7g/lfbm/eO0Rn6+beWMj2LnBlU/1HKri5p1cqyUjaUtlJfz1Jl2X2V8/ruaPtdyvCr2pHiW0fEG8c4zqaVbK8TJRzFGcJZ+316aBm1OFdacnFepaag7fsqQzZvVx7/STk/7uKK4k9Tt6yRirnIhb+oPjlX0y7KuWKvVjYON9Tckg41PqtuS6NM0onQW9riWmVDflyTnJt7Kuf0fT0iLnaG55zYUka2HyXpc8o5cVYmJXlh20jepCM1xfuVa2eOkyymt/1HlO/AQ5RzL4+xvUZEDAyN7jPp9XaSrLmTLFW2QWVH6SBPU8fvsnIe5E/KMTc/r9Zj9uTRObuqW2LFztdMZXbyoyRt4ow8WVfzEwW22ULjj3AfrFwG6jBJV5TnNlROx6hZzkmaLDvoK5ThgOsrG9jf0Vxm+aFi8kRbk9wXpZwqct/eZ+0cAa7t4NpHSy/vWNNR17Xj4zjbxyg7OJp/4xcoK/Q1ri8N6udLekTpCK3q+JDUa9xcU+oFv1c2BqZddpIBlGOVU2vG7swrVrLnljgqn1fbdeAZmvvOfEAZUXCc7S0lfVRz1/9BDlbWF05T/t4n235KZDTQwJDsAQ5RJgn7iLIesavq6+X7Kc+HXsN9l/LcSyvKXuOMvPyBpC/Z/qOmuzRkz+Mi4g3O5U6vVOaeOFH1CXQnPUdW+Ibtf0k6xfbJ5fEjJNUOw78zInZpPlF6bncZ8v6mXtjjFo3naitKUvesYIco14P8enn8NNXfRPeSdITt3pzL9ZQjAjW+KOkE24cof88XKyvXtTr3LEXEl5zzER+jvFg8bYyK9bdsPzEijh3jWHs+pBzp6WVU3lGZqKzGJCN5B0h6bZQ1EEtD5kCNvjj3dM0aOVFYvzRv/s3f1G15p6696mNXiG2/ITL0d+BauLU9h5Ocm6Uh+oPG48uUoy5tPqS8sVwiLRn1OVS5jE1bOSlvaFsolzazMkTudGVoYJs/dGnUluPcRhna9h+S1laul/nDyuITXW8nrMx3OZd7jrP9uKiYbzlA5++ysgHQ1UTRORHxEzXO44j4lUYvt9HT+ZoZEefYfqQ6zmVWhxHuiHiP7W9I2kGZyKk3N+955TOo0Tk7aOR82JHz8Ufx/IzhPddKujAi/jii6CT3RSkTEm2kuQy2G6p+ubJByzvWdEZ06viIiD2cS47toPnzLz85Rp3iOcpw4JdExO+dIfYfqCx7QLkPv6Uc/xrl/9MuO8kAymoR8drK9w5yvKTDnasbhLIDp7YTQcqIr+MkqXy3Vm95/7oxN83iNc550D9wRhnWdnStHhEnlAb5ryXtY/uHysZum4fE/JD27ztDjGvsoKxv/afyWrCWhqwiMcDYS0M29Dpmnqhc6u//PN6Sq5OeIyt2wzYivl1GInoJEP4z6hMgzAuDLTfVqmRSC9Dr2Wn5nIj4sO2TlJVQS9o1Is6t2WFEnGn7Xpq7+f+s9uZfGgEXaq4C/46IqA2JkeZ6ln6oMXuWSu/qxb3wFNu3tf3QUeElDXtKerNzqZt/aozQyYj4fGm0bFvKPWOMCsugkbzam8pteo3achwnuT7s6QZlKPAJmj9SU9NgmiSsX+X86L8RXKtMivTOaJ8P2bVXvUuFuNc4O2vku+r8Qpnpe+Wy/5Fhq7YPkPTxGBCGW/7Oz1GuOf2lIZu4VTTml0XEz92+5MSSa1YZYdqtt//Sm/+6tvLFWc71Z7+h+edXzfITJys/7/co5yDeWLnPia+3znDzt2ou3OtkZQhj6/kVky2tdJqkrzvn6o51/dEE3+WIONkZvbRpRHzPOeVkUeUxd4rOsX20SjbS/vuKMxrhRZIuj4iDh2xikmumlCN5i5Xfw81tj5wj26fTCHfp5OnU0VN0XkbPE+SPKF6ibJD37jWPUp6v97D99hiS8GbC+6KUGYJ/Wj5rKSM4fmz7qLL9UZ95p+UdJ+n4KI2kztMgIvM9fLjx+DeqWBu1vLc3LeBkjTktYJKymmwA5QvO0OFvaf53qXa+6xuV63G/UlqSmOgzI0tIdyvnjyVtYPvWMZcUte3eeCvbq0XE38txftG5tNvxqp9K9fdyjf+F7VcrpyPU1p1utr1JlDXRy7WyatpczC3z9S9nZMH/jhF10nlpSElHO/Ne/E25ZN+6yik3tSY9R1bs5FGSxk6wUUaE3qxc8qV38luZ3vyAiHhTxT47V5RK+UFZwZ4XQ7LOeYKMpi4ZH4f00NZWSidSKux/l5YsQr+WMkFATQa2c5Xp03s3/5WU8wBGhlYuhNJgupPmn1tVc+1KJ0KvI+CE2pGucjM5RxmOLGUI0xZRN9ejU2KhUvZsSU+P+WH9X6/9nJ3zLm6W9OXy1E7K3/1a5TI+TxlWtpTfXBnqdF/lCMq6kp4VESN7820/T9kY3FwZRfAsSf8dEUeMKrcQnImf3qpc+qLXqx0xIvzUOS/szcrkPBcpl9tYTdKmyjm+B0vaP4aEoNo+WFkR7p0fz5O0ckRUhY97QFKdQc8NKXvIgKejpjLtnFeztfKa+RBl58OPI6K18eKO868b5b+q/Kx734NdJD0gIgZeE/vKbqU8L++tDIlbpFxLuLVxavsyZaXwwjEqHL2yk3yXX6aMXLpdRGxSGgH7R8RjKsq+QbkEVDM656iIGLm2qjOB22uVIW3/p7nzerFy6YxPRPvcra7XzM5ZoEv5gYl8RnVq2H5CRHy7/H8t5Ujmlsrz7D8j4g8V+x2rHtBX9lRlR3F/QrWvtpUt5Y+W9NLecZbvWC8E8gfRkjDNHRJBlnIjkya1fOa3VkbpPa48dbyy03RgpXpYnaexr5F1n3L/f6kyE/Jx0VgP1o15nS3buF5zHb6rKBtaf4mItVrKPVLSnyPD05+tvG5eKmm/YfeGvvK/VHZU/FD59xyn86F3P+4NoPygdgDFmfzpXcrM0b3fO2KKyQkHnFNnR+aQuZOyDvHJQeVK2f9ULjtzct/zD1JOyRi6XnXjvQ9RdnCtrZx2t6Zyru5pFWUfo7zWXqb8rDdWDlgNnZZU7knvVV5n36GsC9xBuWTYC3rXpZb9vkQZhXNS2e8jlPfYQ5WrSry+pfw6yuW5bi7fyzWjPmnnxOfICt2w9ZAEG209raXse2oasUPKdq4o9W2nKiuYl85ouuQltZwQtt8WEW/tUim1fUpEbNN3cW7ut2bkobetOytv/KFMw177JRhUEV+SeXdImXtFxM88ZF5h1KXpH7vR0ij7hRgQ5t7/3JCy6yjDH5fcVJQXmj+3lZ2E7ScoK1nzwvqjcmTe9o8iYutBz7mxhFDLNsZK8186ObZSXuC7VIgnCr+2fakyFHrsbOjOCIYtlFMC/qbMWNma6dP2qsq5dM3z41M1lZ1S/lDlXNEvKr+Lz1eueVg7570z2/dWzrH7D2Vl/jdRlx100uVcJmnMD1rGatNohOuOKHu8MrS30zwid0gSVsqdp7zWnh5zGXOrvoPlvdtr7vv0ndprQKP8Ys2d1z+PiiXlJrxmds4C3ZVL9ufy/88o5zAeqJzv98jKjshFpWI4dnbQ2vN3RPl554NtKztg7usRWaw9wRIjjW3cSXM5EM6I0aHPvTKLlEuwPHaM/fTqPHdUXm96SzptK+mktvpa+bveWtIZyjreyVFCKJt//3HYfppyrfOh1w/nih33V3YcXKIMI/52+R0WRcuScGUbqyqncPyHskPxXpLOj4injyizEEvC/VJ5T+y0dJQnXEqzbGPsxJuTsH23yKlEXcuvqvmRlCPv5eWe9GblANEBynvMaaVj8NBh390B27mL8rz+mXLE9sqoy7r/gkHPR2WEzKTniLSChyJrsgQb37J9m+iwvpikTSKimRjnbaUyUaW/N025IPZQMUFG04joxfm/NCrW9Ooru03597bj7rfJuYTE/yhvLJb0cWe407CwtKbLbO+h7E2Wcm3FtovIa5UjFoMyQNbOzdtTeW51WcJpkjD3P0vao4wC/KumsmP78Ih4tgeHA6umMR6ThfVL0hpuhIg7EzesUV5rDTv3XJr/i8vjdWyPTPMfmbX2QxHxMOXFeVzrxgTh18qEIlVRGv3KTfekDuX+4czwe4KyM++SGCOsV5nY4pXK81vK689+w9+eo3gx4Zzkcs27RNIpkvZX9krXHnfX+dc9f7O9TUScUo5la9UnzlNEXNprhEg6xDlaVuMqSSeVhnkz5Kp1uR93TBJW/CMibnSZ91Q6AqobfTF5+OXlyuMeR+drpibIAl32NUm2bykjah5Y/v8R12e9vdQ5V/Xg2s64hknyR0jSD21/S/OTx/ygNLKvGVFukkSQco4+fkBzI0Uft/36iDhyVLnSAXCD7bWiMjIuShRL+T03i4iryuP1JA0dxWvYMuaWrvuEpE/Z/pry9x1rUmHjmL5he++Wt20bEZs516L9raQ7lt//06qfj3yzcvrDzcr7xB+UHRGjfFnSk5VRAEsNZKgupPlizUVCdnGQBiztVsPzE2/a9tWqTLzpyUL7P+sxM/57eCTlJs5pFKOiCVaOkreh1KNPK8f6M1fOdS118j2V0QjnKb/TP1Zd3biZmHM1ZSfoOaoMsdfk58gK37CdJMHGfpIe4Fyv6w3KL9TnlSMKbSaqKCnTgvd60z5YelpG9qaV/ZwQfeFkg54b4le2v63MNPv9cXq3bX9MOZL149oyfV4v6UG9RqJzLuWpyrDLNq9Qzr/6b+XF9QS1JAiLso5XTDY3b+xGixth7rav09zN70Zlz1rNNh6i/FxuWx5fK+nFEXH2iGK9RsqTxznesv3+0e1ecrGNnPNFazPPvlTSwc6RSCmzE7+0VJTeU1F+7DT/xXdsP1PS1zqM2Nzs7lnVpbz+nOSc31LVcBnW+dAoO7ITwvaTlA3DXyrPr7vafnlULocTEX93JuY4NurXglyIOcmbRseRS024nIuyIf+50llk5Qh/beNjkmWsflV+VlF7Zs9+XZOESZnZs3cd2k7ZGXj0qAKeMDpnQLl5hpVfiGumJsgCXXTJ9n1H269VHu+a9lwmV2VIYI37q2RRdkafHKy8z15XUbZz/ohid2VjdutS9vOSvlp+h1H3zc6JIIv/UibM+aO0pEHxPeX66W3+LulCZ6RNM0N5W8fa4l6jtviD5iIhRlnynY1MsrOb7V4H/RpDSzX0NVxWUkZ9tN1jevM9/277170BiYgI27VJ0a5TJgH6sKQDazroJxlAabhZea08UePn+ZAmW9ptksSb31THpSGjW8b/RyrPo0FTtEJzSy0N0ryP9rc7ausveyqP97SI2La0QaoSJUbEa5qPy3114Jz8ISY9R1b4hu0kyXKa64t9LMZbX2xQRelFYxz3WL1ppefu1pLu4AyX7N3811SGBNW4p/JLtLvyRvot5U30lIqy50h6S6lcfV3SVyJinErulcqGTs/1mkufP1K5Ae40xr6WcGaXO0zS4VEm549h7EZLRLxH0ns8QZi7soPlVRHxQ0lyZpM9RFkJGrbfq8oIx0ExRqhWsRCj24pcLuh+ve9EcyRU0uEVm+iS5l/K47+NpJts9+Zx11bwJsmqLkm/KT/NhkvbjaXX+dBbmqM5V7amF/NDyh79SyXJmXzmGFWOsDlDCT+gMdaCjIijy7+t8ztHuItzxHdr5Wd0inJZhkFrgfbrlNXU9kcl/UjSqZEhzGtKUmXDoWcXZYV07GWsYoI1qdUxSVixtzI50IXKRCzHqiUBS0wYndMrZ/vtyrDcL0hLcioM3eYCXTP36Viup0u27wM193t9Ttm4vto55ea8mg1ERuMcKOlA249Qdlx8pIzivqP3HR9SdqIoqnKdPVJ1Dcqma9x9iREpQ66bdZ3/VX1HwDHlZ1wnOacFHKq89uykiqXVlMnylsyllqTItdJ/p5Yol4Zmw+UmZSTDDi1lmp0mvf+rPF63cr87K6ervErZwXyqcq7sCW0FJxxA+Ub56WqSpd0mSbzZeWlId8j4H3ORlG+PzBzf3F5bx8IDGp2AvQ5BlcerDS82z99Lx4mcyQJ/ZvuelWX73aDMEVLrG5rsHFnh59hOkmDjZOW8hRcrT8irJZ0XlfOQyja6VJRk+wbN9aZ9r603zblg9l7KRuxvNdewvU7ZG/eJMfe/jnINvOdFRG22TDnnXjxTeWPYKCKqTmbbn1cmy/mm8sayg3Leys+lwY1FL0z448bKOdjPUXYgfEXZyG1NdGH7rYOe71VWK8qvo/yyNxNs1MxfGDpXtaLsUZJ2icpQrYXkyRP8fFA5r6aZ5v+KiPh/0zrmst87aC78+scxRvi17R2jL0nVoOeGlO30d7b9g4h4ROOxlXO/HjGiWLP82crOipNibv7lyDnrjbLrKrNW9i9E39r5UUZZvqz5SdGeFxXJOUr5seZflzKvVvbW93rsT1Vp6CojZEaOIJfOlc9FxPNrjnFA+XlrUkuqXpPaEyYJ68oTzHUt7z09Ih7a9tyQsl2vme/rr5QOem5E+Y8pQ5m/ofGzfXdWzq8nKacHLFb+rb+krI+8OyKGjipO2PjoHH7tCRJBlvIfUHbSHlqeeo6kC7o2KmqV3/c/ysMfRMTXR71/eRpW9+iprYOUbd1L0vbK+uMdI2Lo8jeNAZQTlSOPzQGU4yLi3rX77aqM4vWLUfcY28+IiK95ssSb71R2gI4d2u+cFtMp478HzNW2fXZE1E7D6KR8Vrsqz4tHK5PX3SoinlhR9mjN1ckXKRMrHh4RbSH2zW10yh+xpPyK3LCdROlZfa4ykdEPneuLPSpGTIC2/fzIdOAD12AaNZrXt50dlL1pWypDrqp602y/JiI+XrOPIeUfqbyRbK+cD/CVqMyiWMpvWco/TdJPoiXTbaPc2Bdq5yLZR0/SedG3vU2Vy0eM25i/TcylVa8tM3D+QmUD4CPKm0uvd/k5yovOV6XRPZe2Dy/7GjdUq1f+4Vp6jkltQoBJE/xYObr0WGkuzX8MmRfuBUgQVrYzVlb1vrKDbkpVSUWcc/JfHXPTGR6uTAL1wJZy+5XjPVx5fuyonLv6o3LsbZk+T4+Ih7qRIGaMhu13lJ1Dr1N2PLxQ0tU1lVJPlsCp1wBYrPl/p6rrbdnGesrR4ocrO9bWbavEl3LHK8NUx5nH3Ct7qvL70AyNe3dEtIbGuUOSME8Y5l62Me/8Ld/jCyJis7ay5f2nKucvHlaOZWdJu7f9zhNeMwd9D6vO6fLesRMrDtnOt6KEc1a+/zJlI+KgaGTcLa/tO+i6vVCND2fiu3HDrxdEaWQ2M+6ObGQuxHndlTPj66YRcX7juY0k3RwRv60ov4Eyq3qXSJW2bb8pMuJh0GtflfRAZSblH5af02NIBulSpvMAikfn+YiYv1brgup9/z1B4k3nVIrbKDu2xgrtd4eM/6XD4T7KaRDNDMRrSnp9jJGMbVKlXbCWcqm21vuc52ehvknSr8c5nz0gf4SkF9bWu6QVPBS5NFbeo6VHEFonuUculv1VzQ2h/0kZZjtKL6xhUBjQOMk5vinpm329aW9QLkE0yr9srx3zs7iOTLDT48yofJ6yQvz6cRprzjk0z1DO6/uKMkzqmtryMReSV91IjBL+qKxUnVu7r37ODJ3PVjYQb1Z+zjXlHqYMC15DOd/0AZJeHhGvqijeef6C8mYkLb2498PVHhrcNVRLHrJkhuoTAnRO8OOcX3ZB5BIT+7e9v5g4hNpDsqorb4ijym2vXJx8fdv7Nl5aU/VheS9Rzkleqzy+Rhk90mY15dSF3s3lauWI4FPUPjdH6rgWZHH7yCkbe0Yuj3Cy58K42/zJmaSvN1KzszIMscbRKvPr1L5G8Tylw+R+yu/P1sp7xS9Uf15fLulHzmiIZmdRTaO6c2hcacB+WI01MCt0DnP30nNdJc0tgzfGMTxXGQ30MeX5+KPyXJuxr5m2X6kMtdzEdjOpzm3LfqvEwo2Crz/m++8fQ8LTR3RGvlxzjY+zNb/xUZMQqWes8GvPzaHu7a9X3xl3bm+v823gdcr2jyOTATZNNH3DkyUH+6ekr9m+f6Pu8hnld6W1Yavs7P2ysgNSylHEQyRVRaq02FHD81e8V7mMTfV80Yj4mKSPdRxA6eX5+KnmN9SsbLxVc+aRuI/m1+nf3lauNGCr52n2le0c2h8R15ROqg2VHXMPV/v6ufdUntdra364+vXKJFbLTIy5VnvkOunNzOa/GHOXk+SPkLSCj9jaPkXZAPiI8uTYVfk7jxwhLGUnWetv64j4UdtzI8o3e9NOUVakR/amlXKDRj2WjLyMKLdIOXLQenEYUNbKxE37RfcU7ksaiRExViPRGZqynjJ742FREcbXKHu68gJzhHJ0ujoleyn7LOUajr2RrYuiZX2/8r4zI+IhZVTuoZGZbKtGpyZle3VlmHhtYqBeuYmWzLB9kjJM/bul93QrSe+LiuVcSvkvSXpTVK6H2Ci3Wv/3ZtBzQ8peoqxcjpV8rpy/D5T0dmW2757rJZ1Y00Pc2NaaymvWMgkf9/y1IK1cC/IdlZ/XaRGxVRnF3FeZaOzIiNikouxGkj4h6WHKSvGpypGL1iz044y+9ZX7rrKz4TxlFvrTxh2h8gRTEtwhNG6BRl0nmc4wyVzXzrpcM0un0DrKyn0zDO76qFiapLGdQzR4usu4I7YHj1OmMZq3jbLDpmo0r9zP3xwR7xjn+Pq2sVzCr9uMqs90Pa8nHZ12TpP5SUQcXK5j32yrczXKdo5Uqdj2yLqfcypE/6BPbQRWp7KePHpif2VEwrbKDoRnKZeEesmIMjco69IDjbHvrlMhmhn/eyPjVRE+th8W3ZOyLhdeOrP5fygHy6rm6w86H8a9x6/QI7aSVo+IE2y7VJD2sf1DLT3aNcjuKmv9SVJE/ML1S318XLk8UNtzw4zdm1Z0SrATmSp+W2VFfCwREbafNslNVNJHJT1emQBGEXG+M1lGzf63dYaNP1vSAaUh8JWoWBxdGd7QZRmY3r6v8Pz06bV/ryud4SnfkPRd23/WXLbhkZzZX9+qrOz0QpfeHhVzmGw/RdIHNUZioIaJlsxQxwQ/DetJutiZ2bQ5MtZ27Kdq6e/doOcG6ZRVvZy/Fyl7HccNiR84naF3nrWNBNq+m3I0bCvl+fFjSXtFXwKKEcd+g7Jh+1/jHHfxztKY+H/K692aytGjmv3+RlJtltp+x9l+XJQlDsZwmaQHKCsr/6scNb56nA66mIs2WTMf1q81qhyBf5tydKoXGtc2Ojh2ZvMBbuP5WfsfrspMzhHxpq4VvLKvrstmjH3NjIhry0ji/Wo6SEb4VuP/q0l6etu+hxzPWA1hdRzNK/fzJyqX3ulqTeVo5+Oam9bwkdTVlNMP7q5ccubgyEzBC21Ux2rX87pLcrCmzyiTfB2sXMd6UOj6MJNEqrQZ+lmVDrlHKRunxyojA09RRaRKl7Kei564myeInpD08Ii4f2novM32h9QehfQrDc4uXM2TLX8zL+O/7a1tPzcidh9VqDjXudRh/wj1uNeSZWmSzOZSJmU7SPMjL0at+rGUFb1h+3dnGOMvnIlCfqsMN6kx9lp/ZeTx4ZLW7auYrqmcRF3rPEm7Nxp3JytHi9smUB8v6fDSq9VLsPPt0UWWONW5FttXNL/hUDMX8TTbD4nMfNvJBI1ERcTvJe3rHL19g3KUrKZhe5XtDyvnPkj5Ob+9cnTsinLjDOdE9z00t+xJ2/E+vfx3n3LMa6n+73SYsgLcy7z6POXfrCbb8T7KzpqTynGc5/YMez0TLZkREec4514sSfCjbMhcMLLgnOqEGJJUOjvWV4ZOPkiaN9fs1pWb6ZxVvVQub297ldre2WLS6QxfVoYc9s6xnZTnzMjkPM5Q2qFq/s4R0WsAXKvsUZdzqbNWEzR4pBxt/Xq51lfPf4qIl5d9r6msqDxced1dV9JFETFw/n7fcW+hrMiOs/xWb/9jh8YNa6CVz/m5mgvHHKVrmPukFTyp47IZXa+ZkWtZn+/Gsl3jir48E7YPVR7/UOWzfZMy30QvU+0flb//e6Nuqs66EdFsJH3W9l51Rz3RMmddwq8/p/zu/VA5DeM+mgs/XVa6ntdn2f6KOo5OR1kf1Bky2cs2XOvFykiVj2guUmWhGi0e8dqzlJ1650bErs7Q0ZGZ0Scs+2VlZv6Joic0t3zNDbbvouwEaKvD3Dhhx5Y02fI3/yqDCDsrpzb9Su2N8Z4vSPqZcuDn7cr63jKf9z6mSTKbS7mqzO7Ke+OS/BHjHMCK3rDdS1mJ3UPZe/lo1a9PeLLHXOtPORK2hvJzbVZMr9N4o1P7KUeKen/MXcpzL20p90blHJtXSnMJdir32Uve0Ry1rZqLqKzEvsL25cpGca9iWRs60LmRaPveyovFs5RfoMOUI0Y1DlaORD67PN5FWUl9xtASc16hHBlbX7lc0XfUUql0Zo3u11uoew3lslBtbtc3Ov5O20+rKCflElbX9nUg1FZ69ql831ClB39JqLgzEVZVcrJYet7GGX0Xz36PVy6xtYHmz0O8Tjn/qcZR5aerX2v8+ZfHlPcMTJhWsU9HRHPNuC+WTr02D1MusXWoMkplVKWo/7gWKb9D6ysTTFxk+8kqczIl1YTldWrwFB9SHv+FXSrxysrsDcpK0z+U50zturIHa8zltzw/a+RSajuLSmXpucrPvrqyVBrdveWNxg1znyQ/gDTmshkLdM3sGu0xzKaSNmp5z+HKdSgfVTpee51tL1ROfamZQznJaN5r1WGZM3dfbWCzKIkAy2jLGZXHOa6h16UJzuuxRqeHOEhZ17ogxphqMmGkSptR2ff/VhpcN5XP64+SWnPPdC1b/hbXKs/hSXyrRG58QDmNI9Rexx1nRHiYsZe/KR0dO2nue/sV5Xm57Rj7vXtE7Gh7h4j4nO0vKwewbsm+7bnls6Ssn4+TTXpl5RKrH5aW1C9WHecAVug5tpNw1v5fqvlzzT5TU3GyvfEkPUS2z4++LHGDnrulcC6bs5Taz8C5pMrHND/j7R41PXm2T1N+gY6IiLHCwzzF+S1D9vcrzU+wocbjiIqkZs75PGdpbu3XZ0m6T9TNGz9I0gnKHtNnKjsQbhURrxjn91gotq+IiA0r39tp3obtZ/aPuCwr7jD/0jmv9/ERcXnf87tK+u9oma9q+73KkYpe1tnnKG8Knyz7HvidKjeP7ZQ34fsrG9iHRt3SM59VJsY4Qzky/GtlQ3PviPhGW/myjc7fu3IT3T5alucZUO4jyg69TZWjj6f2fipH1DrN6/Nc1kgrQxjndVjGiGQdQypLr4uIgdfgIdv4n0HPR0WOBU+YH8BjLpuxQNfMgfP4R33OfeWbiZFCuQ7vm0ZdV2xfEhEDK76jXut7X+d5512542oDXjpbdlX29w7Hd9+IuKjvuYGrUPS0dCQuCGdugqskPTMiRo7ml/cP7DjoqYkKmiTKxfanlB2POykHAv6iXM6ydaR+krKTKo3Kf/T+rwzP/Xu05MEoHXA7KDtfQzmV4KioDEF3h+VvbP9L2Vn7kphbV/6ymmtWYxtnRMSWtn+gHFz7vbJTv3oby0OJFtlaqsts3lf2NEmPjZI4z7ku9neiYrWAJdtYkRu2pRLwei29XMfIUUjPz8LaZb/rKkNi++Piq8K1bJ8jaceI+GV5fDdlEpaRNwpPkAXak68zuo1yLsEh5fdfIyrn9Q3Y1jrKUZB3tbxvkaTPR8TzOu7nx8rGUW9OztaSPhhLZ11slpl4/dxJeC7tfK8Sv5LmRiFG9sh7fmIgKTtr3hl1iYEmyRw5bJu/iYi2kY/ee8+XtF30zdto6+wpoyTvUofzepLvU1fOuXEfk/TEiPhFee5NypG57aM9ccyo71xtQ2BVZcPpA8rQ/JEZMJ3zie9fevFXU2aQv3tvpKrGuA2evrKfVY4YHKf5oYRt85F7GZ/PjRH5DGzfp7+B77llpHbRgOW3IqJqjrIrkvv1vX8hKkvNiJbVlPN2f1pZIR67gtdXvnf9urH8THwduSVyLn31PeU6x38oz91JGUWyXUTUTB2Z9Bg6z4UesJ1rRnXqOzPc9+5DVkZq3KAx/75d7jPDOhB7RnUklvL3UEbE3Ski7mv7/pKeGnV5Ojrp6zh4m/ryvgzrQOjbxqnKa8HZakS5jNuR61wZYs2IaJ0WVAZ8NoiIK8YtuxAGdZi0daLYfoPy/nmYMrpOyqicnZQJR9875jE8UhXL39h+etnHw5XTJg5TDo7VTv+Sc+rHV5WZ+z+rjFL5n4ioXR1i5izEgNOK3rA9X7k8SP8Xv3X+kztmYS1lO6/nWMo/RhnOdpnywr6xpF2jsTTEkHKTZIHuvM5oubFsIemeEXEP59yHI0aNWpRyGyrXjr2LcimlQ5Wh0C9Qjha1ztGx/W3lTajLOpIPVM4NWkv5Of+fpBdFY026AWUmXj+33ByeJ+muEfGO0jN/54iYVvhW//67rL3bKXOkh2dxtaR7RERViIntC5vnYul8Or/t/JzwvO78fSrlO3Vwle//p5Xz816qDP18cowR4tZFadA+SdmoXawMwz44WtZjXIjRGs9fJ7CXdKaqQjysYttWoR3j2AZVpkZdi2OcTsxxPquFqCwN2OaqytGLx49ZrqqCtxAmuWY6M7B/XNK9lSHmiyT9te3cckYiXRMlpNWZYPFpyiWePtlSqV1HGRmzg+byevxB+Z16X4yIRlqg0bxO6/46R/MPjwy1XFXZWfRA5XfyuVExGjmJrveZCfd5snIA5NMxxgoHtteIIcsxjfmesTq3GuWWyUoKA/Z7dkRUL72yQPvs5cz4orKR2syZsX9E3GtE2Z8ro9r+2ff8KpIujohNB5dcajuLJN1J8wfJWtsHziXcnqa8rz5aWef8eoyf7PAWzXORLUu9pPE6t34k6TVR8vvYfrCkT8SIAaeltrGCN2w7fwFtf19ZoRx7Xk5vv26kqLZ9clQubVLev6rmEu38LCqWHGnsd0kjwPYPI+I/Ksr2QszObVzcqy6czrC0BykzOffKtqbnLpXDk5XJR54g6THKOZj/WTvaY/vTygy3XdaR7G1jzVLmurb3LgTb+ylHXB8dEfculaDvRMRDWorKQ7JF1/TEO+cxf0bdllWqWg5kQLmRIZJRH67+AWWIbHPexgVtnUUTntedv0/lvZ07uJwREN9Qjig+OypG1Uu5Fwx6PlqWYrD9OUn3VVZkD4u+cL+Wss3lFKxc7/hSaey59hPp0mFTud1OFc8R22vOGz1RmWF0SajtqEZPYxsLVlkq158zxqjgraMMPW9W8GqSDA5qoG4oab22BuqE18yzlJ0BRyg7YF+gjC4aOdfeuaTb0yPid6UT9HvKCI77S/pnRLTlvOhkWKdpT2Xn6YWamwv9QJe50BHxnJZyF0u6b0SE7d2U59djJd1DOfq8Ze3v0UWX+4wnjKLqeo9wJhU8T5kf4OzetccZYbetcu77gdE+XaZT2LYniHKZhO1PSvpsTJAstMM+X6iMdthC0pmau15eX45l6Hxo2z9TTu/5dd/zGyuvITXTAl6j7OT+gxpr2o97byvX/h0lPaem89P2uyW9P8rUmHLd+38R8d/j7HdZm+Seafshyg7b3tTC9ZSfV3Vm5BU9edTRtl+lHA1shqjVJJyYpLe/1zN0lXMx6d8pe05HcobhDLKJ7ZosfZNkgf6rcymZKMeylXKyf40by42wV7Zq6QhlIqR9yv+Pt/0HZZrwcZZX+V35WUmDM8kuxUPm5LhySZXy3u8qw8WvKY/XUTYIakY9Hhq5luu5ZX9/Lr2HNZqLm6+mzHJ8tuqSfH1EYy6r1DgnO2WOHKPh+uNRPXIR8fpyLNsob2oHRN28jUnO60m+T5J0+4g4yPaekfP5Ti6jA0N5/ny+VZWdPX8sDYKaXs9mRX+1Uv4ctS/jsIuyY+gekvbwXIKxmv3eu2XbQ3mCUbHGNpasgy1prA6bSktVlj1kWaYlBUZfQ87W/HmjzUZhqCKJS6lEf0nSlxqVpb2V+Qlke50YMsLv+VEUi5RZe6uWhrH9DmUF8zI1Kniqz4r8qVLu0WWff1HO/25roE5yzVREXGp7UWTI+SHOMM42q8dc3obnK6MXPlSuCee1FS6NyfWVjcu/Np5/QkQMzejc33B1t6Wkxk52U9wYsWS04/HKe9rNkn7qjHaZti73md7o7lkd9/kn25to7h7xLFUsaxcRj3FOHXm5pK1LHeAmZcb/Y5XLCVZPx+hgT0lvtn2j5uqcNfeISW0r6eW2f61uyULHVr4Tn3O3nBl7STrB9i+UCRKlTP52d0k1iRWl/KzvGRXLKvZz5kU5OCJ+Utoeny4/NbZvdsCV694TJd2iG7aqT0q6dMGIM8u1szmw17YizDwresO21/PZbAzUVhxO9nhZWJsGref4nxXlellP76gMNTtB+YfdVpk0p61hu5fmZ4HeVvVZoCdZZ/Rw58jp2rZfpkxXf2BNwXIz6FXwfi/p1r2GcU0HRMytIznOaM0HlRWT3pw8j3z3YOtGI8FMueDUNnr+6Qxr6d1I19VcJXGkiJiXGbeMeLy/cr+K8ZdVau5v0syRo6w26kXb/6kMbx93f5Oc13upe1Z1qUMHV0TUds4MbLhExGv63reW5taDG7XfcdLx95edpPPicOXSRNeWUbEjlKNiD1Q2gmpGxT6qjutgT6DzskxRGTbsAXN7h2xvUGXpBA1fq7m5Fu5NynU8a9ccfbakTWo6HIbo2kDtfM1ULg2yinLprvcrGy01na/NC+Wjlcv39JbvGF0w52/vrmx09Tq3vllefrcqliry/KWkbPsaVS4lpe5rpf/D9n2VI1PbKqNNemqXSZvE2BmKI+Lo8m/rSPYQu0s6QNK9bP9WmWG8Km9HGS3tkhegGbZ5a9u9aLHqsM3ae8WIY+iaF2X7SfY7oQ1KR8/1yjrm5sokhUMjVSLi28551FsqO5qsnGt7ZozIrdDnCtV3iPf7maQDS8fQIcppdrXbWuT5CbNW15gZgmfUQzSXFO1BZWCvdY3lnhW6YVtbgRjES2dh/bjt1iysZb9LredYI0pWOdvfUqbPv6o8Xk8lq+mI412kDFl8vbIXfKwMdTFgndHaXpKI+KBzSaTrlCM+/xMR360oupZy9KJZS+iNXlR1QHQcrdlcGZr2pLL/QyWd0OiprnGzG2sjlpGn2vL7KqMI7mj7XcqG1lvG2HfTlcoQ0hpjL6sUyyDTYW9XLa+vqRzV/z9lmMqRUZKyjNzo4PVzq8LpYi7UauzvU9G1g6vGqIZL0w3KBDK3BIM6LyYaFevp0GEzjqUacRHx6fLvUpE9rl9rtM0XVPc3HmRUy+udEbHLvDfbX+h/boiLJK2tXOKji64N1EmumbsoI3perfz+bai5dcBH+b7tw5UN4XWUy/f07sdtDfuXSXpwRPzFmWDnSNuLI+Jjqu9IHXspqZ7ovlb6npKOVHYAfqTX0CmjROe2Fbb9vuibajHouRHH3fl+Uxovr9PSWYLbogmepmycnqi5ZIyPdU5FOa9ivydExGPanmuatFHa2M9TJfU68U5q1D3byi3Ji6I8p26lnL86Kpv7mpHTtcaJHFhoL46Ij9l+vHIAaFfl8Y+cghGZMf+0cXfWiMi5TNJJto/RGAkKy3s+I+kzJWJiV0kXlE72A6Mlb47yb3KC7UOU18wXK6ed3OJ4fsTp2n2PayJOe9v5gnI603mau4+H2qPOllihG7bOLLCvlbRRROzmzHJ6z8ov/38pw2LnZWFVXvTb9ns3ZWbThylv2j9Wzhu9rPLQF/catcUflA3GoSLiZtsPtu1xGmjOePYrIuL3EXGTc6L2MyX92vY+NaOmZf/fdWZzfoTq1hZURCyuPc4RPqoxR2vKzeo8SXuXht7Oyo6LN0ZE7bql/yXpFM+Flj5C0m41BSPiS7bPVoaJWnljrUpS5vnziFZSjmwNTXbVZ+y1dxv7/ZxyqYlryuN1JH0oKjKpLoTSgHibM2Plc5RhvVfGkOyinr+26nERcbFzbdUDVLm2qrtnVV9N+Vnfvez/oBhv7boaAyvHnr9G6iJlmPDhg967HAxLLNEz1qhYQ+d1sJcchL2+lv47/6D8u9U421Lecz46ZpmBhzVB2VH3gPvM20mOJNTmoniPpHOdmbCbFbzatTgHNVBbw+omuWZGxK/LebFYOfJ3SeWI817Ka816krZpdPTeWXn9H2VRlKRBEXG57UcpG7cbq/7ven2vUVu2c0oZ6avizNy9jfJc+FHN7xwRp0taKhFP/8ik7RcOGSHdTlJ/I3b7Ac8NO+YNlJ2AW5fjPkV53xmZDb44Qpks9DMar2Nri/JzlPJv8zzlPM5X2D4iIgZGRJXr/K0l3cHzI8/WVCbEnCrn0m4PUU5JkKQ9bW8TEXtXFH+6Sl4USYqcR97W2P6yMtqjfyqFVDkQsQB6+3yipENKfW/k96nUGQ5QqQtIemMv2sllOZ0RxXufyW/KzyqqX+O8eQyLlN+reylXDThf0mttvzwidhpWLnLu+IWau+69IyJuqevYNqP7Tu57PE503xbKgb3O4cwrdMNW2ZNztjKsV8rK/BGSahq2K8X80OP/VTYkanxZOcLa6zXdSTkq+NDK8id5boHjKOXbenak7FH9pu0jND+R0qgT6tPK5BAqDcL3SnqNssF0gEaEbZaR5b0j4qLSi32Ocp7LJrYPiIiPVhzzxLqO1pTOigcpU6lfqTFGIUp4y+bKbJNSdlz8qWKf6ysrShdEznu6o7IC9SLV3Qyb84huUoa1VC1AXo6v09JIyuVcrmls68+2Fyyhjuore39Uhqz/r0bPdz1Ic2urftw5H2grZabzb1Tuq1dROlDjVZQ+pwxD/qGyUreZciRkIQ276H+w8f+bJP26slK4vEwyKtbTucOm7Ot9ygbMTzS/h3jspVF6m+xYrt+CZnZ0Lhv1ZkmrO0Mfe8d5o/JaX+NzyuVYLlR9KPASgxqo0ZIBd9JrpnMawP6Sfln2eddSoTyu5VhDGR3S//y8kUsPDrH/ve0H9kb8ysjtk5WjsK0Z2YsznFN8mktJnVTuOyMTdjmzG++oucrkIaWRtlBL2OypxsiR7Vcq19m8m+3m0i+3lVR1fyoOUdafdiyPn1+e266i7E0Rsd8Y++q5vaTNY27dzLcqBzAeoaw/Dpvq83LleXgXzY88u04tEXYL5ImSHlhGI3udz+cq59q3GTsvSkQ8ufzbORJyAZztTMp4V0lvKo3xtuvQpyTtoxyxfalyQOKpkctp3qql7KrRkmSuje0PKxt535f07phLlPc+57r1I5Xr1Mhr1S3BAkb3XaTsPGyd5z7qYFbYH0lnlX/PbTx3fmXZDyjX+XxR+TlOmaa/puzpA547bcxjf4Yy2c9HlJkZa8ocMuDn4JYy5zf+/0lJ+zQen9dS9uLG/9+sXFNWypvZBcvob3yksuPiHGVP2uuUCS9GldlVGZZ1kjI87Y5j7G9jSWs1Hm+rrFS/VtIqLWX3knS1cgT/HOV8zf8tf+P1xvy9b6VslI9z7OuWv9MBygrWwW3nR/M8kbRO4/HtJF24gH/H+7a8/sry97pYmdhts5b3X6TsnJIyBPYvyuVBxjmmszv+Lhc2/r+yMlv4Qp/3I7eprKw9XRkO2WX7azT+f/cFOuZzBzxnZcfdf0pav/H8g5SZLBf0cxtyXJcoKzALtb3fLIu/8bifdeO190yw3ZM7lluz/Hu7QT8jyk18zVTOcbt74/EmyoQkC/X3XuqzVs6lH3i9kbR15XZPHPHz/ZayP5W0WuPx6sq1iqfyOytDnRcrG+EbN36G/m2HbPe8muf6Xu+dR/soG9fr1ZxbfZ/VKo3Hq/Y+q1Hfo8b7X7NQn+uYn9UFzd+v/L5V9S5lPenTyhDbl5Xv1x5j7PsZkj4s6UPKzqll9TuvpJyesXZ5fHtlp3v1OaWss/1C2dHddh+d6N6tvL+9RdKth7y+Vkv565UdJddJ+ruy4/W65XG+jfE730k5sHBcebyZct312vInKtdHP14ZRXGUcjm66mNY0Udsb3ROtu71Sm2iRvjUKNE9C6sknWh7b2Vvb6+n9RiXZR6iLinS1zR+Yp7PRN/one221PmLbK8cmTzkMZofTtt2fjTn4D5GJWFURFxve+ze/I6aozW/VX4Z2kZrDlKOOPxGGcb8uOaIb4wOqxuW7OYBak92s5syFP7/nOswXirpERHROvfD9v6SPh4ZUruW8kZ0s6Tb2X5dRBw6eguSclmCHypD6sedg/ghSafaPlJ5Tj9b0rtqC5fv0vuUo6xWX5KMaF9eZmNJe0XFnKfixig92ZHZQX8e42eo7JpVfcn3IjK8f8zdVpm30SlET/zI9q+UoyfvUTYGhh9MhlodH0NCw4ul5nBG3sm6jor1Xtt3wNPXKjs2vzngtX6XKTuKqrOxe/SafavXbqfFwBFr5/zjC2L0OptLze/zXAbqN5XHY2egVo6YvEdZ2Wh+J9qW++kPY1xyWBodxtj5mtnwx4i4tPH4MnWfIzzIoPPgmhiyfmnvHu2WNU5jsukLlys79HrLhK2qHLFeKPN+58hkONdK2tnz1/tco/yeVWHjygzFz9fcsm47KzsyRukPjR03WeiXJZ1mu3eteIqkQ8so5k/aDjgiPl6mQizW/KkMVXMCy/dy04j4Xqmvrhx1GbB70wJOVP7uj1CZylFxzM28KPeU9D+qjFCx/SnlNJve3+gVtreLiOoomXHZvldE/EwZSShlZMAYxb1WOUcVESfafqakryo7A0ZZ1BdmPk9bXSAiwvbTImJg1vloSSIVfXOxbT9NlTlClqPPKgfVetM1fq5c9vCgyvL7THoAK/o6to9TfribKcPTtpa0a4yYsG373VFCD8qXtSYJUv82fjXi5YiIgRda26dExDYDKk1VmfI8YD20Qc/1vf5fypCWPylToG9evox3V65bNyqZwNHKz/VK5ejfXSPimnJxPisi7jOs7JDt3TtKWJrtrcasvIyzn0eOej1yaZZhZZtrE39Q0r8i4g2lsnlejEh53/+3cMUi8I33Xtz7PJ3JaR4VEU9zLlx+XFSsGeYJF3S3vZlyHqSVybZab/qNspdKekq0hB0OKLdmRFzn+Wt/LjHsxuIFWFt1yPd46Pe3Ue5mzU0F6DV0blD997i14WL7ds3fve/8eLOke0XEC0qo1o/afl9nPoIbo5Edt4QXfkLSThFxxKjy5f1HSdql7WbdhUesi2f7AOXcpd4xPlM5sr+hpMsiYq+WbX9V2TF1guY31kauf7kQPGJub0u5LynD6msbDPICrMtaKtH9IurWZLSkDcc85s7XzEaZ/ZSf8eHK++qOylH6H5WDnyiz+5D77sRrnDqzGr9ASzeYWs9L299Qzr/8rvJ33k45X/WPtdto2f7A76NzWbR91HG9z9J58QllfpJQruO9Z1RmXu/KmVukN4hxSkRULx3kIcluKv9OL1N23twuIjZx5oHZP0Yknuorv57mr9xR1Xlr++Bo5MewvYakb9bs1421jsvjlZRRSmPV98Zh+8CIeFmX64/t5yrvA6f1Pb+RpLdExMtGlP2HcsBkUMO2tS5QtrGg6/7aPi3Gz/uwzLjjutALaYUesY2I7zjn82ylPDH3jPZ5kE9QhmtKOcI0dsM2Os5BiIhtyr9jZcxzZgZ+uKR1PX9dxTWVCWRG7fNd5Sa8nnKx6l6DeiXlXNtRXiLp7co5us+JuTmYWyl7bMb1wTIaeZRy5HNowqxyQzgpIn5RKkwHqSS9kvSiGDGCMKrhWmGSZDcb9I0u3bH5uOVG2BxN2U6lEh8Rvx+j5/Jbtp8Y3Rd0v52kv0ZZHsD2XaNueQAplxQZq1FbdE1Y0Xlt1SUb7/49Hvmd6/HwJXv+Zft8N7JuD3hPf4N+0uiJ7ytH735fju3pyvDvxyvDhFsbtsrRoQudazw35/gvRANxVA/s3SU9utcoLw2Z7yi/JxdWbLsX7tRZl045Tza3dz1JF9s+Q/M/61HRJhNnoI4JRhFLh+nXVZ+oSprsmtmzmrKh1evQvFp5LXuKxktqMsxSF+BYmDVOj1XOC+wyn/nr5afnpDHLtxk2b3YvdVzvU5LK9a42Edk8tneX9KWYn+Bw54j4VMV+z1beY7qYJNnN7soRuNPLcfzC9csGStkB0EsQtkjz/+aj/Nb2fhHxyvI5HaPKJRqV5/BGyrqWlB2IFwx/++R6jc8u15+I+LIk2d6x2UEbEb9xztcd5Sc1gwYttlXHdX89P7PwSspz7ZY+GvlX27fXXKTsVqpYKmnSgb2mFbph67mU68cMeG6a+33BoOejJTTF9vnKXtVTlb2Gtb2UqyiXu1lZ89dVvE4Va3YOqoRFxM8ryv1RGQrc/3xvHtBIzmUQ/i8yhbwi4knO9f8+IOm5LcX3VIY8SBmu9ABlI+dBytDk/2jbf0eTJLt5fd/jcW6k1ziTj/xWGXnwkrLflVUf+rinckH3fygbQtUXDHdYHqDPWba/olxXsTkqNrJSGR0TVtR+dzwgxNX2oyPi+303lea2F2rt3lFL9ozbcLnC9muU0RObqyzt4YyeaEuQIWWjp9eo3U057+oxEXG1M/tmjWPUuNYuQ+sr1yXt3TxvI+kukZniW8OLI+Jzzqy5vY606qXOGqo75RqepmwEVIdAN7ytQ5mJM1A713Z/t/Lz3b5EcTwsImrDzE6z/ZAxRi8muWZKmiypiTuG2Jf9dlrjtGG1iHht+9sG7ntQxuJqtvdUXuevV2YZfpAa64VGxKuHFO203qftN0RmgG1m/V+isgPjZRGxJGlTZILDlymnCE3TJMlu/hERN/a+f+V+XtVw8dIhwS+3/dioCAmOiLfYfp9zitODJb03Ir5aecy3l/TTcm+ScsT4tBKx09a51smwe3FP5T35TVq6g3bQcwumDLq8QnOdAONqZha+STnFYIcJD2vaXqu8D27iXNZoXdW1QzoN7A2yQjZsPVka9juWUU83/r9EVKxbpbnQECl7ix+jnPPWNufiecqR1+0kvdU5x+PU3k9kKv6llBHIk21/JXIewhK271BxvMvLV5UVLElSadQ+RzmP4pPl9WFualQ+n6xMXPW/kr5ne1gWw4WwlzouATFhZePlyqUy7qyca9rr7Z/XcdOy/0kuGE/X+MsDNK2pDMd9XPOQVDFaUm7222tuGYqfKCubNw0vVW3Q2qqPUHZYPGXAawsxwtMzqjUxbsNl0uiJ/y2dFxsqE4PcszRq11Pl8galgbi6cnm11myPYxr1Wb1f0nm2Tyrve4Skd5fr5/daN2w/Spnh9fJSfkPnciZDR04n7JTrGXtub09EnOz5c/NurZboHC1MBurParL5U9sq5+RdrorRi0kbaJMqnSM3uDFHb8B7huYHGNSRPkbn+hdKw+xbGm+O/0Jorhe6rirXC1X39T570TzVIcADrGTPLXdYOiXGXpqlgztI+klp6I27BNbJzqkjqzvnvL5K0tGV+32k5ocEf04tESp9DcQzlImNzpAUtp9R2UD8n8rjW0i9e/EdlXXk75fH2yqjEYYet+3tldPt1u+L/lhT2Vgc5WMDtreSMrnidW0HXaJUPhIR40SpNMsvVKbhZSYiznFO97un8vrepaN4Iitkw1aTpWE/UHOjns3/V4uIeSG8pSf/CxXlLlL2/h1Qyt1BmTF0L+UyHm0Vl8Nt79YbgXVOkH+P6kYPlodb9SoLtt+tbDhtFxE3lM9slH+VCtmflY27ZiKjhUrespRyE2lNdjOF/f5cGSbf//zxyoRZQ7kkXnBZJmLANtoSv0gdlgfo20enC7TtuyhH/69SLmVgZUfGh21vG3OhlV0N6h3/k5THbPs+EXHxhPsYZ98q+x6r4TJp9IRy3uErlQ2Vl0n6dokg2Vbta3ZKkmw/RXmdWkW5pMoDJb29rYI3yaiYJEXEQbaPVYb0WdKbG+dF/4jfIB+S9LheY9y5fvGhGh0yO0mnXM8Nygb52HN73Zibp5zft75ySZtRDaa91H1d1p47RMThzqWDFJkcrTURnefC6rev3M8tydgh9hN2rvfcqOwo+S/NXStqkiEthN7xVq8XWnRa7zMieo25G6JvPr/tHQcUGeR4ZR1of+Xn9AqVyJUp22eCsnsrOyUvVNZbj1WOkNfoEhLc31l7rrJzbZyw/KujL7+G7UdFxEkVZTvp1R+cSRI3i4iryuP11F6n/52yw+Spmh/xcb1yms2o/X627OfLyvPp5rKNtWx/OCI+UHH440apaFjkQuO4pp7/oatSV3mtpI0j50VvavueEVGzzOrCHEOs2MmjXhMRH78FHMetlMlgRs77KxW8Byl7pLZWVlh+q8yA++NomRtq+37KJE4nKW+et5f00qhcx9Lds/N14pxvdY1yaYTNJd0ncs7ovSV9cVQvlzMs99PKyv7RUeZglJ6iN0TEk0aUPVqjLxoLHkqzPDmz4u7mbokX3h0Rb7b9OkmbKqMJ3iPpxZK+XPv9sr2BpI8rz+tQhtzv2XZu2v6sMinXR/ue30O5lM0La/Y/YvsjE64Nen2hjNq2J0wqsgDHdhfl3+qC2tFXZz6DRyvnvveSRlwYEa3rdnrCxFOl8bCpGiPwo0Zc+8ouSQg36rlhrzc65Z5ZOuXOrumhtz3w3K0ZpbR9nsrcvHE/64ptj8pAfZIyl8F3I2Jz5/yp90XEyIR8fd+pr0bEMyc9zmWly9/JGcq7l/I+3Ew+c50yadQnKvb7S0kPjYr10Rea7UOUnSV3VU7zWaT8XleNPNm+TZSEWWPud+wEmI33raS8Zj5W+Xl/R7lSxLgrACxT7hjlYvtkZXRgMyT4x8oOs6nVY2xfpIw+/IDyevt+SVsMu2Ys9L6jkTzOdRnie++9VdeRQ5fkR7afp+zwfKMyKVzNPNmfKAeXqufYNq45WyuT336lPN6x7Hdkg3x5ck45O1vSCyLivuX8/nGQPGphxIRp2LvqazitpDwxD68oep0yJOeTyvkstYl5JEkRcaHtdylHh69XLotQ26jtHwHYQO0jAP3bOFw5mnmMstHTVnl5jjI75I3KEKbv2f6jMuR0ZIMlIr5VGuK3jfkJeM4q2x3lgy2vr1AiYrfyb5fEL09QjoAttTxAjJcx/BBlIqhe7/vzy3PbtZTbKiJe1P9kROzrisXNKwwahXDL6wtl1LYnTSoykTLiOe7co5si4tq+gZ3antPOiadsv1Q5f3wDZRKkrZQVvNZMvcVZtg/SXFTN89Q+l/OXpfLf7JS7oXTKVYnJ5vZ2nptXYVB4fk+n+VOaf64vixFHuW8aUb+om1bUKcQ+Ij4m6WMTdq5frNJIqbWAnbYvUUYfXFbO69srw5Hb9v8wZVj6GpI2sv0ASS+PiFe1lJskXFRSzhVX1ln2H7KPqXSolM6djysTFq6i7AT4a9TlrniqsoE4VpRL0TkkuEQUvETSfTS/M/DFQwvNeagyseqpyojGL6k+18akTrJ9vDKiJpQRjTURSZK0pe19NJeFvtfArLke3aoMUD1N0ici4p8u0WsVxo5S6XWa2X6RpG1794USjdA2HWB52yQinmN7Z0mKiL9VRnssmBW6YeshadjVPtd1Us2G002Sfl3ZwHypMsvdSyXtavtMzY3W/ratcKmcbaJcvuEeynU4PxGNhAojLERF+gPK5QkOUI6mjhS5buIXG8e/haT7SfpFzM0RHFX+JmUocvO51l7itpHvafPk62523e+gBAzXKlP1D1vXsbmO27zMke5bcqbFuhHRnOv5WeeyRW3+NuK1sSp9QwwKcV3LmRV4JUlr9n9uUTEPqbIneVSn0TQbLtNykXNphUVlhHkPZeWnxiSJp/ZUSWASEdvavpfGm6P8SuX1bw/lef4DtSecaXbK/UpjdMr1uMPc3oaT3X1uXptRDaOu86diyP/HMmbnaW8a0T2V50cv8/VTVLlmZ9lnpxB7aeLO9ZuVoeonqj5UvVf3eIYyxLx3f91ZeZ7V2qb8e/8x66QfVWZS7yUSOt/2IyrKdQ4XHcO0OlQ+oWxgHaFMsPgCZfRIjbcq610nSVJEnOecv99qwnrMFyT9TPm3eruyM6921YJ/Ku/Lqysbxb8qnQpTFxGvLvfm3jl1QETUZoI+SHkuna259kCtTyu/P+dL+kEZVGmdYyvNJbIs9elRnYaD3EV5HevVs9ZQ/VSG5eXG0hHYm7a2iTrkkZjECt2w1WRp2CfxG0lXRcTfpQw1sb04Ii4fVSgiDlXJcOeMU99S2RP2HturRMTGLfu9SBl6HJJ+VXoSq3ql1aEibfsdylCf3hyPy8oxf1u5IPxYyue1IGt91SiV7/coR9SbvZZDb4C2L9Tgz6U6hXvZ16B1N1/inDe614j9T5KV9CXKjpNeD+ejlMtJ3MP22yNi0Dzwe2lunvpSKdhVX1n4k+3nay6D486SapaEWGtIg9zK3vyRStn3KZNOWHN/pzWloYlfTtbckhM/0Px5SVXzkKLbkj3zjqFLw8U5P3Q/SXcqYUD3l/TUiHhnW9kF8BrlfMB/KP/Ox0sauDB9vy6jYg1/j4i/25btVSPnk9+ztnBkVuIPq/5aOXGnXNFlbm/PJHPzJrWl5hpqm9uuaag9wPZ1yu/f6uX/Ut/3sUJ152lEvE2SnEt6bB5lWk0ZtRknGmEfLd34qMrSPmHn+jfKT7VeY8f2OyKi2aA82nZ1Y17z56avpvz9e1MN2o7hir7GcGsjIiLOl3S+c3rSX6OEDzunZ41dlxi2mwXaztIbjrjU9qJy3IfYru3QGxTlUsXzl0VZRTlXtmqkWNLdI2JH2zuUa++X1ZKro+FM5frMWygTZ33a9rMioiZyY2KlITuwMesR0ygkXRsRx3Xc577K5J09v7ZdFQFXRuU/pGyQ/lE5YvxT5Wh5m/dKOsc5BUTKhGH71B31cvNWZRtgQ+d661tLetGyPIAVvWE7SRp2SZJzrcHDI+JsZ3azmt7DI5TzZHtuLs89ZPDb5+3vNspQj94824coU+gPWzdOtteMiOsi4iPN58sFs3bkoktFeoeIeEs5hsXl/W+LiCPLaPMt3SHKL+FHlElydlV76OmTF2C/k6y7+Vl1z0r6L0n3jog/lP3eSdkIeqiyATeoYbsQ67hJOSf3E8rPOpQjeTVhTydrcHZiqW7U5f2SnhJjrKEbC5eJsMtaoz1dGy4HKiulny77uqBUWqoatuX687fSML+HsmPjuJpRuYi4QXle1iYiau6386iYpCttr61sBHzX9p+VI0Bt+zw8Ip49rLOqppPK8xOy3SzpbravVUbptIVQ3qrZiI+InzvD3VqVEZIDVb/25DiGXgO7NtSi49rOC9R5upHmZ32+UdkwrzVJiH3nzvUJO3vWtX23iLhMkkpDfN0x9j3vmmt7Q+W1tM0VZYQ6nGH2e6h+JFDK++BjJf2lPF69PPfwoSWWvxvK73qec0WGq5RLjtXoHOUSfasc2H6a8rsxlO2Vy3Wpdz2/xvZ9leuXL6485pcpoyDeHBFvdy4zN3CJy+Vg1IjoibY/oOyYbkZAtCbP9JDlr1QXFvwO5fSY70XEg0qDeOeKclLW925WztffRxl+fufKssuU7a0j4kfKetkzlL+zlblUlm2egIhYYX+UI1N/VvZEHdX7GXMbOypDny5ULmhfU+a8Ac+dX1HuXOUo1reVDa7HKtOKt5U7p/H/E4a91rKNlZQXrCMkHVn+75YyFysrDdsoM/Q9ujxvSRdP+W+7+aifym2cXf69sPHcD5fBeXmJpLUaj9eS9LPeOdBS9sz+9w0634aUvbDvsSVdNGq/bcdzS/mR9MIhz/9oOR7TIwf9THmfnc+P8t6zldlc11d2qH1d0pcqy95DOZL2HeVyDN+X9P0x9rtW33FfWHvcfZ/5UyWtUvHe9cq/Gw/6qdzfacqG0lnld/iHckTjMuVo7KiyBys7pB5Vfg5UZqCt2e+FygyozZ8fKjuObj/hOXTfEa/9VC33hQn3fU7f4wsa/19cfu9nReNcr9jmfylDCPdR3lfPU1bKa4/pIOUSThcoQ0w/rkzkVlP2iN551uGzeIryXvGr8viBqqy/KHMj/EY5ynySMozy8RP8XVzzfVSO4H1J0h+Uo1NfHOd8HHStGuf61bLtcxdiOwO2u7GyQbVmOb8+rBwRrSl7a+WKDmeWn3cq1y/ueiyntbx+Tvn3pcolvx5ZrlV/lPSKyn3sp8wD89PyeJ3a7+K0f/qvH32vnTjgp/b+dH75txdm/4BR++ore1ZvG5JWKv8/Y9Y/6wHH2qtPV30u0/xZ0Uds9xm3gO1XSDo25sIHv6WcM/V/kn5RuZmrbT81Io4q29xBZQmRFi9U3jxG9u4652E1MzI2u5Jv1//2mgNWLvr8+YgYZwRgb2Xl9Ubl6Pgjbd+kTAz04zG2I9vbKDMyH2J7XWWD/lcjinxoxGuhusQxf3fOhfyF7Vcrs1dWzSv2BAkjNNm6m391JvKIxnHUZpL9oTNdfjME+gdlv9cMKbPUOm7jsP2GiHi/h6Svj4VLW7+ncr5iv7OcWfq+ofm9tAu1Fu1Q0W2tUUlDQ96vVTag3hm5ZvMgf3LOaemdH8/SeBErjkwW8xJJHy9/u3Mryx6hTNzyGY0/h2mSUbFeyOKdlPNdpezVHhgCvmTjZckISa+KiDf2be99ysyXbS6X9JIoS0I5pwa8XtlL/zWN7tHvMre35zjlZ/zl8nin8u91yl7+YVEOXcPzeyaOgmrRf79aZHsjZQfqQZJeGRHfd54ot67ZYES8y/Zxkv6jPLVrjLc8W+cQe022xuk+6hgCHRHfLiOAvbW/fxYZcl+l73q9krJRfX7Ffv+knK/Z1V9tbx5lFM32gzU6z8JAzrwQG0ZEc/mbmu/zuPtZJOldEfF8ZQK8qgg521+IiF0kvSwiuka5NKforKSMDmi7ZlqSIqIX/XOyxp97/NDIjOjnlm39uTbSZHmKbskze7oufyXlqPgayuv7l5y5GKoSomnwZ70s1mbu4p8uCRU9II/MAtb1Wq3QDdvoNrl+94jYX1pycTxaWUH5iDKx0nsqtvEK5QncS+l/pSpCNfouwqP0V+JjyP8HPR7mqZI+6pyHc5hyXcmRX77IteeOlqTyJX+N8uZxruavKzuS7bcqL8r3VIZ73ErZ0zs0096EF6mevZSVoz2UFZVHqzLxiwYnjLh7TcGYbN3NrllJpaxEP1P5uVoZPvjV0pEy8POMso7bBHphaGdNuJ02w24yayqTTD2u8Vyobr2+yQ6o21qjPV0bLrsrR03vZfu3yobeOBVNOzObPk8ZCi3V3yduioj9xthXU+eQvBIK91blKFEviUkok+jV2E5LV3q3H/DcIPeKxjrHEfET2w+KiMva6j3RYW5vw9YR0bw+Xmj7RxGxtXMu+yhjh+d7LtvubdW9oVaj/361UJ2nt5Z0Xa/j1PZdWzpO5w5oghB7TTYfrnNnjydfS7J5vb5J0qGRYYZt+500MeJeko6w3bsfrqf2VQ56+z5JWY9ZWTkqf7XtkyPitZIUEQueTTYibi7n0yqR8+5rPbh0er7Y9ufVd/+KuqSMzXvATcpOth1ayqzrEdnCoy5T+D9Lg77Xebqu6uuZ0zbyomv7SVo6E/TbK7Z7tnOu/l0lvcn2bTV3rxm2r7srO1t3UHbO/Kfyvrqxsq5cY9BnvUwSdXXwZGWU6aPVvqrAVK2QDVvbp0TENp4/uV6qS1RxqzKCdQflKM+HIuKLZbu1PcS/lLRV6aVxLPxasP1f3juWi5Ub/++9r2peTUTsWnrdtleGXX3K9ncj4qWV5UM5uX7Qja3N05VzFs4p2/pduXBUKXNE+hNAtSbniLkFs/+iiqUMBpTvmjBCyh7Wq5Xfwbvbvnu0ZEMtF7heSOu4WUl7f6Mjy88yUTo/JOmGiJiXsMX2jgOKdN7VkP1PPF/W9iuVIYC/tb1X9K2pO8Ikmca7Nlx+HRGPLdewlTpce/aU9CZJX4+Ii23fTS3LKdjuRYkcbftVyvDlZqOnppI2yajYnpLuOWIUe6Dyd32Vcl5ss1PxthqR06DPJc458oeVx8+R9HPbq2puHlv/fiee2ytpDdsPjYjTyza3VGbMlNpHA/4wTqO2WC5LpC1E52mXjtO+8veQ9Dotndm4JpHSJJlrJ8kyfoiyctlLpHOlshO2tmG7duSSRUvY3rP/uQE6J0aU8p7szGreu7/9rPb+ppzec51z+a9DIuKtfd/rablc0o+ca3E3cymMaiTur5xydjfNJWdcUlQVo6gd722LlNeJSZZf2Vd5jb+jc3nJZ0n67wm2t5AGrXIgSb2lcm6t7MT/jPK4zxj2/j5dlr/6qHLQondO/EvS55yJBvfRiKiahlvyZ93v9RHxRmfCzEHRc8vMCtmwjYhtyr/VjaOGDynnHCxSzue5VQmFeqFyvksr2++W9P4o2THLyO//i4iFOiH7K0MHam5pg+b/pTEyZUauzXVc2f7qyp6mqobthG6MiHBZF6xUyquUSsujlA3bY5UN81M0IpmJ7Y9GxF4esuZf5ehD54QRzjDH5yhv+M0RppEN29I7vENkkrCLR713yH5HhiBO2Zu0dCbSQc91NfBGbXsDZcj41srP+BRlMoOq9Z2LNSR93vY/lNeFj1aWm2TJnq4Nl1/Z/rYyodj3K/fVdKfm+V9GHn/YUuZs5e/V+xs0ow5qK2mTjIpdofpw/KYvK0fG36McGey5vrIxLmW2x1cpR5qsPL9ep2zUDosq2bP8O0kiupdKOrjXeaocyX9puXa2RRWNHZ4fc9l23xeDw7YnacDN29yIY+jaeTpRx6kmCLH3ZFNWJunsmXQtyRdq6WkoLxrwXL9JEiNOOtK8su31lMtwdbmOdPW78rOS5te9Rjk6ci32/SLilV122vHedlXlCOVQEfEl22crI48s6WkdOso6GTBYJc1N0fl/MXoaxcMj4v62L4iIt9n+kOojt0JZx3yycnmk26h96Z7FMSAKMyLOcv2STsvts+7gibb/WxldVpNobnriFjDpeNo/yop8b57ORhXvX1R+VlWGiV2szBh7h8r9nTvguQWbUD1o+wuwzScoQxx/rQxzfqKklZfR3+d1yiyulymTVv1Y0msqy16ovKH0JvffSXnTGFXmweXfRw76qdzvxsrGf5eEEZdIWrXjZ/UuZRj0f2j8ZFmXKrMiT/K3OlyZ8W5VZRhz2/u3V958/6C5Sum+5VyrSqBQeVyfGPL8d5U9qyuXnxdJ+m7LtnZQLqfUe2xlEodfSNptjGN6v6Q3K9cL3E7Z8/quyrIPKef2r5QjAhcoR39vI+nZI8qtrqzYfa2U+4SkbcY45qWuU7XXLg1IejLouSFlJ0k8dZCyUvcmZaX4tZJe2+EcGus+sQDn7PtqnmvZxlrK0bVxyhwy4Kc2MeKg8+OCyrIrqSSrG/Ge203hcz6jeezlO1R1zOX9Z0+w77OUjb1zlXWKXSW9exmcW6eWa0Hvd96k5nqrzNZ6tDLp5lGNnxOVWV3byndOjFje8xVJb9BcUsPVVZ8cccdynfxUeXw3VdyjlseP5hLtnDDBNrrc21r/BrfkH+Uc5pcrOxDWVE71+R/lQMFJLWV714HTlEvvrKpcnq1mv2MncZJ0aZfXZvVHuRTbtcqO9+uUGaSX/Lssj2WFHLHtccf1o6KsoabsnR06H2GERc71FP9RjmN1LdxabFJ9mNw4XqQMp3t5jJFkYiFExAedSwxdpwxB+p+I+G5l8d7SJDfZXlP5dx45QhS5dNMiZeKGtvlow7bRW37ib6pMGNFwmTIcrsvn3Fv2oNnrGqpLltUlBLFf9TqSxe+Ulbunav68i+uVc06qeEi6/SjzpiLi1UOKrhsRhzQef9b2Xi27e4cy7EglPP9QZWKx+ykTuRxQedid1xqNDJO/n+21lNMZrmm8fPiIcn8rrx9eIkU+phxNG5m0yvb2ys6s9T1/ntyaqk90caqyo6XtuUEmSTz1m/KzSvkZi3OpoQ+rwzqDtrdWhpVtrPlhqjUJWTrP7S2hzs9UCY/tDcZFxWhMdAhhbAnbrl2eZNK1nbs63PanJa3tnPf+YlUsk7RAIfaKjlNWJgmBVve1JE9VRh/dQfMTNF6vbDS2mSQxojTBSHPkVJcjGo8vU35Hpsr2iRoc+TXq77RSiTa7hwfMeY26ua5d7m01+R1uyZ4QEQ9tPD7A9mmRyw69uaXs0c5l4T6gjN4I1S+X1iWJ05m2XxZ9CVmdiRmX6xzUaYiI10t6ve1vRsQOy/NYVuiGrSZbP2oSX5R0gjNDWChvpNUx5xNU4juLiJ3a3zVYuQHvpwxjvK/t+0t6akTUrp15V+UyO98tj1e3vTgiLq8ofla5WB2ovFj8RRXzJqJj0gcvzPy4G5Q3/xM0v6LUmjUuJkuaNXYIoidcRzIizpd0vu0vR/1cqUFeHBEfs/145bzxXZXfkbaEIH9yzkk9tDzeWbmk1ii3kqTSqPyacuTwXeW51WsPOCZYa3SShovtRyp7sLdXLiHx7Ipddu6AsH1nZWKs1W0/SHPhpGuqMnOtJkg8FRHjdiz1e6e63ycOUn4+Z6uyQd7SSKzttPymsne8t8RQtY4hjAsRti1NtrZzJxN0nE4cYq/J1jjt3NkTEd+1fY7GXEuyXOd/rbm5udWcKwz8VNn52iUxoiTdWK6xUba5iSrPb9urKTsS+5MDvbj2d+jodY3/r6a8brd1Bu4k6WnKOniXKXNSh3vblDqOlqV/2X625vKENBNnDp3mU87NE0oH8Vedq0OsFhG1U1i6JHHaS9LXbT9Pc/fULZSdr0+v3O/MiYgdbN9JGXUmSadHxNXL8hhchpBXSLbPiogtbJ8v6UGlx/iMiBi5iPUC7fsJygxhlvSdiDh+jLLnR8QDSiV+d0lvUSZDqBn5GPc4J0m01dvGycqb1qcj4kHluYsi4r6Vx3CWcv7DjeXxKsr1Rx8yuuRS21ksac2ozC5devE3V4ZaVSV9sL1eRFzlzGi4lEYDcNR+XzikbFXnhztm9isdLQN2O/zGX+aj3L/8f7EyTO1tEXGk7TNr/0bO5Cfv0dJJvqqWGugdh+2PKUOOvm773N75NqLcRspw3Icpz+9TlZW8oX8n2/soE6jdVjmCt5dypHYXSbtExHaVx9x1yR4558n2Gi5LKrURMWqZK9n+lTIj6OHKhFd/HfX+vrKLlEt+jbVcRzmfX6S8aTezqV4v6bMtHSe9UbE9lJ/12KNipZLxBi39nagZ2ZroPmH79L4RhJr9raUMZevcSBzn+jqg7HeVDdUvlKeeL+l5ted12cYdNf+zHrm0UqPcIwc9Hy1Jlkqj8J3KCJlvK9eR3CtKYsdpsr1aRPy97bkhZTdWTsNYRdkBspYyVPbSirJnR8SDxzzWe0XEz2wPrCtEWUanYjud5gbb/nFEjN0obpTfTpkcZzNlp+XWkl4UESdVlD1COe3jucqIpucpQ0f37Ho8XTmzMQ881/vet31EHNdxH4PubXvUfhdnkTOZ4cc09zufpvxe/VY5xeyUEWU7n5ulcfocZX3xcypJnKIvIeaQsttK6l2rL46ILrkvZoYzKegHlXUmK6fNvT4illnS0hV9xPYad18/qrMyArmycl7KT0tIzFibKP92WTOrl8zj8MiQ249ExNARl5gs0VbPrSPijL5DHOdzXrk5ahqZcGdkmMeoG7gb6+C1GDvpQ8ytfbmSMhHD38s+V1fO763ZRueMcZ4gs190zKLoCdeRLA5Rhsd9RHnsu2pEopgBxk63Ly2pcI81GhQR+9j+jPIc/ofy9/6mch3H1mW7GjqvNSppg4h4whj76nlARFzXoVwviuH240YxlPP5c7afGRFfHXO3CzEq9iXl3LwnK5dae6Ey43itSe4TJ9r+gHJkv9kgH3r9KaME16qMCjcaiWvYXqOyYnqq7ftFRGtCngG6hDCqHGvnsG0pG7Dutrbz4yLiDbafrszwu6Py/jq0YbsQnbZFpxB7d1zjtKFLCPRrlfMOB3WA1U5ZkbovZ/ed/8/eeYdJVlXr+/0GUeIgKngxIIgIIgIiqCjmdM3AT0SuCAKiCAqjXnMAI8oFUfGqgDogwatgAvUqSs44SDZyMWHABDiCiMD3+2PtM326psIJVV1dNft9nn6663Tts3dXV52z915rfZ+k/wd81TWjJimqtjah4VAr0px4mO2dFQKLx0k6iRDcGimlzTmIecFjCK/nKlwo6aNEyjZE2cj7KkYS3w/sYfum0jgOIzIEK5GuN39PPz+syobLOElz6V73zZ6L2kTj96a7iDhRUbDQ9lkMcBaYMt4FbGv7j7Bs4/n7zKEbx7QvbF9M3FDeQOzercXs2sSeqLvX1y1E0f8VPdosJBYb2xBREwFbpg/E3jUmnI0m8SWWELnujyRSEQeiGcPwvsd68GdFylCRpvESIuWqKn+S9CLbp6b2LwYG3cxa38CdUhglrV4nspU4mZl6V4gFzMnMpF8sh4aTxlxb2a/oN/08S9VU0um2n9279dB8JFe1fYYkpWjpwQq13YMqtq8lty/pLbYPlXQk3V/rvmnfnp2WuVPFMXbSxmu01sKl+HuBDyqpi5cZ9PeW+BX1rSuK53xFNbMJbG+Yxt81KlZxzPd1eEMfmCJ/5yiySKrS+D4BFNHabUrHKl1/Wi4StwdemSL0/2RmsVblGtIkPb+gTdo2au7tvHL6/jzCU/Wvg/Z6227aqmWKvZt7nBYUmT2VN3tsvzotEN/lCr6z/XCz2uA3EqnWd0q6nRqbCI5MidfZ/jLwrQZDLkpdblZYAP6BKOUYNeXNuTsJwb+9+7aY4fPEfbUoF3kFsQlc5Z6zRbGohdjwSO/TOlyQriEnERkkG9VsP6ekRdI+LF93XmUx3/i9mfr4CZERUIzl18SGf2Y2C4pFbeIvxIbPnDHVC9uOxUrdKNk26avw4Xw+sUjcV9LJaRLZySeAHwEvc9TXkSJb7yZ2QKtGe+pO4vcFvl3a6f8mcVP8K6HkWoVZkymFNUnVNKj9CUGdTSX9lriw10ln3JeIlHySuNj8hgGvle1Xp++Na04lbUdE49YA1pe0JSGetV+F5rWjzAzH5uMf6fttkh5AXDQ2HNBm49LPnYI1fX2OPQQfycTtacL1c0mvI1KHqnq6QkzkAbaomLxQiGQt6fus0dLGa7TuwmVYf28T6wqgXTYB7YSnignt79PC+nfAgyr22+o+0eb6Q7tF4nNb9LsXcT86gpkUxqpRnn/Z/oukBZIW2D5LkSFUlabezqdJ+glx/dsvTXCrpAIvIBSQm6RtP4dIsX8QsQFRsJRQO6/CL2m+UTTout6r3d2SDqNBnWyJRrXBTTcRSnxP0n8SGRjl16tKiv7RCsG8dxHlRWsQc6+R0vT/lNjIdlng6r2SrqjYdoGktTsitn3n9ClD4g4nOyZHydtriU2uxjorc8g3gPOICGDduvO2781O6mScrUh8R9J3mdk43YUQzpwzpnJh2yv1iHo7NPclbFSKNI2DiFD6k4kdum4L2yfafmX5QEp5eJ+kqgtMqD+J39/2Z9I41yYWIl8lJi6X0MfXUNLbiZv0qpKKiLKICN1A9deUbvVa289UKB8usL20yqALbP8f8HglT8Y67SXtD5zo2Z7Bu9r+VIXmHyMmL6emcVwp6cl9W8xQO8rsmTTmnYhU8d9W7KvMN7W8st8gpd1+aTeVU3LSe7mJjyREnepqRC3l+4mIVtda4x6UoxarEJPjy+gRGUsLcoDb3FEHo6gBmQvaeI3WWrgM6+8tZTGsGQ/j+leRJtkEwxCe+oCibvVNRF3gQuopbu9ETX9nSbvZPkHdM3uqqpo2XiSmrIflal0rtq2dnl+ibXlPI29n229Lr83fUiT0NiLSPqjdQCXmPm3bpNgX1N4oSu/HWUMh7i1X1Lg3Nk67TLyCGPPriM/Sg+kTRdTyJUEG/mz7NzX7LTZY9u84V5WShOOZEdwrNqgqlQe1JUWIO/UjvlCh6T8kbe9UG6pQWf/HgDYFhxOZPacQr9FLGbzZfCaRRvuH1N+OwGuJedAbGJ6v/KhYzR0+2lXpNbezfW7DsUyvQFEDJD2MEJB9c7qGbU/cSy8iyoXmbizNrnnzG0lfJ2ocvgr8T90bWjrHj4l6tULQ6F7EjeUR6iFaI+k6213rUCT93PbG3X7X5bmnlR4um8S7hxhK2sl+DCHR/3XgcCdRDVUUGZF0iO23Vxlfl7Zn9hpbxfazFGCL4/1SGEttr7C9Vcexrv+fLm0vsf248vOVhLsqtN2I+LA+MB36DSEs9H8V2h5E3IT+SlgsnWL7xkHtupznXlRQ9kvvj12JicoJhLhGMYE/wfYj6vbdFEW6vutufnQ5z4OBQ233jXBJ+qE7RNe6HRsl6m7ZU7VtLZGetn9vmqAdT6SKQkyod7d9bYW2xefpYmIS/BfCj7LndU8thKeGhaTrgBe6hhWWpNfYPip9lpfDFZSaJX2fmGQeQly7/0jUJj2hX7vUtquVne2eacxqmZ6fzrE6ESkVM2nbJ7qPEFpH+0OBm4mMnNcT6tA/sv3OAe1WI1IJ13ek224MbGL7mxX6PJMoEWmsxKyGgn1NUHeRv/sAWxAlTQPFZ9Lm/urEpkPttEtFWv/HBx0r/a5bDeF9COGpXd2jfGuYqKHg3hD6PQh4KrGw/TaxKXm+7Zf0a5fabgl8gfgcQfgHv9LhJlCl782IzV0Rqr8/GvD8ZfMbSa8m0nqfZ/tPSiJ6VfodF5I+AFxou3YEsO68OrXpeq0kXu89qn6eVgQUStPvcId4q6RtgINs99MUGSpTGbG1vUOaTO4EHKOo1foSscitKnd+EnCxpG+kxy8Evphu7L0uHhdIeg/w/vIuqaR3E+ptVcc/6w1QTOL7NDmcsGFZifDMXFkh+LMHYZhehUslrVUsklJU8Km2v16h7eWKVKuTmT1xqDop/QYNrSuIdBwVr3eKIFf1svyNpCcAVqRdHcBMSmdf2kSZ06T3vQpbpF2ImsAbbD9zUFuVItS2/ylpNUn7uX+E+vfMpNL9gdlpdX+oOu42pIvbYlLUQtIthIVPUz+3G5hRGuzWX2tf1hTp/I7tpZLeRaTFfsDVlUXbWPbU8uAext+bOBp4o0PwAklPJeyKBi62aJBN0CYq1vF3djt31bri2v7Oto9K3+sKApVpU9vbxMqudbq625X3QHNv58XE/aF4H95A3G8GLmypL9o0C7VIsVcDj1P3EPlTiG59mZm67l59LiD8PtvU2O5BqM+WeWWXY0DvlPx03f8EM+JIXZH0dIcoYdeocMW5RFPBvba8hFDpvtz2ngqrk6p+5VcSOiwL0+Nawn9pIdt3MdvBX9JCvIjAb5IWtevRwAN8DBwIvEPSP4kSlDo13HXn1dD/WjnOMqf5yAadi1oA20sUjhpzxlRGbMuki/wuRIrah1wtRaxouw0hNy9iB67vGzldnD5HTICvIG5ojybqEfceFFnrc14RdUKP6vOcQlnyHkQE4DnE5PINrqAq2DLyWdtGpqN9G+uK/yIWDp8hXu99gd/YflOFtvcjbtTLbJkIufwqFiNrEcJHTdQMi3P8G6Hu+TJgTVcQfmnzfxonCr/O/W2flx5vT9heVBG76dw5XUDUoP/SoTja7flbpue8D3hP6VdLgbNcEt3oN2ZHau32xGfqMGJHspK9S5sIgsJ65ul0LFycasu7PL/131v0646MhW7HKpynUjZBR5taUTFJhaDZl4l0z1l1G65un/VxIsPn61T0dy61bSNm0hi1syja2V3S1TuPdfx+GOU9jSn9vcuudVXel2pXY1uco7gOFN/XIFJ8+4nuFW3LOhXLPE5tv6XhWCplX6ihtYmkXYmMnu2JWsaChcS4B26+djnnwDFLOtihRr+Y0vuKmffXwM+TpKOBI91MKbwxxedOIRL6NOKae437Z0+8EbjF9uc6jr8eWMn2x0Y01vsSqcd3AP9HlKFdmcb9Ttsn9Wk+VVSZV5eeW/uauaKh/hmrPX83CqYyYguQInG7Eh5K5wM7FpPqGlxOTJjukc7Zt04n7bbtrEhT3Yy4KL/VFdJTO8bebRLfNzXFoVwIMYHuWvc1gG6qZZXeH712mGvQxrrircTO/2uZWZxW2i0lditniVwpalyq7HQ3VjNUiDXsQgg3nQLs4wEpRCXaRKgbo/Y+kkvLnz/b56fJclXKm0p3EsqoPf9PaSf8Skkn2f5Xr+cNoPhMPR/4tO1vKDxuq9ImglCr/nJIfy/A9YoMk7LH6S+qNFSki76JSBfdR9L6kp7kaumiTaJi6xEbQ7sQ74kvAV+puogvsRC4DSgvVMyA+uBEYzETNajtLXGzmte6vp3la+m6HStzBi3LewDU3Nv5DoWlWnHd24gK2T1uUWNboolgX9F/Z0bKBaqn2L0MSZtQPaOpaY3thUSGz/2Y7TiwFFguGjOIFL2s0v/StNi7hpkFLVXalt5T9wD2lHQ99ZXC27BEkalyDLGJ+XcGX7v2orsw3tGESOnHhji+ZaTP1weKx5IuIoI3H7FdNbtvztEQ/JmbzKtLNLlmrmj8QNI+to8pH5S0N/G5mDOmMmIr6ZdEHc//EMXys274FT8ErycicjcSE5a5ukiiqDsruJOITLWS7q/Q5+eJ1+y/iQ//64G13SGG1aPtKkR6WWe0pWrE9keER14T6woUacSbpHH/tOrEvttOco0d8W6R0+WO9Wh7CPAlN6g7ahOhbkPxtynEJnYg0ifPqhrJk3QEsXD5IjHuXYh6oq/A4M+katZ8lZ6zMRFt7RT2GChGoqgZ+S0R0X8MMcG9tMbf3DiCoIb1l23+3tR+bSJ1sxB+OBc4uGKE+0vEDWx325unhchFFT8TjaNiqf0DiY3MNxKbiccPaDIUqn7me7StXdtbars68X5cQMVaV82kq7+U2AQoWAhsNijaq5nynpcR76265T3FBlkvb+ft3aMOS9KzCLXbzYjNyycStYhnV+izVY1t2ug5krAkKu6Pn7U9UHFX3T1OP2F7kz5tTmP5Bd19iI2c3WwPtFlTyxrb0nnuS2Ql/brLIr38vG61iPchUscP9Iy4Xa/2Ra36JsT/6htpzC8EzrX9qj5tH9Lv3E5Ca3OBIuVyobukZHY87+pekcJ+v1tRkXS0o7a+Wy23XUHjpcm8uu01c0UibWJ9jcgGKK4V2xCBlx1tz0nZG0zvwvZsZi6y5d0/qP4huA54XL+JwqhoOolv2efqhDR+kWp0OmEuP9DfVdLJhL/XfxCpkC8nhEwO7Ntwpn3XG1OVG5KiBvA4wlZBRO3IHu6jdKew+XkCodR7ROlXC4kPYBXxqIuAN3u2muFhHpD+pZapcan9a5gxCj+dmGTVihY16Pda24+UdAwRFfuOaqSo9rghFQz8TPbYhLjcA1KwJZ1PbFAdQUyS9iSue11Ffzrargb8O3C1w5ZkPeBRtk8f1Da1b7xh02Thkto1/ns7zrMWcLfrKZQ3ShdNz6stPFVquzWxqH0WcUM93BUyIDQcMaU2YiYXeLbPcZ22qwP/SBHJhwObAv/bb1NPw0tXb1Pes9zfrBlv574T+rTIejzxObrYFUpsUrundDvu8DyuhWqm2Cvsuoo5SOFx+r7ivlFxvCY+Dz93Mz/cyqTNvLfZviZd735IRNM3Ao52jxTZjkUDzIz5B57taTmo/9OB/1dcdxTq7Cd7PLWzlZB0hu1nDDrW8furgWe6QzQyLQ6+nxe284NhXTNXJBRlU8X89lpXELsbNlOZimz7qUM4zW+IFKlxUEu4YRikBezbJK3hehYfAA+zvbOkF9s+TtJJwHdr9N3YuoJIl3q2UxpNmuR9kf4evPckPO7uwWwLhr8RKZBV2Bf4QloAiFA4fuWgRm6ZGufwR/50+mpE2oU82/Y/JO3kasIcp6qBj2SBG/p9aqbma0OFQFnBmsTEaRCr2j5DktL77GBJ5xGLv0GsB3zLIdL1VEKVtIqFQ0Ebr1EAbN+ZNlE2Jd6fg2jz9yJpWyLNvonIV6N00URt4SlJ7yU8oX9MZOe83cmfsSLD8P5tLGZCpDB+iQa1vUQk/Ukpwn4G8TfsQh//cLdMV9dwyntqeTtr+dTDwjJt/XQNHZh91WQB2zGGxin2buBxWnW8GlBHm94bGzM7c2OQtcmGtq9JP+8JfM/27mmBeQE9UmRdsZ69AusTEZ+CO4gMpXmHIlNtNeB+6bUuAigLCdG/fvwX8C1JbyKudxBzlkMJLYdMFxSaAv9D2CXWLfGrnc3U9pq5IuIQnuwXyBg5U7mwHRLXA2dL+hazJx2VdqcVtY/3Z7agyCCrjlaTeLWzzXkCMZFcg5g0bAm8xvZ+g9oSEzqIuq/NCaXdDSq0K/qupQDbwcou1YbY/pmklfs1SBOHcyQdWyUq3OMcbdQM1wOulVQ7NS5Fhg8mXqN7MDORrpRqmngBcJCkHxIRkEFeowsIb+RDqekjWTrHe7odr/DebFvzdXsa/88lvY5ILV63QjuINOltFP5snyP8jk8iUpMG0nLDpvbCJdHm74X4O/fzbJGvxcSifhAHEfXXD5Z0IildtEqntt+ffvxKihpViYq9m7hOb5m+PqRQnq4UGfcQvH9tV/Im7UGb2l7Zvk1Rv3RkijxfXrHfDRTlEJUneJpd3vNq0iK0WHhWWWAm6no79xNaMz18rDvG/ngiuvwIYlNzJeDWipsPMKPIXCwi6ygyo+Yep4PoeU2R9Cpi0+VBhJDl4wk/yUGvV3ny/gyibhSHMvzdbQZbkeMJh4avEf/fHWmmvj0XvIbI+noA8f4oFrZ/I1LWe2L7C5L+REQCNyf+1msJW5T/HdWACyR9xB1+sN2OzUNeRNwHv5zej18iFrlVggSLmclmehopm6liv49V6Gu0mXdl5oipTEUeBmrnT1iuzy1uBgMnWoqU3A2JG/zbSr9aSqSv9o1GqJ0K6yVEtPLUUhphVQ/cVxGLgEcBxxKL4/fY/sygtql9LQXYjrafJ24KRU3dy4F7uIKgVYo6voXla4P7+Zq9kPhfFIuW9xCbCb8iaokGCu2oRWpcipq+geX/x/1q6x4HXG/7T6Vj7yEmPvvb/p8K/TZS2Sy1L9cAr0KKtHn06rHbEpsk9yYsUtYi/G8H2m8ppT9LeguR9nmkaihQ99qwcR+1zC59v56Iwh5ape8uf+9C4L+q/L2pfc9U0QptjydsXP5BLDgvcfV00c6o2ECfUg2ptk4Nau01BDGTNqRF7H7EJG1v29eqYm2eGqSrawjlPR3nWyv1eXOddk2QtISo5T2ZqPnaHdjY9juqtnfzFPuDaOhxWuHcPd+jilTXbYmU7a0kbQq81/YuA855GlHecgORubGh7ZsVmRhLqly72pI+U09KD8+1XXXDZixIer3tI0d07rfb7rbh0/a83a55Vw2ao84n0j3i3cDLba9U4fmX2X5M+Top6TzbT6rQtva8KzM+csS2B1UWsH04kJiU1XrTp4nYr5jZGa5LKx83279JEY+CSnWbtouUwXOBJjtYtRRgO3gtsD/hQVuI3fTzdC1zIrHj9wIitXgP4E99W8AHid1vJL2AUI3dlbB1+gxhs9QX2+ekSfnGtr+fJvUDL8yJWxrs6B5NyftQ0keJiPqmRLH/wIUtzVU2geU3VyQdRkRAK9E06mL7B6n9AsLKqY4S879SFsXuxAIAoG82QAdNvEYLpKgFfzkhzAYDrteKLJGX2n4zoczZRK38UklHMVvk6+yKkbnFhOjUs4jrwBWSznU1bYDaUbEaC9eumzJq5/37RiJy2W3TsG8kUUOo7SUiRW8HvpYWtQ+levpX7XR1D6e8Z7msItXzdm4c+bR9naSVHFoEiyVdWGPYbVLsG3uctuR227dLQtK90iZMT8GqEnsTUcRnAruUNh4eT3xGK6FIXbbrlzUV15iRbgwNmT9IWtMN/c4HsDPdMxkaoXBk2A94qMKCr6BINZ/3KAS6Xkrcm+4ighNVaJPN1GTelRkTK8TCVjVqCiV9zPYidVcmrKqk2Ko+t+kknna2Ob9RpCNboTJ8ADM1aL3GOSwvtpvV0LrC9j+Bj0r6JBF5/W06VoX72v6cQpirSE8eFDW17dvSzzsBn3PUH14mqUraNpL2ISbF9yFEOR5ILIr7iU0Uu6tnKZSRv8rsFPl+N9F7pEnOPYiI+m3ASxz1vqtVGTMxkV8duEvSP6hXS9iN1ai3CfJJlo+6DPRFU3hRL6ZZzeiexIbHB23/QtKGQFV7I2i3YbOImgsXR4r4Y9KCpWkqzlbpe+ci5wkMWLDZPjN9frYlUr32JT6TVRa2G9neJW0kkK7VVdPEBtErZfN3RIr3i5htR7CU2J3viVM2iZvVjreu7S2uV7Bs0+bPFRfE0D5dvQ3fYCarqOp1umfkk2o177ele9oVClXm3xPXsqo0TrFnRuDrTkXZyh9ptvnbjX6fjxsUNetfB74n6Sbi/d4Xh9DTvl2OV6qbk/Qo4n9yn3ioPxFijtf0bznRvNv2yYqyjecQNbKfprSZ3IJhXQMLTgL+ly5Zga6hbj4uFJmFKxPzgJ1tX1+j+SJi3nEAsen8NGIeUYUm867MmFghFrbUqyksUlrbFPC3qs+l4SSeiJa8UqHEWNc2Z19iAvpAIlpyOrGz149hebG9mBAiegMzCrB9d/AVvpdHpkn/WkT90F3AfST9p+0vVui3qCf6vaTnEzf+Bw1oo7QIv41YiJajw1XrKPcHHgtcAuBQ3B00seyMDG1T+nlQrdn5kgofyjWBZ6TJ1lOY8Wjsi9vVEhapccViayXCw3dglKZjDE2iLp+nYc2o7R9JeishaIIjzfzDNYbcZsOm6cLlcuAbCqXycv12lbrNxiJfAOk9tjrxWTyPsCeqqojaJio2iK6LfHcRBlHUND/YNdQu60YSPYTaXoVA377ENe8yYC1JH7X9XxWaL2L2BO/pRLbKXNA0q6hN5PMVxDXndcQ95sFE1LgquwPfIjzHrydKTiql2NPM47Qqr+j1C9s7ph8PVijSr0UszvvSa0O/dN5BG/tHAW9MC2EUontHExtj00pbv/N+DLVW0KFbcEu6r5VZQyEc2tTrea7Yw/ZPGrbdIGVwLctmStfbSyq0LTYp6sy7MmNiKhe26qgptL2/SjWF/doWkRy3U1L8dfq6Z/qqTcNJfBsV1k1szxKmUQgV9UtPsbvYDzhUZCvvNHq2pVBVoYgn2S52lvcEfmZ7B0n/RuxIVlnYfiAtit9ERMgXMiBSQyzWryAEIn5sewmApEczo9g5iH/avqN4iVIkte8NrFhwSHpo5y5liub1a/uatKC7g6j7PkXS/dKvK03w0v/z5UTN1fslPRhYz3bVSdoLSj/fCdzoegq2TaMuS11SbrV9vsLjcSCKeurDiM/whpK2Iqw6KvlfEhs2/6DGhk2p76YLl/sQQnPlG+5AQSINoXacEPN6DCGGcguxsL/IdpXNkzZRsbZ8T1EPfQ/is/0nSefYfuOghi0jiW8nNi8HHevGZrb/Junlqd+3Eu+TgQtbt0vPb0vTrKLGkU/PpKz/g/BorkvjFHvPiC9+RqGBMdDjtEDSTsBHiGi66MiS6RYF1Wzf3ILitV6DUO/vR1tF3tWLRS2A7bMVwmDTzG8V5RvPBD6iSLdfMKRzDztiW/AtZmrlVyG0XX5KNcHOOUfSbrZPAJ4naTnxxopBo8bX2zYbvpm5ZyoXtgyhplAt1Ged6nPVvM6k0STe9q8UasZFMfx5KSpRhSNZPvra7dgsJN3f3b3YKjPoBt6D8oL6WaSLk+0/VF1Te0aY5hYiLaVKm89L+m4aa/m1/QPVaxrPkfQOYFVJzyIi430N7EucwvL/k5Ppb2+EZ/smbitpHZfEpCrwKUII7elElOfvhPLjthXbr0d4mhX+hGtIeqST7UcFXkFMFspRl50qtGtTM3owEVk/Oz33CkU6ci1c37IHmi9cPusO0/l0LRvEMGrH35Dar0F8FhYTWQL3qtB/m6jYIAZdENZKr/WrgMW2D9Ls+rN+1I4kql1tb8HKCvX3HYBP2v6XpErRHbVLzy/O0cQyDJpnFTWOfKb38/tZ/l5eqYzCLVLsVfIztf3LzmMDOBR4oe2+JUEd/JnIuCreR7NEvhiwGdByQx/geknvZibzbTfCu3eaeSnhd36YQ2hrPeDNQzp3lU2u2rhDZC7dD18zir6GRDH/rZ05NqTrLYqsvk6h0VpZZ5m5YVoXtsOoKfwcXVTQqpBS044noidI+jOwu+1rK56i0SRe0oHAPsxEZ06QdLT7KPYpBGqeAKyjqJktWMhgQaNhebE1uYHfnCYsvyWiO3vDsujnqv0adlzclmNQyqft36Z+y8eqRmshalv2JnbSX0MsXAZNhjclLqprpY2AgoXUt5Kh5qIW4HEOld7LU/ub0uZLVT7N7AX5bV2O9WOHFCG5nRR1Se/3QZPLrdL32jWjwJ22b+nYKKmTGtbUsgeaL1wabVAxnNrx1xGbao8hIr2fJ1KSq9BGeGoQPVM2E/dIk9GXAu+see4mkcTGtb0ljgJ+SWyunasQo6u6adI4Pb9ELcuwEo2yitpEPoksm52Aq+36tedqkGKvdh6nBTfWvCdCfNafSmRafZFQYG7yN9f2/EzsRVyfv8qMmGMTEbuJwWG79Ufi+vVzYrH08yptFc4M+7C8ReNe6fuHhj3ebtj+oUJRf15i+yiFOOLfbB9Rs3nr662i9G01YmPrs8SG5rBKCjJDZloXtq1rCmmngnY0y9eZHEP1OpOmk/i9iQXIranNR4ibcT8p+nsSKUr3YPZu2N+ID29PPDwvtiY38NcAnyD+x4ts/yEdfwYR+enHvsA1wJeJi96o0n26Yvtu4BhJxzEjeDVo8rEJMZm8NzMKvRAX531GMc4O/pVuLEUN5DrMWFlVYZagUfo81rn+7MHy7/9Xdjk2i5YpRNdI+g9gpTTRO4Dw1a1KG6/RWguXlhtU6RSta8dXBT4KXOZ6aeZto2K1UzY7eB/wXeAC2z9QpPZXmpjSIJLoIdT22v4Ecf0r+JVCdbsKtdPz1aK8p2PcRap7JW/n9L6/2cnTOP2NOxB/70/cpRSmC78BrmmywEs0SbFv7HFaYomkLxECUGWtjp6bCLYPVOzEPZXY0DlS0ulE7WedyGkjz8/0/q0qYjYVKMoRtiHu0YsJcaMTiA33QXyD2Cz5PjUDKG3ouEcsIDY+6252zykOccQXEe/JOu2GoaXwBNtbKCyR3ivpcKpv5mXmmKn1sVVHTSGwrKawX/qhZtRnX0pMCGuroKmLx123Y33ad/MYu9yDPSyvJnaTb0+PVwF+0Jl20qPtQxypzI1l+psi6ePEAvXrVLyBt+zvvoSM/i7E7uqXgK/UuMg17ben4BVQSfBK0na2LxrlOHv0+3Li9dqaqIN+CfAud4jf9Gn/VSKl99Pp0H7A02zvMKDdrsB/ELvh5ejfQiKi+swB7d/T7XiVFKKU3fFO4NnEpO67wPuLz1eF9o29Rnuc7x69Foxp0+6pxIKw7B+9FDjNdt+FmqS9gHcQE+8/Ogn8KGrHD6uYOtmYLlGx8wdFxUptr6N+xkdr0gLiQbZ/kx5vQL0ayrOJKMKy2l6gUm1vat8oNU7SEUT0oZyefxPhRd71HqfwGn9c6d5SlPe8hlDu3r7imGt5OytUUHe0/TtFjfv3iUjiFoTq+Ksq9LktkYp8Ds3EHIvzFCn2/wn8m+2BKfZq4XEqqZu9jl3R+zttuLyM+NvfYfuYGn3X8vxMY+01mbTtvXv8buKRdAVRsvFDz/gcV/KElXSF7a1GO8Ku/ZYzmO4kNlG/UvXeNi4kfZDQqvgSs8URq8zLz6bh9VbSpbYfK+liIvvjL8Rm2cYN/ozMiJnWiG2bmsI26rMFjepMSpP4DSWVPT4XEh+kQSwGLpH0tfR4ByKlugprpol4OX16jwrRjmGwkIgUPbt0bKDgTVMc/sKfIdLaHkjUEl4r6a22j+/XVt3FOcrn7ifO0VjwSiXPy/Q+6ex34C55moi/HHio7fdJWp+YoA1MqbF9oqTLiGieiKyCOguJfYkI07vS33EGYXk0iAuJGvP7MfuzuZSIpAyiLEy2ChH1rjRuR2ruO6mfnlqwiOZeo10XLvQQn/KMXdWxpajYAmAN2wNTVD2c2vE2tBGeapLxsQxJDyc2XO5ve3NJWwAvsv2Bfu1sW9LX07iX1VDWYC03rO1Vu9S4rdL3Oun5wyjvgfrezqvaLmxqdgM+b/vw9N6+omKfHyQi6avQQMxR7VLsG3uc2q79uVMINb2Y2KxYh7iHbl1svtSgriVUN7/p9YlrYFWP9knljnQtKO7PdcSyvinpeba/PaKxdcXtdWDGRZH1WIjAierz8sbXW+C0tFH0X0TZnYksncw8ZGojtuMkpTm8l4gyFXUmBw+KCKa0qw3p4jFGKJYOTO9LEedl/dqulPqoUF1+p2enT3/I9tTK9KfXaleiru8y4HDbPxrQ5hfMqAl2YvepQSpH3RVWUCfbPrbzdz3a9rXjsH1cv9+nc3yaJABl+xHpfXq67Uq1NYpU5PszuxZoTu0BUrT9ycCvXUPsptT+XsCptgeKIaUFz3+yfP1TbYn/OovM9PyuC5dBkQ91UVMGqtrAjJ2GUbFWGR+KFOg3A0eVPp/X2N68Qtv/Bo51UhquQ8qweTaRAfFORxp01UjPVZ5JjdsivW5ftf3sQW2boBBgexizy3t+njIFPmS7StolkpbY3iZFgB+dFsaX2n5sj+eXI4Y/BN5u+7vpcdXXaontbQY9r0/7NxP38Nop9qX/z/bEff0wIno60ONU0oOIMqInEvec8wlRtRv6tLmVSKP/InAdHVHUGp+JbYkNwHsTmxELgUNdQewvbeC9g7hOH0HU61dJGZ9IJP0nsDExjziEKAk7yVEuMKjtUiJT5Q5m7Aft5v7wlVCHDgwhOjZXgYzaaCZ1uljIzhJFq5J90fR6m+7dj7d9YXp8L2AVp/KIzPxjaiO2bVHUtC4mFpXHEDutb7N9+qC2blhnkiItvwK2S2MoJvF/73dDlbQw7UTdh0gp+WXpd/cZEEUsGJtMf9OISYv+3stM5O5/iMlSVX/R2qq4JW5WQ8GrKgvXCjQWgJL0eiLCcyOxaCpuMJUEZxT1xAfavjk9XpvYSOibVifpm8Tn7hqFwM8PCSGIjRTCaB+r0n+J1ahoE0IoUn6GWFjWrn/qtshUda/RpjU9jW1gxknLqFjbjI/VbF+q2SJhfa8Hkl5n+5PExsNrJP2KyA6oqvIL7Wp7i0j2bZIeQGT0VLo2qUF6vodgGZa4WfW8nc+U9GUia2Nt4Mz0N6zHbGX8fnxf0rOr3Lu70XJTqI3H6WLgJKJsBiJivZhYQPXiZOK9v2n6KlPnM1Hb81PSI4jslkcT15t9624ETCK2D1O4G/yNqLN9t+3vVWzbyh++Bd10YOaz33DxOm1C6DB8g7jWvpC4llSh0fU2bb4dTpqX2/4nw/NYz4yAHLHtgVJNrKTnEOIY7ybSF3qqi0r6mO1F6mFy7gH+l/0m8UDPSbykb9p+QSmauOxXVLQoUqQv/5DZ6dPbuE8NpGYLECxHlV20dJ7GEZPSOT4CfNn2ZZKOcLIe6fHcuwlLkWJyWLxmdSalxeJsY2bXuPW8yKYFfCF49bFStPY5wLNtv6lCn+sQi5VOtcqBUURFvdoTiLrrrdO5Tu8XKS61vY5YGFdJie/WfrmI9KAodXrOtU71dwqLpE1t765Iobqgwm7r1cz8f1ci0vPelxYlg8Z8me2+NkoD2l9he6u0yHwMaZFZMcp0ie3HqWZNj6RriVTTkwg15XNUo75/XLSJig2h7/8lFOhPTp+LlxA10T0VfJV0EBRZNsvhGe/UkaAodTmSSMErxIg+a/vdFdqWrzPL0vMHbTJ1OU9dy7AiTfMfhGBN4e18Yq/rimK3YRfCLuzLDkV6FLXf6xbR2wF9FlGxfxJRsVniYqMk3dN/S3icPob42y+t8nlUl/rLbscajmuPfpul6q7zsdyx0u9OJsq2DiNEGWdtBFbcXJ84FFlMaztZk6WN4lcCb7D9iIrneBERwICw0OqW1j1Uut0TJuQ+cTqhk1PYBq5JXLf/fcT9vpcol/mq86Jp3jPVEVvNCEGVuQX4VYXJU7F9/zxiQXulNNAgtVgUNjU537CUCrIn8L3yJJ6wLVgO2y9I39tEE8sy/VBNpn9Yu421IyZdWAK8WdIjgUFpgW1eJwAUdRoHAg8iar0eTwjf9Fxg2v4Z4XfXefy7xE5iFU4khBOeT0QD96C6muEnCB/ndRUiDC8hal6r8Bvis9OUBZLWTtkMpOyCKteff5V+fgaprsVRs1ZFlfkFpZ/vJOoxq763TlNY3XyN2SmuVSdpjb1GidqrexNWWEXKdV9LqEQbGxgkHW/7FYOODZs2UTE1SNnsYH8iWrGppN8SeghVLJlaLWBbZqocBryWiHIXgluf7tsiYXuWjoSkw4BTezy933kaq6i6ordzmkQu5zvvjhIbRT32dj3OMa6oGLTzOP2zpN2Y0V7YlWpaG1U4kEjJnIWae35uS3z2/hMoNk6KG/pA/9xJRNLLiOvtrZJ+TvieH0/MPypdPyR9mHjtTkyHDpS0ve239Wk2DCbVb3h9Zmdq3EGUCg2k5fX2jcTm2J2SbmcON8cy9ZnqiG2KdmxN7LSIECa5CrgvkSbTMzVJofL3QGIRtCUR7Tm7SgRH0oHu8F/sdqxLu2W7sQqV0GNs/0/n7/q0X874vduxLu1WAr7rAQqzo6JhxGRf4NtONZ6SViXSsNYEvmP7kBGP+WrihnRxisptCrzX9i4j7rdQq1xWGyLpHNtPGdBuAbH4/iszAlBnuKLojqTPEWlA36KBsqik3Ym6q0JFeWfggx4s1nUacDpwA5GeumGaIK4KLHEPNdVS+8cD15Z2eNcAHulqtWLdbvR2hQyI1P4AIkp7JbERsT5wgnsoi3a0XZWZhYtJCxc3UK1UHzXlLs+dFZVJ14arbW9Wt9+5QtL3iAh1eZL2ctv9Uja7ZZysSkQSb4X+721JdxLpz8v9iooTnjaZKor03KWErQjEoufetl86qG2Xc61NRBFHrvCpEKB7EpFWfDGxIXmb7UoLgT7n7Zv9oRAJfAiza+WrpjC2QpHCvbHtxSlLZg1XsN5RiPt9kkiBNCGkd+AwsgF6vV6StiQyPt4HlFPWlwJnuaVzgKRH2r62zTnmC5KuIUQUr0tBlIuAl9n+2oCm5XNcBWzlsAAsrreXu2LWWFM0WwcGIpDx3rb/31Ej6Z3EZtHXiM/EjsCXqsz32lxvM5PFVEdsicjF3sWFVNJmxBv7/URksl/Nzd7EBf56hxflfamuDtrIcxP4jaKW8QZiQf6dNO5VCW+0rqilGbzDH+w2SWu5QUF86n9vlreeqJra1iRisr/tz6T+1wZOI/6nRxB1QCNd2AK3O1RCkXQv2z+RtMmI+4SZCObvFaq5vyOixn1xqhNJUY2fNOj31+nrnjRQFnV4Hl9HpKvdDezparZFexOTrGcCuzjV6BKL9G52GJ18mvgsFdzW5VivMbeK7Lud1+hxxGSyaL8r8AXipr4cknazfUKXxVpB3w0ISW8nNh5WlVRE0ETsiB9dcczjYh3b5ffCsZIWVWjXq27rFQyu27q630KqIm0yVTbpSBs8SyHINBD1SM+v0G6WvVFD2ng796PnDr2iTGUX4EfMpMia6rV5jVELj9O0adu3fKkFXV8vz3h+nlgjs6UOx1Ph2jsh3GH7Ogi7GUm/qLOoLXFvYsMZIjV/5HhC/YZtfzAFQorN4T07szf60Ph6K+nJ3Y7P1eZYph7TvrDdtLw7aPtHkh5t+3r1yCqWtKntnzBjifDQXs/t0raXXc+aVEshajqJH4YZ/O3A1Sn6UfYHq3LxO55YLD0njf/lVLRUmenGz1TUXy1wpJkOWlSsnJ5/P0IN9XDbJwConv1EU25QpIp+HfiepJuIReao+YDCA/dNRPrlQqBnPXEHp0v6fzSoE3FLewCFGNuriM0HAUdJOsYDPB4dXqb7djl+FtWsc1T+W9MCv+91T9LTbZ8paaceY6psQ6Ualj0d1F24FEJvjdIu0473IZIOsf32JucYI41SNkvv6dMJS5Qiqn8wM5kFo+TPkjZixsbrJYRIUhUul/R42xento8jylWq0Cg9355tb9QQSdqOuEcUCt+jnofsQHyexiH4siPJ4xTA4cfb9zMq6S1pwb/M4q1MxXvyILpOaiR9OUX9L1eXsokhRBKrTaYmg3U7NhLXKD+umM10CPFan0W8Nk8mLOIyPXBYZQ20y+pCm+ttuXxgFeCxxFy7tkNCZvRM+8L2pwqLk6JGZxfgZwq57n/1aPNGwl+z088WBvtltfLcbDqJd6Q4f1wtzOCJFNNvNWz7MNs7S3qx7eMUarBVa0YBvkJMLMueo6fQfwJ1OCEAtRJwNbHQXZ+Ilv+03vBnkHRfVxBIsr1j+vHgdFNaixRhr9FXZcGrUr+FsMQtQNXoX0HjOhF12AMofI53r5FWtjchmX9rav8RInWr7/tVPYTYCjxAkI2oJTqAmfrD/Yj3TT+eQqivvrBbl1RUFlU7r9FaCxfbR6Xv7614/l7nefs40zYbsheRsnkEMymbdYSQmtRtDWPh27i2F3gcsLukwm5rfeDHRTR2wAJkPTrS81OK6MD0fOBiSdu6gb1RYhEtvJ370G/BdD0RKR3HwraJx2mxKbxkdMPqeS05MH1/QY/ft2Waat+OYfZGYufjgdj+oqSziYwRgLfa/sNwhpfpoI2Wwqy5gKQHE/oXmXnItNfYrkpMZAtf1/OBTxHRydW6RZ0k7VREZFTdKqfzHA8FfudUD5fGcX/bvxzQrtUkXtL+hMLkzenx2sCutj81oN0OhEfh1a6gMtml/aW2HyvpXOL1/gNRs9W3FlFRl/pI4gJR3hFbCLzZg+snC+P3exA7n88hdvLe4KRSWHH8/0cs6k8gPCkH1hNq+drNNQmrlSqTw+IcOxNWGY8klIoHTsY1x9ZIpX5b+RynCfe2pc/EKsTf/KgB7frWDts+Z0D7dYl03qcTn60zgEVpE2mkqIXXqKQfE+mLsxYuRBp3z4WLooZvH5b33q200FOImbyMjrTNChsIE4ta1G2VzvFlYgP1W4SHZU8LHLWo7S2do6sac4H71GGm1N+ti0wGRf39EvdR/C+1/RHxvvwl9e2NOs9Vy9t5wLk2dw8PTklfIXQyzmC2PsDIUzHVzuN0Z9snDzrWo23ZrvCzRNS4kl2hRqi5oT7KytOKpLf3u5akzKDtScJ3bpbOXHdMjea200CRGUgolO9i+8QBTbqdQ8BVg+YvmfEw1QvbJpQvvE0vwpKWED6Ud6TH9ySsSbYd0K7tJL6bPcDl7i+q8SliYXUhISp0mu339+unyzleRURdtyBupmsA73Gqge3T7sVEmtiLmK3KuRT4HydD7LlA0hsIpdE9bX+hwvNrTw41BMErtROcaVwnopb2AGkyvwexeID4vx/r+j60c4ZaeFmn9o0se1LbRguXtAFxHpEmdVfp+V+pOOafAlt4PGmbtRhmyqZC/KWo2zrX1eu2ivbbArsTadBH2X5nn+celH7s6slo+1V1+q5Lj/vEMjG6AW1b2Rupi7czMNDbOU3+PwKsS7xWdbJN9ugx5mF4gw9E4XH6bGLM33VFj9Nu84+qcxI1sCvsaH8q8Ao30NwYcN6LbT9+mOec7/T7n6X518OYKaPYBfg/2/uPeEw/J9wcFgP/W8xjphFJC4nPwAOJa+330+P/BK60/eIK5yjfYxYQpYq/tL3bKMacacdUpyJLeiIhwd6ZVtcvkqgeP9fhHsWiNvV3R1rc9mXQwrUCCyQtqylMO6+D+n0ysKVDQGo1YlJca2Fru7AhOYcasv62vwF8Q9J2riYkNBQUNXX7FJOxFH3dl6hVfgEh0jPwNOWbgSvUbjIcwas2gjNt6kRa2QPY/mhKuSqyJ+qIPiBpY+L16fTvHZQVcByhJHpzerw2UY9dJYK5l+2Pp8nhuoR43GL6i86VaWrZ08ZGZjXbb23YFsabtlmXoaVsumbdlqT3E76xxf/peuLz9B3gXgP6Gndtb5P0fCDel+qi8luj781s/03h7fxtkrczMMju6VDgha6o4t4x5jlZwHaiGY/T7xE6DPcEXinpx+7jcarmljuzTpO+17ErLNNYc0N9ShlWtEVtot/r/hRg89Kc7TiitGrUPJzQctkLOFLSl4iN5p/NQd9zzfHATUTp0z7AW4h58Q62r6h4jvI95k7gi7arahpk5pipXtgCnyOEdWZFLwawqsL8fQGwSvp52YUpTYAG8SdJL7J9KiyLTNZJjW00iSfqWr+sqO0zsVgbVPd5h+270vlvq3nzK8Z7LyKldgNm38yqiOQAXCfpHV3a16mTq8O6pUXt84lJ1Qtt/0zSayqeo8nkcBiCV40FENyuTqTscyyq+Rx39t9U9AFiQXkQsQnwtNR3lffqFp4RYcP2TekzXYW2k8PGXqMt+Kak59n+dsP2twFXKOzG5jRtsy62T0s/3uYuKZsj7v7Ftt+d+tqA2KR6r+1TJFWtP23sydiSfYn0/Hcxk57/6ioN1ULlN9HU2/nGuotazVZ/Jv38Z6Km9zA3sM6q0Xcbj9PfERPpFzGzIQaROVJVKPCytHGyIfB2RalMFd/vgkaaGxqjAvU8pt/7+6fEdaDYIHswFfRYWg8oFtLFhsvTiM/wfgqBwrfNZaBhDnioU8qwpM8S14D1iw3FKoxrcyzTjKlORS5SAWu26SdkYdsDo1tp4XEioVIs4DeE0M51FcdwPjOT+BeSJvG2DxrQbgERdSx8Sk8nogo9F/WSbgOKcQnYKD2uXDsl6TuEmFFn+mM3Aa5u7RunT0paqd/f16PNJUSt9YMJyftH2/5tSlm52NVqbGvXbkraB/gAM4JXX0jt9iC87HrW5ZXO8VBCAOEJxC7kL4DdPKB+u8e5JqZORDP+vVeXblLneYAnbLpRP9XJn0/SfYBzqvzNauFlndoPzWu0KpKWEgJh/yQE8iqnbKb2Y03bbEKblM0WfV4LPJeYlH4OeK1DSVtEunlffYB0jta1vXONpCtIKr+eKYWolMacntvI21nSx4F/IzYEyxsuPYXc1D1t+j7E9XZ12/tUGXMTNByP05Vt9xK5HNS2SJe83uH7fV/ggbZHumjSBJUyzBXqUw6mKC3alhlRwW2J98ptUEkcsemY7ktkXb0CuJG4hp1KvGdOdkuru/lE572gyb2hRbApMwamfWH7YWIy+lVm3wybRo3q9r8G8RpX3hlK7RpN4huOsbEISekcrUyu1aXmq0bbXxAKyott/6him4cBbyMiJP9HTFDPBV5MTLKOaDKWin0PS/BqmTVSjTa160Qkfcz2IvUQNhvVjbfLOC4gIp+nEIrFvwU+bLuvd7Ck3Ql/1iKitzPwQdvH9261rG2ryaFa1iWPC0Xt9/q2G6uLzwWllM2XAl8q/WohkfL62BH2/UJi4/EOIiX6GmKTajfic1mpTlYta3uboBbp+ZoRCvyh7a3TdeiiqgvbHue8hwfYDaVNpk7cNKun32JjGHSZTP/E9qY1z9F4Mq2WvptN+1Z4jO7smnZw04ykd9j+UI/ftdJVaTGmnxEZBItt39Dxu7fa/sgo+h0Hku5iJp1ehFjfbdTY9G0abMqMh2lPRS6itduUjg2y7GmNpPd0PI6Oq6fm3p4m1T+X9DpiEr9uhX5/QffFR8+bUZWFazr3Rba36/HrCyU9ynbT2pA26ZNbECqun02v2ecJ4ameSpspcr5s4inpTKLe5K22v9+vM7UUrClFl+8i7HcqoeWVVIvjxXmreOY1qRMpFoCHVTj/KFlEWOccQNSAP40Q6+mL7S9Iuo64BtxN1PZWTbPaPn3fol4G8jLaeI3WQsl/Oy2UlqPqZl5asB1G1CBtKGkr4H1ztYFRk2GkbDbCkQZ9GizLfHg9EYm8HPhgjfO0Sc9vSpv0/C9LOgq4d8pA2YuKdeMFauDtbLtW2UMFFgz5fJ0Mw+O0afkFtPfdbNr3xJQyDAsNUKLvtahNvxvJwrUfaXP9m+4hEjpNi1oA2ysNftZAVrV9hiSlOfPBks4jPiOZecZUL2xt1/X5HBZlP9ZVCEGiOvVBi2gwiWf2An4VIjp1nxr99mOVPr/bnhDF+AVxM6trAXEg8A5JtdMnU8TyGOCYtEv9ReAISacA73eF9O8UJakaKZkLj8FulP3xXkPUb9Xl3g7P42VIOrDzWBnbxYJhq25tCcGwuWADh3fm30m1vYo6yr72SmmMr2KmNvgoSce4mt9z28lhG6/RurTx3y5zMPF3ng1g+wpJ8zItzfaVwJWSTmqasjmkcZgoSxho4TJPWCBpbc9Oz680F7B9mELl929Ene17XFHlN/XVyNtZ0oMIz+snkmxRiKjzDX3adNvkWZuIqo+65rO1xyktJtNu77vZtO9Tme1wsCLwDaKU6vtU13IBlpWOFBvk9yRq1m+tMvdpikModF5nDc1DGgWbMuNhKlORJe1m+4ReUa6Ku6XDHM+9gFNtP6fi8xv713U51/m2tx/8zIHn6SdZ38oCog1p9/H5xGJnAyLCeCKR3vch2w+fgzEMzY+xYn+N0ui6/Q+rnqtN22HQo/+BtTKSrgK2s31retw4dbKYHNreteLzW6f5zzWasSha9r9VjRrKcdAmZXMIfT+c2ADpVIEdaVZQG1qm53/EHarb3Y71ad/I21mhznsSs1XZX277WX3adOplmLDcOhs4epybIQXq43GqhuUXPc5VS0+hTd8K9efivvvT+fA6jxK1KKXqcq4dgMfafscwztenn8MJf+WTma163bNmfUVGYef2Y+DeRLBpITEX6LuxnhkP0xqxXT19r7tDuoweu723AL/ygHqgLqxGDRsc4O0sb/vQ7dgsOsa8gIjgNn4NquKwgNiSmVqx81I0pS9DSp/8OaFy+V+e7Xt7Sq86o2GgLn6Mkgb6MQ6JWrtRknYF/oNILS3vpq9JTPRG0nYYqL31hZi9i34X1dP5OrkBqFxLPo6Fa2mjZwNmL7SqbuZdI+k/gJXSgvEAwuN6PtMmZbMtJwOfISJytaI146Jlev6ziJTrMs/tcqwX/0jfb5P0AOIaUiUjYB3b5TrbYyUt6tegasaWpD08PnG0nelt87aI2ZlbTyeErwai7noKA+/JffqulDUm6anAccAvic/gg9PrO82qyG2V6Jdh++uS3jaMQQ3gPsRnr7wBZyKzKbM8jTLGMuNhKhe2to9K39/b4jSfArYmpNdFTGivAu4raV/bPb0sNdtmYCVgHQbUEKV2bSfx5TTEOwnF3GEpsPacKKZ0z32YuSieIOnoCumew0if3N32+R3jeaLtC0Zc19PUj7GI4LexR6rLhYQl0P2Y/VovZbC1QJu2w6BtHeVi4BJJhRrpDoQC5ECGMDkcB6eRPCipZ+9R8HrgnURJwRcJC7FavtZjYJz1T3faHrWF01Bpkp4v6bWEpdlDUxZEwZrUqxtv6u38Z0m7Ee9JCIXxYW2sHUgsxsZBz/tqmkgXGUEHuJ4IZVvfTRPR8YcQ6bEQmzeDMjcOB57tJDyXMhq+CFRSkp9QilKqO4hSKqguSrRT6WERjBhpGmXa/Pyz7TcPfHKmoFGwKTMepjIVuUADivoHtP0fokbz2vR4MyLl7P1E6tRWfdqWUxDvJDz4Bi5MU9RzK2IRXBagWgqcVdRE9Wi7gFAj/FKv57RB0ua2r+nxu6GlezYYV2OrD0lPJGoKizTCora3iurktcT/6iTCj/EcVVS8VQN7pI7Nkocx26KpUr2mwirod07+jQr12/u7gVXQXKMKyql92m5N1IGLGsqzmm19cyehID2vTdnne9rwKBhmymaDvg8G/khY9pTFcv466r6b0uR6LWktoj71EEJRvmBpnb81XXMKb2eTvJ09wFNW0vrAJ4HtUrsLiRrb1lkRc1lS0aXvfiU+2xAbc0XW1S3AXp7RPeh33gNdU0+h47k/JeY7szbIBr3e3a4/K+I1qSqarfZ9JxHpPsZ9bAOH1O8Ztp8xyj6mAY1ReT/TnGlf2LbxR12ubqI41qumQiHC0ZOqE4Cmk3hJ59pulH6bdg4/QhTEC2pJoV8NbFtaMK0C/KBqPU9qsznL18h9oc/ztyO8XBcRKYgFC4EdKy4wf0JE/TrfHwMjAWrox5ja1rZH0nBsmZYAT7B9R3p8T+AC29v2aXO+7e01W+QCarw/2iDpy7Zf2rGwX0aeMM1G0keAM/pllAxovw1Rf7kBszcD5+3r3KX+aS2i/uniOej7F10OV9ocGxdtrtdpgbkctn/d7XiX9nPu7VxhTCP1PB7Qd89FddqA2N/2eenx9sCnKm5ittJEUENtDkmfZybaC/By4B4evqr1vELSi4Bi7nW27W+OczyDUK6xrUSbYFNmfExlKnKJ1VxR1KILP5X0aeB/0uNdgJ+lNNJeYgiXERd1EQudm9LP9wZ+zYBaomIST9iENJnEf0/SfxI7S+WLVZUF9aHAC23XUW8uKKd7ivCDrZTuCSDpIOCpxML220TN1vlAz4UtoSC4BvEeLtcR/41Q2qzCLbb/t+o4O/hv28vSxRXKt1VVuGvbI1WNTKi/LdM9ikVtOucdaXHbr9/t0/eR12r34MD0/QVz1aGitvSdwF+BjxIpeE8iPI9fVaQIzlMuBr6WMjhqKYwnTqRLpGY+0zJls23f81IxegCN0/OBbzFzj1uFuKf9lLDvqcImHZuOZ0nqmd6vlvZqFZmreuxu9EtlXFosagFsn582GHui4WkiHCTps4Q3czkTYdDC57XA/kRtrgj16U/V6HfikPRhYFvi2glwoKTtbQ+slVUDte8hkWtsK+AuyvsK3+8H50Xt/GXaI7YfAC50g6L+lDK1HzMpjOcTF+jbiQVzTwNyhaXBqUW/KZ3hmbbfNKDP9Wz/vld0rkIaUOPogaQLbD9x0PP6tG+U7pnaXg1sCVxue0tJ9wc+6w7Lgh5tH9I0HS3dkFYiLublm/dA0ar0Wp9MGJzX2gyQ9CMinbipPVK/c/eLAHwPONL2qenxi4mFwMCUJEnH237FoGOjQFET9F3bzxx1X6m/YlNlIRHRX0TUrj4J+IDtx/VuPV4kXU8sVK52g4t700jNOGmTsjmEvlcmJvPLojXAUZ7nSrBtrtddzvMa26+p+Pxjgc94trfzHrb36/H8F9o+raMsYBkeguiTpE/afl3b8/Q4d5tyqCMIAacvEouOXYjN8q+kcyx3n0pzhw3pkjJOqCJXygSTdAKwKXAtMxtcrjLuFY0UWd/K9t3p8UrEXKZKZL222ndm7pF0NqHzcQ/gCuBPwDm2uzqvZMbLtC9slxIKybX9UVv2e5ntx3QcW2J7m15tSs9rPImXtIo7apW6HevR9uPAvwFfp94OLZI2Am6w/U+FKuIWwBds31xx3Jfafqyky4io51LgGts9owCSPmZ7kaTT6L6T/6IK/XbaQaSmg606JK0JvIxQyFsAfB74H1ew/Gm6cVGFfml16f90IvBA4jW7gRDfGuj123leSfcgJkqbtR1zFVL04RW2b5mDvpaVGki6zvbDuv1uPiLpu8Bzi0lWg/bPINJD60ZqxkablM0h9P1ZQlynWGC9ArjL9qtG3fd8oU4qr6QfE/63s7ydicVTz809tbDAU4hlLSbuK58FHg28zQ3T9eugduVQ3e5PpVOMzlJK0tWuV0q0wpaMpOvPU4vMOEVJ2tkVF7Y9S95GMdZSH6sAexOZFuXyr7xx0YUiYCDpVUS09iDl2vF5y1SnIrdJn9TywkLFOavUTv1Z0ruIOiITu3CV0oAc5tm3SVqrwST+QkLJedCxbiwEbgPKfoJVU1O+Amwj6WHExOE0YhfyeRXaAixRKGUeQ0wA/g5cOqBNscN5WMU+lsMV7SB6tF1KjPcYha3QF4EjJJ1CiI71XCy6oT1SW2z/H/B4hXekXCFlU9LbiZrLVSUVi3YBdwBHj2ywy3M7cHXa4S6n2Y9C+bq8KOzcqJjv6bm/B86W9L/MXphWtfvZk4jUrEwpUsP8TlGrnbI5RLb17NTaM/ul1k46mu0Nv4C4t/ypxin+vWHXbVRJ97L9cUnPIRwK9iQWuiNf2NKiHKrN/UnS44kU10cQZTsrAbfW2NS/WNJmtn9U8flzXjIyjziEKB87i7g3Ppl4b1ZhlGrf/Tge+AnwHKJ+9OXEBlOmO/eQtB4hIvXOcQ8m05+pXtjCsnz4jZm9K1XFU+1zdBEWqsiuhNVEUcN0bjpWlVqTeEn/RkThVpX0aGZqhhYSqUwDcTtxh7tt36kQoPq47SMlVU5tK6WhfUahGLzQdl8rmSLN0PY5TQetUPo8iJk0wnOA91XZUNCMX+ieRJrZ4UQ09ElEnfDD+7Rtao9UhX62TPcHPgQ8wPZzFUrf29nuWV9n+xDgEEmH2K56sx4F30pfc8GmaRdewEaasTcR9fyox8Ev0tc901ddtqwTqZknXCrpKGanbJ6d0mSr+mE35S5JG6VNIxTK4xPhZ9uQ8mbxncRncmD0saBuVoraW+DBzDXxeUTpyJWS5qqutrHHqaT3dDvuarZwnyQyik4mLGR2J8pfqrI9sEcquRlYLmP79+nH/ToX8gpBu6ZaJ/Me219MqaqFCONbbf+hYvO9iP/VEcyofc9F1PRhtneW9GLbx0k6ibB2y3TnfcTrc4HtH6Tr/M/HPKZMD6Y9FflVxE7ig4i8+McTtgZVUk0vcctaOkkLiUVfz3rcHu1q1ROl57+SuIH9gJkb+VLg2IrpxI1FDCRdAnyM2Ml6oe1fqILybzHx7EWVCWmXyHody56vANcwO41wS9s79W61rO31wFnA52xf2PG7T/SLJGqE9kjqb8v0v0Sk4p2OWuZ7ELVAVdRQdwTOLBb9KcL+VNtfbzvm+UavVPGCYaSMz1ckHQMcUSNSM3bGnLL5DOIzdT1x7XkIsKftfmOaeNK9zVWyPlr201qVVGGp8kCi9nRLInp5tjvKhUaBZsqhmnicljU5ViGioT+uki6qVPpUTpeUdKHtJ1Qcd1Odj25qzFOfspk29bcnzZ1sf21Ak7GimfKvcwktmT8Al1bMSMxk5jXTvrC9mthFu9hh07Mp8F7bu1Ro20ZY6FGE+Exh//NnQiCj64JjWEj6f1Vqd3q0bSxikCJ/+xKLsy9K2hDYxfaHB7S7mxCnKFLZyrvolSakamfZ07i+RdIadTcsSm3b2G20sWX6ge1tVRKYqvH3dnutlp1n1CiUig9heUuosd2I1V+Bek5Rj1rzAleoOU/n+TGwESMQNptWFEr5mxCv1U9s/3NAk4lFYxLqkrSyGwpyKdSytwKut32zpPsCDxyUFTTfSO+zU20/p8JzzwWeSZQG/YEoUXilK9jgNRzba4kF0kMJ9fiCNYko126j6Hc+IOlTRDS8SCfeBfg/2/tXaHscEUC4OT1eGzi8yuZFG1LQ5yvAo4BjCYeJ99j+zCj7nVQkPRz4NHB/25tL2gJ4ke0PjHlomS5Meyry7bZvl4Ske9n+iaRNKrYtorVlwSczWx69F0cBbyx27RWCSkcTvqsDaTGJf1DaSS/qP7emukjGOrbLZuHHSlpUYawrAe8o37hs/wLou6hNvAn4f8A/CFulrzVYLLax7PmHQpb/fFgW/f1HxbarKrxsN6Cm2iXt7Dba2DLdmiZ1hmV1WFXruBd0OTaX14/FRNr4EYTA2J70SbueI1YZ/JQ5o6g134kQgSv7hP6yxnma1kCOjZYpm037fLrtM9NGU5mNJM1rsa2WfJ5INy0LdS0mBANHyQaSmm5sFSrfW8xdBvIMGp7H6WpUL4V4BXHNfh2x8ftg4towKk4C/pcuasyuZjc4yTwF2NwpSpQWq1Wt/LZwSWTT9k2KcrKRYvuz6cdzmf/lNfOBYwgbvKMAbF+V0rfzwnYeMu0L2xtSyuTXCY/Xm4DfVWnoFsINwOrlVDTbZ6d006o0ncSXRTLWpZ5IRiMRA4fY1TqS7umSR2oVbB9BCC5tmPo7Q9KvgA/ZvqLiac6S9F80iKwTNh3HKWptRfiWvrJiv98g1C6/T82aOtsfTTU5hd3Gnq5ut3Fjw0UtwBuBU4nJ9wWEkMrOFdsukfRR4L+JhfHriSj5XLGq7TMkKaXDHSzpPOJzMi7mTbqLU625pPfbfnLpV6el6E3V8/wqbVbdn8m5P9xa+nlZyuaI+3wKcCbQzZJsvotttWFcQl1tNrbeXPp5FeCxxLVrZCnqBWrncVpWGF6JuF5X3azZwfbHCb2O96bzHQh8vMbwK5NKVG4haYlIWpd4rddI2U2/7td+wvkpoe5dpGk/GKiaDbBA0tpFSr1CUXlk112F+Nst7tDVkPR6YCXbHxtV3xPOarYv7dgYq1rjn5ljpjoVuYykpwBrAd/ptwCTtJvtEzRb/XEZrqAumiJxP2R2Wu82tneoONbLbD9GJcl9SefZftKAdlfZ3kJh3XO27a9VTReVtD4hYrAdMyIGBw6qqUltjyKiw6cyW+yqqhIrkh5JiF28AniL7S9XbNfYsqd0joWp0UCrnlKb2pL8khba/lu6eS1HlZ1ttbNluhexCC/SJn8KLKiSOpk2Zt5NpLeJ2Cz5gFOd8KhJC/EnAacQC4rfAh+2XTUDYxRjqmxzMlekVOLn274+Pd4Q+LbtR1Rs/3piAXEjs/0rJyYVuU7K5hD62jBlqPQ9Ni2ogbfqkPptdE/sca4HA4fariPo2Ai18zgt17neSWxqVvWh7VbrOvLSEUkvBD4KPAD4I1Fz/mP3se6bdCSdQ2xeFE4O2wIXES4TfctAJO1OKCifQnyeXgp80Pbxvdq0HOs1wNadc+B0zfzBJF3n5xKFPsnrgJNtby3pJcDetp875qFlujApO/KNSalSG9terDBLfyBRP9aLIrLa2CqIULV7LzO79ucSO8xVuT3VBf1c0uuISfy6FdpdJul0QiTj7Qqv1Ur2JGlHtVIdXhd+l74WUON1UyjLvQx4MfAbIh35g67gu1vQJLLea/Oi2I2ruCBvonZ5EhFNuozZ0T6lx1VSgtrYMl2UJjvXLutY+iEV7KDSAnZglGGELCIm1AcA7yeiNruPcTww/lTobryBUAS+Pj3eAHhNjfYHApu4Qo36PKZOymZbvsLyn59TgJELE42JrdL3zkyJJ1C9VKcJTe+J3bgB6CtsOGTuTWQDQWyuV2U94FongS5Ja0h6pO1LejWQtCvwH8CGCu/vgoXMjY3MBwiRzu87fD+fRj1HiEmkaylEFWx/QdIS4nMjYCePVrjP3QI7tv+pceTpTw77E+WEm0r6LbGGePl4h5TpxVQvbCUdRNTIbkKkMq1M1J49sVcb20UO/Xsb9rkSsavzzCbtE4toNonfmxmRjNtSPWXfBbWkt9g+VNKRdDdWH+gTWrxWklavGcG7jkjZ+QbhF7o+sF+dBWbD+rp+mxd9UxhS2p2Jm9A7JP2TULscKOJk+wXp+4b9+uiHG9gyaQh2UGlT6C0sb+g+8nS+oisiA+IhxOcYou5lnDvMrxhj312x/R1Fjf6m6dBPiIl1VX5D9brreUHLlM2mfW5KfBbW0uw624XMr9rrodKyRKcNi5h9T3w60NU9oJOOe1shJDVXXsNtPE4/zexNk9u6HOvkQkIo6n6EBV3BUqqnx7bhX7b/ImmBpAW2z1LY/UwtbmE5mNr/CJgzFXpJ97d9Y+exuep/EkkZUM9MmWsLCC2WXZhJP8/MI6Z6YQvsCDyaSAvG9u9SFHMgKYXv9SwvDtQ3qplqTm+TtJYr+KH2Og0NJvG275Z0I7CZwsqlCkUt2pImAwWQtB0hfrQGsL7CouE1nvGn7cX7mJlwrNGw+9r1dcXmBbGrfEH5dwoBqX5t20Tyiz7OsP2MQcd6tG1iy/Qconb4QUSaWMFS4B0Vh30i8CXi9d2XmFT+qW+L4XIiUSt3NRWzENqiAQrUHrHKeVPS7vsvCWG2w4FHEBsbVbieiPh+i9mp7pXLCsbAC0o/10rZbMEmqd97M7vOdinhUT2VKDQrdmf5++LADdA22P5B6n8BcIDr2QyV7213Al/svO6PCrfzOJVLtWLp/t73vp5Kh35FlBSRNrefDPx9Dj4TADdLWoPIUjtR0h+Z8lrE0mY3hHf4ysCt/Ta5x8h/Ad9SWEkVZQOPIUQpD+vZagUllantT9w/v0FoquwP/CexOXZi79aZcTHtC9s7bFtSoVZXR8Dp68Ri7TTqT6RvB65WWOiUa06r3vwbTeLTzuguxO5fIWhk4ibTFdunpR9vs31yx/mqCgt9jFg8nZrOeaWkJ/dtEc87uOL5+52jvCuNpMOKcVTgSJbf/e52rHz+dYnF4MOIHfAPV63NVdj6rAbcTyHrX46cPqDimBcTKc3F/2a3dKynLZPD//g4tbCDAu5r+3OSDkw71Oek2qK54k+2q/5fh0UbBeo5R9KqRDnBfxDv4TUJxe3K4lHAr9PXPdPXJFA7ZbMttr8BfEPSdrYvGlU/85BvAxczhxtMsLzNkKQ6NkP3dggplc93YOexEbIdMx6nKwFVPU6vVyjvfzo93o/YeOqJpG8STgjXSFqPWLwsIQQDj/boxYFeTMx/3kCkaq7FiLMnxk3nZrekHQiBsnlHSn3+E/E/2Zx4T14LHOTm7hLTzPGEhsBFxIblW4j74g6uLnCamWOmWjxK0n8CGxOT/kOI2teTbB9Zoe0lth836Hk92nZNkUoLjCrtz7e9/eBnLtfup4R8fG0fxR5iE5XEcYrXSrP9Ua/0iDzzBoxlbcJofOM+z9mOqAlbRKhsFiwEduw3bknfIWpkzyUiNmvafmXFsR2Y+nwAUSNWLGz/Bhxj+5MVzlHbe7dUU/wmuqebV0n5vtj24yV9F/gEUVN9iu2NBrUdBpKeQVLOpqZoVos+L7DdN4I/X5B0IhGZOZ2oVT8TuK5N2vukIOlyQhCl2MBcACypcu0aQt+rECUgnSn6I/WhHBdV7wkj6PcqYH/Pthn6lKuJMI1FSCn108bjdF3iWvt04rp9BrDI9h/7tLnWSahJ0juATW3vnjLVLqjyemXaU9wvxz2Opkh6u+1Dxj2OcaPZYnUrAX8G1q+ZMZKZY6Y6Ymv7MEnPIhYOmxAG1N+r2PzjqUb3dGrYyKTdunWAq21/t9HA4SBJn6X+JP56Ig2m8sJW0nOB5wEPlPSJ0q8WUj2F6DeSngBY0j2JOqg5iXI1rK+7J5H6fA9m19n+DXjJgLb/Zvud6efvKsSXKpEiBB+X9Poqmys9aGLLVGQqNE33BviAwhbpTURUeyGxKz9X7EnUja5MSa2X0dqqLJH0JRooUI+BzYmd5R8DP0klEZV3LSV9zPYiSafRffOjqbDcXFA7ZXOIHE/UMT+HuO68nDm69o2J4yXtA3yT2Z+JUXuV1rYZUm8hpTWZGyElaOFxmhawL6vZ379KPz+DKGHC9lJJI4+wDyrfmEY0u8Z+AaHrMukRo52JYNCKzrLPU7qn/iIvauc/U72wBUgL2aqL2TKPIsRhns7siXRPsZy0O/tIQsDh/ZIea/v9DfpuOom/DbhCUueCuF8K9O+IVKUXMduXdCnVFy77Ev54DyQUJ08n6hDmgtr1daVU2mNdwc6oA3WkEa9Uflxlgmf7SEmbA5sxO8rzhQr970XYMh3BjC1T3+iQhyOItrHtbxLCQuMQkNmy2DmdQ9ooUM8ptrdUCBr9B/D9VNu2pqR/q1jTV9hLTGKdVe2UzSHyMNs7S3qx7eMknQQ03dCcBO4g6vTeyczkvaqiexsuVdjKlW2Gzpa0NfTccB63kBK08DhNi+ADbd+cHq8NHD4gG+A3CsuuG4hyhO+ktqsyo9cxSiaqfGNIlGvs7wR+SaRkTzJZITnYUlJRaiZCgPNvrAAbNpPMVKYidxTzz/oVFd+Mkn5CpPX29Lzt0uYaYgJ+l6TVgPNs17Z9KKc/1GzXOAVa0sq2/zXoeV3a7UCkWjWOUCs81P4fywuSVKrNSTf8B3e0HRhJVQOlX4Uoz910v/Db9sAJXsoEeCqxsP028FzgfNuDosWtSH/vPiz/Og9Mm5R0lseniIqkY4AjPForhKkh1STuSuy832D7CWMe0shokrI5xL4vtf1YSecSC+o/EKUQc2U3NKdI+j/gcbb/PMf9dvMrL3C/a/Y4UTuP0+XSpQelUKfPwvuIuvP/tn16Ov404DG2R7pxNUnlG5nejKvkIJMZBlMZsfUQlGsJxbN7EybjVbnD9l1pDLdJjX3BLpa0Wd1JfIoY3BN4eDr00xqL1Q0kHcLyUcSeE7QhRqi/QUQCL6NGGnUaw/sJxd//Y3YEocpEp7bSr+0N6oyvBy8BtgQut72nQmr/s/0aaAi2TMTrfB6h7HfXgOd2cqGkTxKvV1kQrXIqdku2B/aQ9AviPVJsUo2sZkzNFKjnBbaXEKnU/0nU3lZCYRVU6zowbhqmbA6Lo9PG2rsI0bo1aOFrOQFcS1qUzSVtNtUkPZ74HD+CKENZiblTrW3zXlggaW3bNwFIug8D5mzps7Bvl+NnAf02B4bFJJVvDIVJvk/0IUdsMxPLVC5sJW0L3M8dKm+SXgj8ztWUFO8P/ETSD5h9ge5Xa7ZpErmAuDBslB7XnYQ3msRLeipwHJEKI+DBkvawXUUVdTFwEJHi+jQiHXrQxe3JdESoCY/BujzI9r83aAfwUmCjOpH1EuNS+v1HqgO8UyEn/0cGp/K1tmUCVrP91oZti4hfOYpedQNhGDR9f7ShtgL1fCPV9tV5Tze5DoyVhimbQ8F2sSF1LqNPx50P3EWUu5xF9XKX1qiZX3nBJ4mNj5OJ+sfdiSyjkeN2HqeHAxdJKtwKdgY+2K9Brxr50nhGXSs/MeUbQ2Ti7xNdOHnwUzKZ+clULmyJGqBXdjn+Y+Boqk3GD2rQ7yMatOlG00n84cCzbf8UQNLDiZqkKunQq9o+Q5JS3enBks6j/+swrAj1hZIeZbuSqEYH11A/sl5QRLN/L+n5RL3xgxqcpy5LFH6QxxBR6r8zk6rWFQ/Hlumbkp5n+9s1xwuw81ynH5ZpUAs9DNaxvbj0+FhJi8YwjrmkyXVg3GxRLGoBbN8kaeSKtwCSPgQc2rGofpPtd81F/2Pg6+lrrqntV17G9nWSVkr3q8WSLhz2ALuhFh6nDmuW64jF+N3Anh5sLTXWGnnbe46z/zExcfeJQWVJtj80npFlMu2Z1oXtfW3/svNgurndd1BjhV3Ef9vevE6nVSffki6yvV3b83Rh5WJRm87zM0lVBSNuT3/3zyW9jrCjWXdAm2FGqF/ZMM30EODyVN9cNbJe0E3pd1HFMTciLf4PSRPhzyjsgxbaripm8naW303tdqzcZzG5EvAOSf8kFvUDa85TlsPngX8pVDVfantOJoXzgCYK1JNOk+vAuKmdsjlEnmv7HcWDtKh+HpGaPHW0LHdp028bv/Lb0pivkHQoIShVx9O+MZ1lUarhcaqwh3sVEe0UcJSkY9xHUb9lhLgxQyqVmVQm8T7Rpiwpk5nXTOvCdtU+vxt4Q0tpoldKWt/2r4c4roJVBj+lEUskfY4ZhdPdmK103I9FwGqEVc/7iah2VzGqEsOKUD+3RdvjCHuBq5lRkK7KTbZvoaT0K6my8IXCS3Fj24vTDugatn/Rr41tS/o6KYrebQOmR1+NbZla1px/EHiS7Z9IehyhevmUFuebJGorUM8XJH0E+LLtyyQdYbuqwvkiZl8Hnkakbs5naqdsDpGVJN3LyTtcoT57rznqe85pWe4yTFajeur3KwgbltcRSv8PBnbq22JE2P66pLdVfPrewONt3wrLPtMXEZuwfRlDrfwwSmUmlW73ifkeuW5TlpTJzGumdWH7fUkfBN6VaswAkPRe4MyK51gPuFbSpcwWyxlGjcqopKhfS9jsHEBMOs5hxgKj/4DsH8CyaPUBruDVNcwItaQtgSelQ+fZvrLKuYE/2/7E4Kd15UjCEmHQseVQKBtvQ/gjLyZSzE4gBCQGcbGkbYvXvCKtbZkk7QicmRbzpHTop9r+ep9md9r+CYDtSyQNQ5htIkibWvPZv7UfS4A3S3okUOd9tkF6X/6dNDlLqe6XDH+Iw6FhyuawOAE4Q9Ji4rq+F7Hwm1balLs0Rs38ygt2cHiI3w68N53vQMKibqSoncepmB1Nu4vq9e5zWis/pFKZSeX9wB4dGSOHMb83QduUJWUy85pptftZnVCZfSxwRTq8JTHZe5Xtv1c4R9eo1DBSfTRkKfUULVzHHSrKCq/UG233VfpNz92GuBkWC5dbgL1cTWhr0Lkvd3+LggOJeo9CYGJH4Oh+KVelth8lUpBPZXYqck+1XknbEWJIi4gbf8FCYEfbW1bo9wrg0cAPi79N0lVV0qcl/YhI5fsVsWlSOfVaDW2ZijHb3qrj2KD/zQ3AR0uH3lh+bPujyzWacCYxrU7SvsC3iwyTFDk8mfg8f8f2IRXPs9y1adjXq2HTJWVzB6BvyuaQ+38u8IzU9+luaHs2CXS7xlW97rXs9yGlh5X8ykttu72n+173hkXa8CgoPE6PcQUrKklvJLKmvpYO7QAca/tjFdpeZvsxKlkHSjrP9pMGtW3DJF4/2tLtvTRX76+mpPKk1Qlf6mI+0bcsKZOZFKYyYptSd3aV9FDCjgbgWtvX1zjHKGtVhr1zeiTdI7MPBN4B/EeFc3we2M/2ebAszXYxMIwJy6Ddk70Jb8TaKVfE4hLg8R399RMIuydhy3EPZhbyAH8jrHiqcEdKK3Yac52arTap17VtmUos6HJs0DXgGGa/Rp2Pp5FJTKvb3/ZnYJmA0WnEQu8IItrad2HbJtV9HtA4ZXMYONT3/3fgE6eDNuUubViPuIcvBZC0hqRH2u6ZSSBpV+Let6Gkcj3uQuaoBtItxJRsf1TS2YQGhYhMhMsrNp/TWvkJv360ZZw1/o1oWZ6Uycxr5vWHry1pIVt5MVtGo/W+e8UQzlHmUd0W4ra/K+nwbg26sLRY1Ka256ddvbmgccqVG/gb2j5H0vnE6/beuu0TX5Z0FHBvSfsQaUfHVOz/VwCS1qV+vXWbFLMlKcL938Ti//UMmJS2eH0mlglNq1s5ba7cj1CtPdz2CQAKK65BtE51HyNtUjbbddxC9XZCaVzu0pJPM7tE5LYuxzq5kBCKuh+RQl2wFKgq1tcKtfQ4TZlHTbzCFzG3tfKTfP1oy+GEs8MpxP/4pcxdjX9jJL2IGY/zs21/c5zjyWSGxVQvbFvSzftu4yoNU13NR4gdUtGhPmv7miGPtZ/ycVVV5EvTQu2LxMV5F+BsSVtD/9TeCgyaZC4GLpFUTrn6XKUTR53o7iwvW983XdThvXufKn109Hcv2/+0fZikZxFR3k2A99j+XsVzvIi4GT6AsCl6CBElfGS/dok2diyvB94NfCk9Pp0pVW8dErUVqMfI4cQm3kqEkNrKktYnUhl/2q8hgO0rFcriz7Y9aTWija8fbemMfKiG6u0k0VHu8tH0VZS7LAQGlru0HUJZL8Mh8Nh3/pKuj78CtgNQOCI8Gfh71TTmITAuj9M5rZVPmhhXSjqpKJVJmSMPLiKZ00qq8V9CZIkJ2KmzLGy+IenDwLbAienQgZK2t11V2CyTmbdMZY3tMJC0xPY25fohSRfafkKFttcBL7Rd2WevDZK+RdgTfbvj+HMJIaiBqa+Szurza9uu4v3b69ybD1rMpwV0kXJ1btWUK4Uf4cV0qCJXmZynaPbGxEKlLBDW00y+qBeSdLztRpF3SVcSN8Hv2360pKcBu9p+dYW2FxAiW6cQQmi/BT5se5MB7VYCvmv7mU3GvCJRSqt7KTObABAT+M1sz8uFS/ofQ2zwHAI8h4j2vMEVPYgV9lMvsn3HaEY5GppeP0Y0lottP37wMycHSf8DfLozM0jScwjhnCrlLm36/ypwNjPR4f2Ap9neoU+bbwJvs32NpPWIz8ISYCNCw+FjoxxzGkM3XYPljo2g37HUuqbU6RcR16AriA2Pc2y/cZT9ZuqhsGXcyvbd6fFKwOWjrpXPZOaCqY7YStoIuMH2PxU2BVsAX3B4iA6ijffdjXO1qE28gVC5eykzaUDbEDvVL6hygiYpvQVNI9SSFtr+W4qc/jJ9Fb+7j+2/Vuh+lRY3zfsQtVblRbuZEbHqxj0l7QE8QbMVL6Nxn0VxiX/Z/oukBZIW2D4r1QVWYRH1bZmKCPVtktZyUkXO9GQi0+psF+m4dxEiX034FXBBqkksb/bMa5GwFimbrVA71dtJYhjlLm3YF/gEkWFi4Axg0EbghqV7z57A92zvrlB2vwD42IjGWmZOPU7nQa3rWume/ipgse2DNON1n5lf3Bso5lhrjXEcmcxQmeqFLfAVYBtJDyNS004l0oKe16uBpHukNKVu3nf/r2K/SyR9iah1Kyv1Vln01Mb2zyQ9ihDK2DwdPgd4je3bq5xD0nt6nLuKpcKhNItQn0QsvC9j9mRQ6XEVQaTjU43rN5n9Wg9cFDcU9tgXeDlxU3hh5ynpvyguuFnSGoRB+omS/kjFSYcb2DKVuB24WtL3mL1oGajyK+n+wIeAB9h+rqTNgO1sz0nK51zSLa1uBeJ36WsB0y8SNgzK14BC9fbF4xnKSBlGuUtjHCrCL6vZrPzZfQZJA8H2Ukl1Pc+bMtcep+PelLtHio6/FHjnHPSXacYhwOUpU09Eiv7bxzukTGY4THUqcilt9M3A7baP1GB7k2XpOpKOtP36Bv0u7nLYtsfqa6Y+frKS3lR6uAqx4PxxlTFLusB2Ff/WoSNpf0Ko4WZmFsd2BZXgNsIekvZuuqhLIj+3EzeUlxO7pSfaHriTrxa2TCnS3Iltf6FC2/9N/b7T9papvu1yJyuJaUTSxsQEoIkC9UQjaXUnleFMZhjlLi37P464Nt+cHq9NCKT1vD9JOo3QEbiBUP3f0PbNCiusJbaraBoMY9yL3OFxOuq5QGmDfk5JdbzvBi6w/VqFM8V/2a4aFMjMEWkDYtv08FLbfxjneDKZYTHtEdt/KST/92BmZ33Q7nJZ6KjRYq1hJHAu6KnAa3tWOpmkw4gIdxVaRaglnWH7GYOO9eCNwMOq1hB2UFvYQ9LTbZ8J3NQ0Fdn2rZL+jRCZ+StR+1o1Pa2xLVNn3bGkB1M9CnI/21+W9PZ0rjsl3TWo0YTTRoF6IlF4PH+OsMNaX9KWRObHfuMd2fxCPTyOC6pkQUwYrctdWrJFuYTI9k2SBvmE7g28D3gmsEup/eOJz/ZcsIVL4km2/1ph3I2R9GXbLyWicd08uEdaQ+lQkT+59Ph6qme6ZeaW7QhdAhOig1/r//RMZjKY9oXtnkTq6Adt/0LShsAJA9q0DmG3iQSOmDp/22pUSwWGqN+5DXh2R199F3mSVkn93C/twBeLhoWEYnAVrk19N2Ed2+UJzrGSFg1o8xRCtKkzDRkqpiKn+qP3pPMIOFLS+2x/vsKYW9kySbofsZDflfA5rnozu1WhKlr49j6eiBZPM20UqMfCEFLGP0aITp0Ky9SSn9y3xYpJ4XH8RCKiX4iM7czc+LrOKcMod2lJba/QlL68b5fjZwH9xBKHyVx7nB6Yvs/FZsNySHo4IfB1f9ubS9qCEKP7wDjGk+mOpE8BD2Om9vs1kp5pe/8xDiuTGQpTvbB1SK4fUHr8C+DDA5ptmsQOBGxUEj4oBJGq7HiOS+K/MZKuZmbhuxKwDrHbPZAWEerXEGJIDyAmg8XC9m+E12oV7iIEvs5idrS4SsSktrCH7YPS9+X+ZklVd6bfDDy6iNKmBeOFRDR2ELVtmZJYyo7EpPThxGL2obYfVHG8EJHxU4nPxAXE++MlNdpPIrenWuafS3odoUC97pjHNIhjSSnj6fHPiEVX5bR527+RZgWmpz0yX5si+0HSKwl13sLi5DNE+uvUYfufDIh09it3aUnZKxTi3trXKzSlIveLqr9oeMPryZx6nNr+vULh9nMejwL+McT97ag0nqsknQTkhe384inA5k61iCll/urxDimTGQ5TubDtWKQtx4DF6SOGMIQmkcC5oF8aZXmH905C2blSjU7TCLXtjwMfl/R620dW6asLX09fTSgLe0AoZbapfTqCECwbxA2EmEfBUuA3FfvYKn3vjBo+gXjtu9ky/RG4lFAUPd+2Je1Ysb/CCuAp6WsT4n300xVAWGkRDRSox0zblPHfSHoCYIUq/AGEx3KmOw8g6t0Lsbo1qJ5tMo30LHdpg2d7hUI1r9DDRjGWOngMHqcerwL+arYv7dgYm/Na38xAfgqsT6jgQ4ijZvXqzFQwlQtbZhZpRVrF8en7yxmQtppSDgcyYGd6TiX+a9DPd3U94FonlV1Ja0h6pO0qhu6tItQOUa/NWV6kZ6Coke3j0gT84elQ5QWX7V8T6pHDomr95W+BSyR9g1iMvpiIxL4xjauntYqb2TK9g6il/TRwUqqHrkyaKL3Y9hFE6vcKgdspUI+Ltinj+wIfJ9LUbyCij7m+tjcfZkZdFGLz571jHM+4GaUa5crMKOYPVGJ2F3uicZAWsiNdzHahsQJ+S/6ssFksrj8vIawSM/OL+wI/lnRperwtcJHC5m2ushkymZEw7arIy6n1djvW8NyXu4e6sqT1iUjgdsxI/B9YddHcYkx9/WQHtL0c2LqUmrKAUI4caOiulib0kg4CnkosbL8NPJeILA5MdVX4Ex9H2GyI2Hncw/a5Fdo+lJjEP574P10EvCEJXtRG0q9tr1/heX1rNG33nBirhS1T+nt3JRa5GxNR36/Z/lmFth8k1Ju/xOyJ0px7h84VaqFAsnnWlgAAPB9JREFUPS5SSvqRRB3kNaSUcduVduMlPdH2BYOOZWZIQnCPSw8v8QqsLqqSq8CQz3sgsA+RESOitOLoKpk+WgHVzdVdAX85AcER9PtQ4Ggig+gm4BfAbrZ/Ocp+M/WQ9JR+v58vm0KZTBOmfWF7BfA62+enx08APlV1wTXg3CO5gbdB0nU085PttTi9qkpNsaTvE7V95Qj1nq6malykjm9J2MdsmQRwPmu7m0BTZ9vLgP+w/dP0+OHAF20/pkLbi4la3mLcLwNeb/txfdr0SnMX8HDb9xrUb+lctS1V1MKWqeM8jyL+T7vY3qjC87uJrdh2t9TnqSDV1+/v2QrUn6pYZz82FFZMjVLGu13X5uO1btxI2s32CennWQt/Sa+z/cnxjW589NvwbXneqwgRtFvT49WBiyren85nRt38hSR180IvITN80v9nwYRkuWQymSliWlORC/YGPi9prfT4ZtrVUPZF0ltsH6oeVhBzkAZ0Y5NFbeJ6SQcQ6aoQ6YdVI5fdTOjrvM7/sH23pDslLSRqQqvupq9cLGphmXrnwDS1hGwfX3p8QhIJ6kdrtUm1sFRxO1um8nmuJsQi3lE6V8/0+oYp0JNOKwXqcaDwdT7R9rXp8dqSdrX9qQHttiOiLOsUKfGJhYSYXGY2b2RGYf9IoLzwL66HKyL9yl3aIGaLmN1F9dKPiVM3b8tcR6k7rhnl40W/PctrMnNPuo8Vc9R7Eqn9t1bJ7stk5jtTvbBNKYNbpsWShiyk0O2mWiwql3T53VzQxk92X+AThMCQgTOAV1fpdAi1qksk3ZtQVLwM+DshdlS17eeYqaPejep2G2dJehvwP8woDH9LYcmA7b92NhhSDfbHGJ6lSh1bpkH0FX6R9HzgkcyeKFVSzp5QaitQzwP2sb1MUdzh97kP0HdhS0xu1iDuCWuWjv+N6Ve/boJ6/Nzt8dQwqNzF9jUj6noxoUtQ2JPtQHWl70lUN2/LXHtwF9eMTYh6zWKz9YXAwLKgzNxiu3yNR9IOwGPHM5pMZrhMeyryvQhz8A0oLeKHMRmXtHmvm7iknR1G5X2PDRtJ3awYXDdNtUZ/Q49QS9oAWFijJvBehEjY9sSN+xzg0w5rikFtf9Hn126zuz2gBvsS248rP0fSlba3rHDerrZMw0h97JdyqrAxWY2YJH2WWOxcanvvtv3OV3qkXxfMyzTslLK5ZalWfiXgKtuPrNj+IVU3b1Zkyp+Vzs/NNKdutyl3GULfWzNznT/X9uUV221LbDrfm1A3Xwgc6mrCiBOJpMtsP0bS1bYflY6dZ/tJI+73dOD/eUaEck3gZNv/Psp+M+2RdLHtx497HJlMW6Y6Ygt8gxB8uYxSBLMKLXem3w50LmK7HRsqbu4nW/iYHWj75vR4beDwAYviVhHqIvLV63f9ImKS1iFslX4EfDR9oVBXXgj8aVD/tjesPejq9NsxamOp0tiWqSVPsL1Fqrt+r6TDgSqZABPLhKZffxf4ctqIMJGJ8Z0a7e8l6WiW3wycd4v4MdPP73xqRYloV+7SinQ/aJIlsYFD4fzvROQSSTsDU7uwZXxR6vWBO0qP7yCuJZl5RJrfFiwAtmG0iuaZzJwx7QvbB7XYKTyUmjvTkp4LPA94oKRPlH61kDnwclNDP9nEFsWiFpalMPYVAbF9Wvrxtm4R6gp9Ht7nd708WQuOZKYeuMwDibrR/xjUeRrjd2wvlfQuok7u/VUjAS3oZqmyf98WM7SxZRpEv1S1f6Tvt0l6AGFfNcqNgbGjFgrUY+StwGuA1xL/z9OJCHtVTgY+k9rU8b9d0RiG3/kk0qbcZVyMZaN5zCxiPB7cxxMlHF8j7uE7Eq4FmflFWZjzTsJV4sXjGUomM1ymPRX5aODIJJRTt21tW6AkArQV8D6gPCleCpxl+6a646jZ//cIP9lyvenLbQ/0k5V0JfDUYoypzvScIo1pQNs5V1KVdG2v9EpJ19jevMI5rkpRyO0JoY3DgHe4jypyjfF1TUVOqaHH2d6t6XlpaMtU4dz90uvfTWwmPINQkjahXP3utv3OVzQkBepJokhhHPc4poUBtfYTx1yXu7ShtNH8UsKmrGAhsJntXFM4AlImVpHyXDllPJPJZIbBtC9sfwQ8jPBS+ycz6cRVLAI+DvwbDXamJa3sGhYbw0It/GQl7U7sYp+SDu0MfNCzVYM727SaOHSkwyxHv9da0s9sP7zH735qe5N+507Pu9z2oyUdAlxt+6R+tbF1GLBI/C6RDXBHt98POG8bW6bGPscd57kXsIqHK8Y270l/96m2nzPusXQi6cu2X6oedlRV3h/pPAcTquRfY/Z1bzkhtcxghnU9ydRn3BvN40Thwf1O4CHMLimY11ZlmbmhZXZfJjOvmfZU5Oe2aLsQuA14dumYqVZXuEFaLM21IfyfJe3GbD/Zv1RpaPsLkpYwk/67U6pf7cfviPraFzFbiXgp8IYK3fbzqR30Wv9c0vNsf7t8MC22q9oU/VahevtM4CNp4bKgSsOWNdi/BC6QdCqwzMfW1SwR2tgy1U6vL9CMjczNtv8paTVJ+3mAjcyUMUwF6mGzKH1va0dVpCu+uXTMzN+/e74zVTvHkzQhtn0lcKWkE+dIh2A+cSLxGb4auHvMY8nMPxYT2X1Fydhu6djA7L5MZr4z7RHb9bsdd9jTjLLfsRjCp7/3k8B2zPjJHujqFjVbAk9Obc9LE4Mq7eY8Qi3p4cA3ib+xWFRvQ/ztL7D9swrnWA34dyJa+3NJ6wGPsn16hbaN1UEldX0f2H5vhbbrErZMT2fGlmmR7T9WaFs7vb7UtlukeKqjURqhAvWwKVL/JR1ve1ReopmaTJtCcptyl7lmWFkMk4ik821vP+5xZOYnbbL7Mpn5zrQvbIsbmojI6YbAT3vVZna0bbwzrTFJ7bdB0oHAPsBXiNdrR+Bo20dWaNvaDF4NPFJThPU/gKKe9lrgJNu31+h3JeD+zE7XGrjx0XSRmAS5NiIEoOZUXbRlen0rG5lJRNJDSg/nUoG6NpKuAf6LSLl8c+fvB/2Play70s+zrMkkfcj2O4Y85BWCadv8maQJsaT1bP++43O8jKobvpOIpGcQGVtnMDkiX5k5QtL3gWOZnd23p+1njG1QmcyQmOpUZHcIHyVRg9dUbN4mVWNOpfY1HD/ZvYHH2b41nfMjwEXE4n4Qrczg1cMjdVA7h1dtNzGT8rl7irdIen0a943MpGsZqLKTX1sdVKGyuxsRYT5U0iG2j6nQV/kcTWyZCtqk17e1kZlERqlAPWz2BV5OeHV2pvhX+R+/jEhVh+UVY/+dUBrP1GfaoueNy13mmrSoXQn4nO1njns8c8yewKbAysy+t+WFbQZgLyK77whmsvvmnQBcJtOEqV7YdmL7hwqz9iqsY7u8aDpW0qKKbRcxt1L7rfxkE2K2vcddVF+crmr7DElKu+AHSzqPWDRWYZQeqav0+d2BwCa2m0zMmiwSdwG2sn2bpPsSC8NaC1sa2DKVntvY55j2NjKTyKcJC6iC27ocmy+sZ/u1KUJ4dIP26vFzt8eZRMta+0lkoibEtu+SdJuktVYwsbstOzf2M5mClJX2onGPI5MZBVO9sJX0xtLDBcSE9E8Vm7cRYvpB6n8BcEAR8RkVbu8nCxH5vEThPwewA/C5im3bRqhH6ZHaL9f+N0CjyU7DReLttm9L7f+SXrO6LJC0tmfbMlX6HLdJr7d9N7Go6+YdPK2oSL2GeA0kzddrZhFl3RdosrB1j5+7Pc7M0FiQbRKZ0Anx7cDVqT64LNZXJZNpUrlY0mYVBCAzKyAtM78ymXnNfJ2kDYs1Sz/fCXyLqCGtQuOd6SS1v7joX9ItwF62L+vbsD2Njehtf1TS2cD2RNRhT1f3n1tEuwj1NyXdm6gR/CHJI7VG+6ZcD5wt6VvMTiceqE7ccJG4UVJChniNy4+xXWXCeDhwoaRZtkwV2kGL9HpJTwQOZsY+oohMTbNabhsF6rnmL5LOAjYsv6cKKry3tpT0N+L/umr6GWb0CTLduXFFWNQOqdxlXHwrfa1IbA/sIam21WFmhaBx5lcmM9+ZavGoAklrEhf1v89Rf1cB+9s+Lz3eHvjUqG4qmidG9JIWEq9zrQi1pHuletllHqlEdPOf/VtWOndP8ZaW6sS11UElPaXfOW2fM6jfdJ7NmLFlOrPqrnwb4RdJPyEsnC6jlLLeMI17ImijQD3XSLonkZFyPPCqzt9XfW9l6tFGkG2SkPRC26dJ6rphafu4uR5TpjcromBWpjqSrgSe2pH5dU5OX89MA1MdsZW0OTHRu096/Gdgj351T0PamV5aLGpTm/MljTIdua2fbCuGEKG+iFS3mBaz/5T0Q4ZTy9hTvKXKArYPtWuwh7i4WJm0A59+rkob4ZdbbP9vjb4mnrSAfdm4x1EF23cQ6YfbEamXLoTgMiOljSDbxDCkcpexMAzV/kkjL2AzAyhnfpkIilTN/Mpk5jVTHbGVdCHwTttnpcdPBT5k+wl92rTemZZ0BJGa+0XiorELcBMpDdr2D+v9JdXQGPxkU7+NItSS/g14IHACYdtTiNQsBD5je9MKffcVbxnQdh3gLSxvM/T0no1m2o5FLl/tbJlq+xwnJXGIG99KxIS9HJkayXt5PjBpdUiSXkuUHqxOvDeWAh+x/amxDiwzNaiLL2+3Y/MJjclXPpOZz5QyvwSckeuxM9PCtC9sr7S95aBjPdru3G1nuvNYj7Zn9fm1qyycmjCunWl18XTtdqxLuz2AVwLbAD9gZmG7FDi2SjqfpOtoKN4i6XQidfs/CdGdPYA/2X5rhba1F4nDIG0ibOcZW6bVgYtGmOY+lvfyfKBbGnu/1PZxIuldwBOA19m+Ph17KPBx4BLbHxjn+KaVNoJsk8R8KXdpgibQVz6TyWQyzZjqVGRC/OXdzK6D/EXFtm2EmJ5WeYTDpZWfbAsulXQUsyPUZxfRvl5RvRT9Pk7S/7NdVdSrkzbiLfe1/TlJB6Y04XMkVUoX9vjUQWvbMrVJry/ey5IeWiyYSued2lS+RGMF6jHwCsLi4/bigO3rJb0UuBLIC9vR0MbvfJIYa7lLS+bUVz6TyWQy42O+TtKGxV7Ae5mpdzqXWOz1pLQz/UBJnyj9aiGhrDwQSe/pdtz2+6q0b0FbP9mmbJW+d/bzBGIhNSiq96AkPLWU8HXdGnib7dMr9L1E0pdoJt5SpG3/XtLzicnbg/o1aLNIlHRatzaltlUWyk1smYbhc3wKy9c8nww8psU55zttFKjnnPKitnTsH5LurnqONqn9Kyht/M4nBttXAldKOmkc5S4tWcTc+spnMplMZkxM7cJW0krAybafWbPpMHamy6ItqwAvYGZxMUrGsjM9hAj1XrY/Luk5xHj3JBZwVRa2bcRbPiBpLeBNRDrhQgb/j9ssEg9L33cilFRPSI93BX5Z5QRuYMvURvhF0qZEDfJaadFTsJApt4Gx/QVJS5jZmNlpHtch3SDpGbbPKB+U9HTg9zXOs0L5sg6BNoJsk8gGkiZKiMnJVx74OwM2tjOZTCYz2Ux7je2pwCts39Kg7dCEmBQWNqfafs4wztenn22Jhde9iZ3ptYBDbV884n5bRaglXWV7i2Sdcbbtr426ljFtfBxg+4iG7dvUYJ9r+8mDjg2bJsIvkl5MRIVfBJT9UZcC/2P7wlGMdb4gaUvgycRmyXkpcjXvkPRI4BtEjedlxHi3JWo/X2z72ornGVgbn5lhXLX242IShZiSav87mfHgBmBUugSZTCaTGR/TvrD9MvB44HuUoqj90kVLbYcmxJTUVC+1vXHdtk1QQz/ZFv29qfRwWYS6qnqspMWEOvKGwJaE+u7ZtgemubYRb5F0VtNocxt1UEk/Bp5fEvnZEPi27Uc0GUuF/loLv0jazvZFoxjffKWNAvU4kLQKoS7+SGK81wIndktR7nOOFcKXNdOMSRRikvRT4M3A1cCytPxp3XzIZDKZFZmpTUVOfCt9NaGxEJOkq5mppVwJWAcYdX3tMPxkG2H78I5xHMbs6N4g9ibqdK+3fZuk+1I9ZayNeMuFkj5JLPbKGx89LWyGUYNNpDufLakQY9oAeHXFtk1onF5friWWtGvn76tsEk0wewOPKylQf4TwXJ6XC9u0gP18v+dIusj2dn2eskL4sralTa39hDOJQkx/sl3nfpTJZDKZCWWqI7ZtaLMzLekhpYd3Esq9VRc9jVFDP9kRjKN2hFrSA1k+VezcCu2usL3VoGMdvz/d9rPV3crG7mNhk1JTtyI2Ksop2EuBswoF3QrjvhdQ+PT+xPY/+z1/GDRJr1cPP+cCV/B1nlTSBtW2RcQzRUR/UFwPJpFRp/ivKGgIfueTyLjKXdog6RlE7fMZ5CyETCaTmWqmMmKb6gIfZPu/0+NLiKgpwFtsn9Kz8QxtdqbXA64tUoElrSHpkbYvqfWH1GdpsagFsH2+pJGnI7eNUKdI2C7Aj5ixsjGhYj2IJuIt60Az0athqINKWhl4DVG7CRG9PWoO1EZrC79M6wS9Ik0UqOc7fXcyO7IQCm4Bltj+xmiGNHm0EWSbZAohpnRvPGCuyl1asiexibgyM6nIOQshk8lkppCpjNhKugB4me3fpMdXAM8AVgcW235GhXM03pmWdDmwtdOLmyYBS6rUX7ZB0hGErUHZT/Ymokawb4pty35bRahTDdQWTaKWTcRbUgrwf/b6fZWd/DY12JI+S0yyikXjK4C7bL9qUNs2tBF+kbQO8FaW/3sHWTlNNAov5kKB+lwPUKCe71QQCzuaWAQUi7X/R9TqPpgoFVg08kFOEG1q7SeRznIXYtNj5OUubShnXWUymUxmupnKiC1wz2JRmzjf9l+Av0havcoJWu5My6UdA9t3S5qL13qr9L2pn2xT2kaorycWerUXtrZ/TdSO1mEtQuCqW8101Z38xjXYRHrrlqXHZ0qaC7XdNj7HJxK1yM8H9iV8IP80uqHOD9Jm0Eg2hMbEoPfow4CnFxtTkj5N2G49ixDfyTC0WvtJ5PPAfh3lLouB+awwfLGkzTx/rboymUwmMySmdWG7dvmB7deVHq5DBVoKMV0v6QDg0+nxfsTibaQ0VfgdAp8GyhGK27oc68dtwBWSOmugegqwtBRv+VVVxeY+tFkk3iVpI9v/ByDpocykYI+SNun197X9OUkH2j4HOEfSOSMbaWZUvGLA7x9IZLYUFmmrAw+wfZekkdeBTxDD8DufRMZS7tKS7YE9JP2CuL+I0FKYz4vxTCaTyTRgWhe2l0jax/Yx5YOSXgNcWvEcbXam9wU+AbyLWHSdwWhVb4H2frJtum4ZoT6VeirKEGniEJPLulSNrPajzSLxzcBZKSVahGhWVRXoNiwiUtUPINLrn05EXqtQ1P/+XtLziYn9g4Y9wEw7JO0EfIR4L4qZSfxC4odrBpziUGKT6ezU9snAh1Kmy/dHNe5JYxi19hPKpZKOYna5y9kpZX9k5S4t+fdxDyCTyWQyc8O01tiuy4wPY3GjfQxwL2AH2zdWOMcFtp846Nh8Qi39ZFv0+1XgbGZHqJ9me4dR9pv63rmbeEvnsY7fb15hgt/XGqWtOmhSRd6EWDzMiSpyqe/aPseSXgCcR9RaHkmkXL7X2UZjXiHpOuCFtn888Mm9z7Ee8FjivXmp7d8Na3zTRpta+0mkh5J8QV9F+Uwmk8lkRs1ULmwLJD0deGR6eK3tM2u0bSzEJOk4QsDo5vR4beDwUS8wu4zjXsCptp8z4n7WJSLUT2cmQr3I9h8HtCurKS9HlVSxUYq3VLFGabhIXBl4LSVVZGDkqsiTKPySqUfTzTdJm9r+SRF562SeRuLGThtBtkwmk8lkMsNlqhe2bWizM91tQVRlkTRs1MBPdi7pUFNejgHKxoV4y0sJUaOChcBmth87hPH1XCC3WSSOURW5sc+xpIcTEfn7295c0hbAi2x/YJRjztRD0seBf2MmYwUYrPQt6Wjbr+5x3cuRuB6ohd/5JDLGcpdMJpPJZAYyrTW2rWkpxLRA0tq2bwKQdB/m4LVWSz/ZFv02ilD3W7h2nL9bSvC4xVva1GCPSxW5jfDLMURt8FGp7VWSTgLywnZ+sZAQY3t26dhApW/br07fxyVAN6m0qbWfRG4t/bys3GVMY8lkMplMZhZ5YduDljvThwMXSjolPd4Z+OCwxtaHF5R+ru0n24ItikUtgO2bJA0zOr1K54E5Em/pJzLVZpE4LlXkNsIvq9m+VJr1kkyzrclEYruVCJmknYHv2F4q6V2Esvn7PeH+vSNkEc0F2SYO24eXH0s6jPrCf5lMJpPJjIQVYmEraQ3bf08/P8z2dRWaNd6Ztv0FSUuY8Y3dyXPjodfWT7Ypo45Q98uX30DSqMRb+lmjtFkkjksVeav0vYnP8Z8lbZSeh6SXAL8f9gAz7ZD0IELc64nE/+p8IpvihoqneLftk1MGwnOAw4DPAI8bxXgnHbfzO58GVgOmUigrk8lkMpPHCrGwBS5IHnYnEQqWGw1qMISd6ZVJVhvp57mgrZ9sU8YVoYZI/y3EW55GEm+p0rClNcpW6XvlRaKke9i+0+F/uzFzrIrcMs10f+BoYFNJvwV+Aew2lIFlhsli4jq3c3q8Wzr2rIrti8yB5wOftv0NSQcPdYRTRGetver5nU8c4yp3yWQymUymClMpHiVpNeCOchqupNcCnwRe1s8Kps85KwsxSToQ2IdQUBawI3C07SPr9ltzjFfY3qrj2FVVxIGG0PdmzCzmzhxmhLqf8FYb8ZZhWKPUoSxGJelI26+fi35L/bcWfkl+pgtWwMjURNDjGrDcsT7tv0nUiT6TsEj7B3Hd27JvwxWUNoJsk0iH4N9clrtkMplMJjOQaY3YngnsAPwBQNKOhL3KcwhhoYEL25Y703sDj7N9azrXR4CLiBTBUXK9pAOY7Sd7/Yj7LBhlhLpfSnAb8ZYbmy5qGy4Sy5Hkcfgh106vl/TGHscBsP3RYQ0uMxT+LGk3IkUeYFfgLzXavxT4d+Aw2zcnT9s3D3mM00SbWvtJZFzlLplMJpPJDGRaF7ar2i4Wta8moqfPsP0nSR+ueI42QkxithjQXVRMj23JvoSf7LuY8ZN99ag77RKhPiHZh1RayLdMCV5Ec/GWJZK+RE1rlESTGuyxpkc0TK9fs/Tza0iqyJl5y15EZsoRxPvtwnSsErZvk/RHYHvg58S17+cjGOe00KbWfhIZV7lLJpPJZDIDmdZU5DOBc4AHAzsBm6RF7XrAdyv6dj6ejp1poNLOdIpy7QF8LR3aATjW9sca/DnznpSOt10pQr06cFHVdLxhpARLWkgshitHSyQt7nLYg2yKepzrXsCptp/T5zm3AdcRC/eN0s8ws5Cf0/TFOun16fk9U8Iz04Gkg4BtiGvmwyU9ADjZ9jgyDOY9PXx/C6bO/3ec5S6ZTCaTyQxiWiO2OxOpxz8jIonfST6hTwPeWfEcjXembX9U0tlE1EPAnnNhl9HUT3YYXdMuQt0mJbixeEtba5QOqqiDPmKI/dVmCMIv07cLNiVIeovtQyUdSZf/k+0DKp5qR+DRwA9Tu99JWrN/kxWXFdD3d5zlLplMJpPJ9GUqF7a2/wJ8oHgs6SKipvEjtn9a8TRyKZxt+25JlV+vlII212loo/aT7cVi4BJJ5Qj152q0b5MS/Hlgvw7xlsVAlah8Y2uUJotE278adN507otsb1fluTUZl89xZvQUG0NLWp7nDtuWVNg6rd7yfFPNMATZJoyxlLtkMplMJlOFqVzYdmL7d1QQjOpgEnemR+0n25UhRKgXEhHxZ5dPC1RZ2LYRb2ljjTLKReIqg5/SiNrCLx0L+IeltHMYU/p0pju2T0s/3tap+i5p5y5NevHlVDN6b0n7EPW5xwxpmNNIY7/zScT2H4GXjXscmUwmk8l0YyprbIeBpHWJnemnM7MzvSjd2OclknYH3g7M8pO1ffz4RjVaJB1BpAGXxVtuIoSs+oq3tLFGaVODXeHcy2yBhomky4Gti0yEpCa9pF9fHfYey1E1Cp2ZG7q9d+q+nyQ9i9hkEqFJ8L0hD3NqqVJrP8mMsdwlk8lkMpmBrBAR2yZM4s607S9IWsKMn+xOw/STHRVtUoKBrdL3gzqOPyGdq594SxtrlElUB62dXj8P0qczFZD0XOB5wAMlfaL0q4VERkHV89wbuBn4MvAz27cMcZgrAlVq7SeZcZW7ZDKZTCYzkLyw7cEE70yP0k92VDROCW4p3tLGGqVVDfagcw/pPJ2MMr1+VOnTmWr8jqivfRFQFk5bSnh390XSPYGjifr464EFwENS3fy+tu8Y9oCngSEIsk0aYyl3yWQymUymClOdipxqLTv/wFuICeCbbPec1HezNpnvdidd/GR3BCr7yY6LlinBYxFvkfRV4GxmLxKfZnuHIZx78wHevU3PO7L0+lGlT2fqIWll2/9q0O59hAXVvqX0+jWB/wZ+Zfvdwx3pdNCRqj/1gmwrYrlLJpPJZCaHaV/YvpeIZJxELPReBvwb8FPgtbaf2qftlcBTO3amz7H9qFGPuylt/WTHhaTvA8cyOyV4T9vPqND2TaWHy8Rb+kXWh2GN0maRKGkn4CPAusT7shBiWjio7XwlL2znB5I2Bg4BNqMURbfdNz1W0jXAY23f1nF8DeBi25uPYLgTzyhr7ecrkjZjpsTjzEkod8lkMpnMisG0pxD9u+3HlR4fLeli2++T9I4BbQ8HLpQ0a2d6JKMcHm39ZMdF45Rg24eXH0s6DDh1QLPW1igta7APBV7Y1Lu3KSNOr5+E99mKwGKi3vwIwrd7T6r9b+7uXNQC2P57Yf2T6cok1tq3ZRLLXTKZTCazAjDtC9u7Jb2UmbSpl5R+13eyNqFCTG39ZMeC7V8TtYHDYKB4yzCsUVouEm+c60VtYpTCL68Y0nky7VjV9hmSlIS/DpZ0HsuLq3Xi9B7utgi+e+ijnB5GWWs/7+hS7nKCpHlf7pLJZDKZFYOpvQEnXg58HPgUsZC9GNhN0qrA6yq0n6id6SH4yc4pQ0oJbiPe8naW9zfudqwbbRaJSyR9Cfg68M/SOar49rahsfDLoPTpUdQEZxpxe7Jx+rmk1wG/Jf5ng1iLEJ3qtrDNEdveTKLfeRv2Bh5XKnf5CHARoWqfyWQymcxYmeqFbRKHemGPX5/fr+2k7kwn39ae3q3zjNYpwURNbUEl8ZYhWaO0UQddSKQsPrt0zMCoF7Zt0uvHkj6dqc0iImvhAOD9RMbJHoMa2d5gpKOaXvYlau3fxUyt/avHOqLRMqnlLplMJpNZAZh28ah1iMXpBpQWHVXSRSdViGkSkbRzt5TgzmM92tYWb5G0JeF/+z6grKq8FDirWKwO6Hci1UGbCr9IusD2E0c3sswwkbSQiKgvHfdYMtODpDcSGyXlcpdjbX9sXGPKZDKZTKZg2he2FwLnESl2y3aZbX+lQturgW1t354erwL8YD6rIk8q3RR1q6rsSroc2Lqoc0tpmEsqtm1kjVJq33SR+CAide+JRJTnfKJe94amY6lKWtQ/OfV7nu0rK7b7OKEo/nXmNn06UwNJ2xC19mumQ7cAe9m+rHerTFMm2O+8MZK2Zqbc5dz5XO6SyWQymRWLqU5FBlaz/daGbSdSiGmSGFJKcBvxlg0k1bZGKdG0BnsxYUFVCFXtlo49q8Y5atMyvX5c6dOZenwe2M/2eQCStifeWznTZDSMUpBtXjJh5S6ZTCaTWYGY9oXtNyU9z/a36zacNCGmCeV3RH3ti4ioesFS4A0Vz9FGvKWpNUrbReI6theXHh8raVHFMbehsfCL7T1HPLbMcFhaLGoBbJ8vqXI6sqSNgBts/1PSU4kF8RfKi7fMLNrU2mcymUwmkxki056KvBRYnUid/BcdSq6Z+UGblGBJ6xLiLU9nRrxlUfKZHdT2MtuPkXR1kWIu6TzbT6rQtnENtqTvA8cCX0yHdiU2Tp4xqG0b2qTXjzN9OlMdSUcQ4lFfJP5PuwA3ERswRbStX/srgG0IXYLvEp7Qm9h+3sgGPcGkWvt3MKOkPhG19plMJpPJTCNTvbNse83Bz8rMAxqnBKcF7Msa9tvUGgXaqYPuBXySiBQbuDAdGzVt0uvHkj6dqc1W6Xunb+0TiPfa0+nP3bbvlLQj8DHbR6Y69kwXkt/5dcRmwN3EBtVFYx5WJpPJZDIrJFMZsZW0qe2fJJGL5RgUtcjMLZLOZyYl+IWklGDbnZPzbm0bi7dI2pawHLo3YY2yFnCo7YsrtJ1IddCmwi+SrrC91aBjmclG0iXAx4B3EvZOv5B0je3Nxzuy+UkqSXgVUWsu4jpwzHy3hctkMplMZhqZ1oXtMbb3kXRWl1/b9qCoRWYOaZkSfLntRw86NuAcjaxR6i4SJb3F9qGSjiSiZ7OwfUCd/ueScaVPZ+oh6T3djtt+X8X2mxHerBfZ/qKkDYFdbH94iMOcGrItXCaTyWQy84epTEW2vU/6/rRxjyVTiTYpwY3FWzqtUSTVskZpoA764/R9SY0284VxpU9n6nFr6edVgBcw876rwrPKGywpYvuPYQ1uCmlTkpDJZDKZTGaITGvEdqd+v8/em/OLlinBuwNvB05JhyqLt6Roy/4d1iifGnW0RdLOtk8edCyTaYukewGn2n5Oxed385SulQGxIjGpJQmZTCaTyUwj07qwLaxU1iVEU85Mj58GnG2778I3Mx5apARvxowozpm2f1Sx3QW2nzjo2LDpsXhY7th8YJLTpzPLas4vtb3xgOftCvwH8CTg3NKv1gTusv3M0Y1ysmlat57JZDKZTGa4TGsq8p4Akr4JbGb79+nxesB/j3NsmeVpmxIMrEyycko/V+VSSUcx2xrl7EJ0bNgiY5KeCzwPeKCkT5R+tRC4c5h9DZFJTp9e4UiWTsUGxErAOkCV+toLgd8D9wMOLx1fClw1zDFOGw1KEjKZTCaTyYyAqVzYltigWNQmbgQePq7BZHryeWC/jpTgxUAVT9gDgX0In04BJ0g6uqIq6Vbpe1NrlLr8jlggvggoL9qXAm8Ycl9DwfZp6cfbuqVPj2FImf68oPTzncCNtgdumtj+laQbgFttnzOy0WUymUwmk8mMiKlMRS6Q9ElgY2Yici8DrrP9+rEOLDOLNinBk6hKKmll2/8a9zjqMEnp0ysykh4PXFuk80taA3ik7Usqtj8VeIXtW0Y4zEwmk8lkMpmhM9URW9uvS0JShW3M0ba/1q9NZiy0SQlurEra1hqlBRtIOgTYjFCuLfp96Ij7rc2Epk+vyHwaKG823NblWD9uB66W9D1KCsu5ljqTyWQymcx8Z6oXtrBMATmrIM9vtkrfm6QELwYukVRWJf1cxX7bWqM0ZTHxtx5BCJrtyfy1CJm49OkVHLmUhmP7bkl1rvPfSl9lpjetJ5PJZDKZzNQwlanIkpbSfTImQnV34RwPKTNChqVKWtcapSmSLrP9GElX235UOnae7ScNajsuJjF9ekVE0leBs4koLcB+wNNs79DwfA8GXmb7v4YywEwmk8lkMpkRMZURW9trjnsMmeq0TQkeoirpasBcpAPfLmkB8HNJrwN+S1hTzWcmJn16BWdf4BPAu4jNvTOAV9c5gaT7EX7QuwIPZMajNZPJZDKZTGbeMpULW0kLbf9N0n26/d72X+d6TJm+jCUluIU1SlsWEYvoA4D3E6nWe8xBv22YpPTpFRbbfyRE8mohaU1gR8LL9uHEYvahth803BFmMplMJpPJjIZpTUX+pu0XSPoFsXApT8Cdo0zzmzlMCX5I6WFla5Qh9r+QeD8unas+mzKJ6dMrIpKOAw60fXN6vDZwuO29BrT7B3ApEek937YlXZ+vlZlMJpPJZCaFqYzY2n5B+r7huMeSacRcpQSvR4c1iqTK1ihNkbQNEQFdMz2+BdjL9mV9G46XSUyfXhHZoljUAti+SdKjK7R7BxHp/TRwkqQvjWh8mUwmk8lkMiNhKiO2BZKe3O247XPneiyZ3vRKCbb9yRH3ezmwdaEimxZuS0btzZq8d/e3fV56vD3wqXnuvbstkR5+byJ9ei3gUNsXj3NcmdlIuhJ4qu2b0uP7AOcUUfYK7R9K1Na+jPAAPwj4mu2fjWjImUwmk8lkMkNh2he2p5UergI8FrjMdj/7mMwcM66UYElX2N6q49hVo15gSrrA9hMHHZuPTFL69IqIpN2BtwOnpEM7Ax+0fXyDcz2KWOTuYnuj4Y0yk8lkMplMZvhM9cK2k2RdcajtXcc9lswMkh5PR0owMBcpwUO1RqnR7xFEuvUXiUj1LsBNwFdgmcrzvKIzfRqYhPTpFRJJmzHj/Xym7R8N8dwX2d5uWOfLZDKZTCaTGRYr2sJWwFVV0/Iyc8MYU4LXJaxRns6MNcqipCw7yn7P6vNrz8eMgklMn15RkbQl8GTiPX2e7SuHeO7LbVep2c1kMplMJpOZU6ZSPKpA0pHM1G4uALYChjbJywwNubTDYvtuSSN/bza1RhlCv0+b6z6HwNJiUQtg+3xJOR15niHpQGAfIvov4ARJR9s+ckhdrDg7oZlMJpPJZCaKqY7YSip7g94J/NL2BeMaT6Y7Y0wJbmSNMoR+39PtuO258NBtxCSmT6+IpMj6drZvTY9XBy4aVmRd0g9HnUmRyWQymUwm04SpjtjaPm7cY8hUYl8iJfhdzKQEv3oO+m1qjdKWW0s/rwK8gFAcns9slb4f1HH8CcT/bN6lT6+gCLir9PguZvt4D+P8mUwmk8lkMvOOqVzYStoYeCfwV+CjwDHAk4D/A15l+wdjHF6mg3GlBAMLJK3dYY0yFynQh5cfSzoMOHXU/bZhQtOnV0QWA5dI+lp6vAPwuSGe/xVDPFcmk8lkMpnM0JjKhS0xufsCsBC4BFgE7Egsbj8JPG5sI8ssx7hSgoHDgQslzbJGGXGf3VgNeOgY+q3MJKZPr4jY/qiks4HtiejqnrYvr9pe0k7AR4B1U3vFab0wnf+aoQ86k8lkMplMZghMZY1t2Z9U0nW2H9btd5n5QTel1blSXx2lNUqfPq9mRoRnJWAd4H22Pznqvpsi6U2lh8vSp+dg8yEzh0i6Dnih7fmeGp/JZDKZTCYzi2mN2N5d+vlvfX6XmR+MJSU4sTIpKpV+ngteUPr5TuBG23fOUd+NmMT06UwjbsyL2kwmk8lkMpPItC5sN03qoAI2Sj+THs/rlM8VlLGkBM+BNUov1gOutb00jWMNSY+0fcmI+x0m8z59OtOIJZK+BHwd+Gdx0PZXxzaiTCaTyWQymQpMayryQ/r93vav5mosmWqMKSV4pNYoffq9HNi68O6VtABYMp9tVCYxfTpTH0mLuxx2TjnPZDKZTCYz35nKiG3Vhauki2xvN+rxZCoxjpTgUVuj9OzXpR0l23dLmu+fxYlLn87Ux/ae4x5DJpPJZDKZTBMWjHsAY2aVcQ8gsywl+ETgfoQa6wmSXj8HXRfWKAdLOhi4mOFao/TiekkHSFo5fR0IXD8H/bZhPeCvtn9l+7fAKpKyuviUIelBkr4m6Y+SbpT0FUkPGve4MplMJpPJZAYxlanIVZH0w/mc/rmiMK6U4NTX1sxYo5xbxxqlRZ/rAp8gUq8NnAEsSn6+85JJTJ/O1EfS94CTgOPTod2Al9t+1vhGlclkMplMJjOY+Z7+mFkxGFdKMLZ/CPxwLvoq9flH4GVz2ecQmMT06Ux91rFdrrM9VtKicQ0mk8lkMplMpioreirynCyeMgMZV0rwWJB0nKR7lx6vLenzYxxSFSYxfTpTnz9L2k3SSulrN+Av4x5UJpPJZDKZzCBW9FTkzW1fM+5xZMaTEjwuJF1u+9GDjs0nJjF9OlMfSesDnwS2I/7PFwIHZiX5TCaTyWQy852pXthK2gn4CCFIpPRl2wvHOrDMCo2kK4Gn2r4pPb4PcI7tR413ZJlMJpPJZDKZzGQy7TVyhwIvtP3jcQ8kkylxOHChpFPS452BD45xPAORdBwRubs5PV4bODz7m04Hkt5i+1BJRzLjV7wM2weMYViZTCaTyWQylZn2he2NeVGbmW/Y/oKkJURaL8BOtn80zjFVYItiUQtg+yZJ8zZ1OlOb4jq5ZKyjyGQymUwmk2nItC9sl0j6EvB14J/FQdtfHduIMplgZVJqfPp5vrNA0tod6dPTfv1YYbB9WvrxNtsnl38naecxDCmTyWQymUymFtNeY7u4y2Hn9MnMOEmKwvsAXyEWtzsCR9s+cqwD64Ok3YG3A7PSp20f37tVZtLo5u2d/b4zmUwmk8lMAlO9sM1k5iOSrgK2s31rerw6cJHtLcY7sv5I2oyZ9OkzJyB9OlMRSc8Fnge8FPhS6VcLgc1sP3YsA8tkMplMJpOpyFT72Ep6kKSvSfqjpBslfUXSg8Y9rswKj4C7So/vYjI8lYv06eLnzPTwO6K+9nbgstLXqcBzxjiuTCaTyWQymUpMdcRW0veAk4AiXXI34OW2nzW+UWVWdCS9EdgD+Fo6tANwrO2PjWtMg5jE9OlMfSStbPtf4x5HJpPJZDKZTF2mfWF7he2tBh3LZOYaSVsD2xOLxHNtXz7mIfVlUtOnM/WQtDFwCLAZsEpx3PZDxzaoTCaTyWQymQpMu6rpnyXtBnwxPd4V+MsYx5PJAGD7h8APxz2OGkxq+nSmHouBg4AjgKcBe5L/z5lMJpPJZCaAqa6xBfYixFD+APweeEk6lslk6rEYuETSwZIOBi4GPjfeIWVGwKq2zyCyeX5l+2BmBMMymUwmk8lk5i1TnYqcyWSGx6SlT2fqI+kC4EmErdOZwG+BD9veZKwDy2QymUwmkxnAVC5sJb3F9qGSjgSW+wNtHzCGYWUymcy8RtK2wI+BewPvB9YCDrV98TjHlclkMplMJjOIaa2x/XH6vmSso8hkMpkJwvYPACQtAA6wvXTMQ8pkMplMJpOpxFQubG2fln68zfbJ5d9J2nkMQ8pkMpl5j6RtiHrqNdPjW4C9bF821oFlMplMJpPJDGAqU5ELJP3Q9taDjmUymUxmma3T/rbPS4+3Bz6VbZ0ymUwmk8nMd6YyYivpucDzgAdK+kTpVwuBO8czqkwmk5n3LC0WtQC2z5eU05EzmUwmk8nMe6ZyYQv8jqivfRFQTqFbCrxhLCPKZDKZ+c+lko4ivL8N7AKcnRSxC//lTCaTyWQymXnHtKcir2z7X+MeRyaTyUwCks7q82vbzp62mUwmk8lk5iXTvrDdGDgE2AxYpThu+6FjG1Qmk8lkMplMJpPJZIbKtKYiFywGDgKOAJ4G7AlorCPKZDKZeYqk93Q7bvt9cz2WTCaTyWQymTosGPcARsyqts8gItO/sn0wkFPpMplMpju3lr7uAp4LbDDOAWUymUwmk8lUYdojtrdLWgD8XNLrgN8C6455TJlMJjMvsX14+bGkw4BTxzScTCaTyWQymcpMe8R2EbAacADwGOAVwB7jHFAmk8lMEKsBWZMgk8lkMpnMvGeqxaMKJC0kFD2zH2Mmk8n0QNLVhM0PwErAOsD7bH9yfKPKZDKZTCaTGcxUL2wlbUMISK2ZDt0C7GX7st6tMplMZsVE0kNKD+8EbrR957jGk8lkMplMJlOVaU9F/jywn+0NbG8A7E8sdDOZTCazPOsBf01ie78FVpH0uHEPKpPJZDKZTGYQ076wXWr7vOKB7fOBnI6cyWQy3fk08PfS49vSsUwmk8lkMpl5zbSrIl8q6Sjgi0Td2C7A2ZK2BrD9w3EOLpPJZOYZcqk+xfbdkqb9PpHJZDKZTGYKmPYa27P6/Nq2s6dtJpPJJCR9FTibmSjtfsDTbO8wrjFlMplMJpPJVGGqF7aZTCaTqY6kdYFPAE8nslzOABbZ/uNYB5bJZDKZTCYzgKle2Ep6T7fjtt8312PJZDKZTCaTyWQymcxomHbxqFtLX3cBzwU2GOeAMplMZr4i6ThJ9y49XlvS58c4pEwmk8lkMplKTHXEthNJ9wJOtf2ccY8lk8lk5huSLrf96EHHMplMJpPJZOYb0x6x7WQ14KHjHkQmk8nMUxZIWrt4IOk+TL96fiaTyWQymSlgqicskq4mBFAAVgLWAXJ9bSaTyXTncOBCSaekxzsDHxzjeDKZTCaTyWQqMdWpyJIeUnp4J3Cj7TvHNZ5MJpOZ70jajFBFBjjT9o/GOZ5MJpPJZDKZKkx7KvJ6wF9t/8r2b4FVJD1u3IPKZDKZeczKgEo/ZzKZTCaTycx7pn1h+2ng76XHt6VjmUwmk+lA0oHAicD9gHWBEyS9fryjymQymUwmkxnMtKciX2F7q45jV9neYkxDymQymXmLpKuA7Wzfmh6vDlyUr5mZTCaTyWTmO9Mesb1e0gGSVk5fBwLXj3tQmUwmM08R4fldcBczacmZTCaTyWQy85apVkUG9gU+AbyLUEc+A3j1WEeUyWQy85fFwCWSvpYe7wB8bnzDyWQymUwmk6nGVKciZzKZTKYekrYGticitefavnzMQ8pkMplMJpMZyFQvbCUdBxxo++b0eG3gcNt7jXVgmUwmk8lkMplMJpMZGtNeY7tFsagFsH0T8OjxDSeTyWQymUwmk8lkMsNm2he2C1KUFgBJ92H664ozmUwmk8lkMplMZoVi2hd5hwMXSjolPd4Z+OAYx5PJZDKZTCaTyWQymSEz1TW2AJI2A56eHp5p+0fjHE8mk8lkMplMJpPJZIbLtKciA6zMjA/jyuMcSCaTyWQymUwmk8lkhs9UL2wlHQicCNwPWBc4QdLrxzuqTCaTyWQymUwmk8kMk6lORZZ0FbCd7VvT49WBi2xvMd6RZTKZTCaTyWQymUxmWEx1xJZIQb6r9PguZtKSM5lMJpPJZDKZTCYzBUy7KvJi4BJJX0uPdwA+N77hZDKZTCaTyWQymUxm2Ex1KjKApK2B7YlI7bm2Lx/zkDKZTCaTyWQymUwmM0SmfmGbyWQymUwmk8lkMpnpZtprbDOZTCaTyWQymUwmM+XkhW0mk8lkMplMJpPJZCaavLDNZDKZzAqFpLskXVH62qDBOXaQtNkIhoekB0g6ZRTn7tPnVpKeN5d9ZjKZTCYzTKZdFTmTyWQymU7+f3v3EmpVFcdx/PulDMvSnoMgekkUJaIogSIlEREEaRjdQVDSi4KahNFAgsqeFmQ5iYSoICoqCZXILLOg98M0e0iRTqpBUZiEFda/wVm3jnLv8VlxuL/P5O6z1vrvtda+g8v//vfeZ1tVTdrHc8wGVgCf7W6AemBVbd/VuKr6Frh475e2Z9QDgUnAVODF/2reiIiI/SkV24iIGPHUKerr6ofqSvXY1n61+r66Tn1ePUSdDlwI3NcqvuPVNerUFnO0urkdz1WfVZcDL6tj1EfbOdeqs4ZYy4nqhq74F9Tl6ib1evXGFvuOemQbt0ZdpL6lblDPbO1Htvj1bfzE1n6r+oj6MvAEcDsw0PYzoJ7ZzrW2/Ty1az1L1ZfUL9WFXes+X/2oXatXW9uQ+1XPUN9r861XT/kXfq0RETGCpGIbEREjzcHqx+14E3AJsBiYVVXfqwPAncAVwNKqWgKg3gFcWVWL1WXAiqp6rvX1mm8aMLGqflTvAlZX1RXq4cB76itV9UuP+AnAZGA08BVwc1VNVh8ALgMWtXFjqmq6ehbwaIu7DVhbVbPVc+gksZPa+CnAjKraps4FplbV9W0/Y4Gzqmq7ei5wFzCnxU1q6/kN2KguBn4FlrSYTYMJNzB/qP0C1wIPVtWT6kHAAb0uYERExK4ksY2IiJFmh1uR1Ql0ksBVLUE9APiudU9oCe3hwKHAyr2Yb1VV/diOzwMuVOe1z6OB44HPe8S/VlVbga3qFmB5a/8EmNg17imAqnpDHdsSyRm0hLSqVqtHqePa+GVVtW2YOccBj7dKagGjuvperaotAOpnwAnAEXS+K35Tm2tX+30bmK8eR+efB1/22H9ERMQuJbGNiIiRTuDTqpo2RN9jwOyqWteqmjOHOcd2/nm8Z/ROfd3VWIE5VbVxD9b3W9fxn12f/2THv+M7fzF9tfl2NjiuV5V4AZ2E+qL2cq01w6znj7YGh5gfht/v5+q7wAXASvWqqlrdYz0RERE95RnbiIgY6TYCx6jTANRR6hmt7zDgO3UUcGlXzNbWN2gznVt7ofeLn1YCN9hKw+rkfV/+3wbaOWcAW1pV9Q3autWZwA9V9fMQsTvvZxzwTTueuxtzvw2crZ7U5hq8FXnI/aonA19X1UPAMnasPEdEROyxJLYRETGiVdXvdJLRe9V1wMfA9NZ9C/AusAr4oivsaeCm9kKk8cD9wHXqW8DRPaZbQOe23vXtBVEL9uNWfmrzPwxc2dpuBaaq64F7gMuHiX0NOH3w5VHAQuBu9U124/nXqvoeuAZY2q7hM61ruP0OABvas86n0Xn2NyIiYq9ZNdSdQxEREdEv1DXAvKr64P9eS0RExP8hFduIiIiIiIjoa6nYRkRERERERF9LxTYiIiIiIiL6WhLbiIiIiIiI6GtJbCMiIiIiIqKvJbGNiIiIiIiIvpbENiIiIiIiIvpaEtuIiIiIiIjoa38BQ4Ys8azjjtIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "forest_importances = pd.Series(importances, index=X.columns)\n",
    "\n",
    "forest_importances.sort_values().plot.bar()\n",
    "plt.xlabel(\"Feature importances\")\n",
    "plt.ylabel(\"Mean decrease in impurity\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0d0e0d1",
   "metadata": {},
   "source": [
    "### Sequential Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d01e6ed9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: mlxtend in c:\\users\\preetam\\anaconda3\\lib\\site-packages (0.19.0)\n",
      "Requirement already satisfied: matplotlib>=3.0.0 in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from mlxtend) (3.3.4)\n",
      "Requirement already satisfied: pandas>=0.24.2 in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from mlxtend) (1.2.4)\n",
      "Requirement already satisfied: scipy>=1.2.1 in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from mlxtend) (1.6.2)\n",
      "Requirement already satisfied: scikit-learn>=0.20.3 in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from mlxtend) (0.24.1)\n",
      "Requirement already satisfied: numpy>=1.16.2 in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from mlxtend) (1.20.1)\n",
      "Requirement already satisfied: setuptools in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from mlxtend) (52.0.0.post20210125)\n",
      "Requirement already satisfied: joblib>=0.13.2 in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from mlxtend) (1.0.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (0.10.0)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (8.2.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (2.8.1)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (1.3.1)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.3 in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from matplotlib>=3.0.0->mlxtend) (2.4.7)\n",
      "Requirement already satisfied: six in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from cycler>=0.10->matplotlib>=3.0.0->mlxtend) (1.15.0)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from pandas>=0.24.2->mlxtend) (2021.1)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in c:\\users\\preetam\\anaconda3\\lib\\site-packages (from scikit-learn>=0.20.3->mlxtend) (2.1.0)\n"
     ]
    }
   ],
   "source": [
    "!pip3 install mlxtend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9ce8a387",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c313c44a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5e471bcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "STOPPING EARLY DUE TO KEYBOARD INTERRUPT..."
     ]
    }
   ],
   "source": [
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "\n",
    "#Parameters to understand\n",
    "\n",
    "sfs = SFS(knn, \n",
    "          k_features=3, \n",
    "          forward=False, \n",
    "          floating=True, \n",
    "          scoring='accuracy',\n",
    "          cv=3,\n",
    "          n_jobs=-1)\n",
    "feature_names = X.columns\n",
    "sfs = sfs.fit(X, y,custom_feature_names=feature_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "73814408",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature_idx</th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>feature_names</th>\n",
       "      <th>ci_bound</th>\n",
       "      <th>std_dev</th>\n",
       "      <th>std_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>[0.5561407708881972, 0.5246647509578544, 0.558...</td>\n",
       "      <td>0.546332</td>\n",
       "      <td>(destination_Home, destination_No Urgent Place...</td>\n",
       "      <td>0.034528</td>\n",
       "      <td>0.015344</td>\n",
       "      <td>0.01085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>[0.5654776155135265, 0.5390325670498084, 0.578...</td>\n",
       "      <td>0.561098</td>\n",
       "      <td>(destination_Home, destination_No Urgent Place...</td>\n",
       "      <td>0.037178</td>\n",
       "      <td>0.016521</td>\n",
       "      <td>0.011682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>[0.5925305242997366, 0.5459770114942529, 0.575...</td>\n",
       "      <td>0.571313</td>\n",
       "      <td>(destination_Home, destination_No Urgent Place...</td>\n",
       "      <td>0.043267</td>\n",
       "      <td>0.019227</td>\n",
       "      <td>0.013596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>[0.6042614316495092, 0.5572318007662835, 0.588...</td>\n",
       "      <td>0.583365</td>\n",
       "      <td>(destination_Home, destination_No Urgent Place...</td>\n",
       "      <td>0.044002</td>\n",
       "      <td>0.019554</td>\n",
       "      <td>0.013826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          feature_idx  \\\n",
       "59  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...   \n",
       "58  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...   \n",
       "57  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...   \n",
       "56  (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...   \n",
       "\n",
       "                                            cv_scores avg_score  \\\n",
       "59  [0.5561407708881972, 0.5246647509578544, 0.558...  0.546332   \n",
       "58  [0.5654776155135265, 0.5390325670498084, 0.578...  0.561098   \n",
       "57  [0.5925305242997366, 0.5459770114942529, 0.575...  0.571313   \n",
       "56  [0.6042614316495092, 0.5572318007662835, 0.588...  0.583365   \n",
       "\n",
       "                                        feature_names  ci_bound   std_dev  \\\n",
       "59  (destination_Home, destination_No Urgent Place...  0.034528  0.015344   \n",
       "58  (destination_Home, destination_No Urgent Place...  0.037178  0.016521   \n",
       "57  (destination_Home, destination_No Urgent Place...  0.043267  0.019227   \n",
       "56  (destination_Home, destination_No Urgent Place...  0.044002  0.019554   \n",
       "\n",
       "     std_err  \n",
       "59   0.01085  \n",
       "58  0.011682  \n",
       "57  0.013596  \n",
       "56  0.013826  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(sfs.get_metric_dict()).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fca0ba31",
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_df.to_csv(\"Model.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87e52881",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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