Diwali Sales Analysis..ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bab70c34-c67c-4fd7-8a25-0bbcd793928c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2953cb3a-0985-43d5-99dc-ca34afd1ab77",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Diwali Sales Data.csv', encoding= 'unicode_escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c8386044-3a83-4092-9b82-cf1b18520f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11251, 15)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "abbe5dcb-4774-4d57-b176-c39de384b48d",
   "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>User_ID</th>\n",
       "      <th>Cust_name</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>State</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Status</th>\n",
       "      <th>unnamed1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1002903</td>\n",
       "      <td>Sanskriti</td>\n",
       "      <td>P00125942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>23952.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000732</td>\n",
       "      <td>Kartik</td>\n",
       "      <td>P00110942</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Govt</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23934.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1001990</td>\n",
       "      <td>Bindu</td>\n",
       "      <td>P00118542</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Automobile</td>\n",
       "      <td>Auto</td>\n",
       "      <td>3</td>\n",
       "      <td>23924.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1001425</td>\n",
       "      <td>Sudevi</td>\n",
       "      <td>P00237842</td>\n",
       "      <td>M</td>\n",
       "      <td>0-17</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Construction</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23912.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000588</td>\n",
       "      <td>Joni</td>\n",
       "      <td>P00057942</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>Gujarat</td>\n",
       "      <td>Western</td>\n",
       "      <td>Food Processing</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23877.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID  Cust_name Product_ID Gender Age Group  Age  Marital_Status  \\\n",
       "0  1002903  Sanskriti  P00125942      F     26-35   28               0   \n",
       "1  1000732     Kartik  P00110942      F     26-35   35               1   \n",
       "2  1001990      Bindu  P00118542      F     26-35   35               1   \n",
       "3  1001425     Sudevi  P00237842      M      0-17   16               0   \n",
       "4  1000588       Joni  P00057942      M     26-35   28               1   \n",
       "\n",
       "            State      Zone       Occupation Product_Category  Orders  \\\n",
       "0     Maharashtra   Western       Healthcare             Auto       1   \n",
       "1  Andhra Pradesh  Southern             Govt             Auto       3   \n",
       "2   Uttar Pradesh   Central       Automobile             Auto       3   \n",
       "3       Karnataka  Southern     Construction             Auto       2   \n",
       "4         Gujarat   Western  Food Processing             Auto       2   \n",
       "\n",
       "    Amount  Status  unnamed1  \n",
       "0  23952.0     NaN       NaN  \n",
       "1  23934.0     NaN       NaN  \n",
       "2  23924.0     NaN       NaN  \n",
       "3  23912.0     NaN       NaN  \n",
       "4  23877.0     NaN       NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bc72626b-8600-4cbe-bd16-ceb3c0b2e6e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of        User_ID    Cust_name Product_ID Gender Age Group  Age  Marital_Status  \\\n",
       "0      1002903    Sanskriti  P00125942      F     26-35   28               0   \n",
       "1      1000732       Kartik  P00110942      F     26-35   35               1   \n",
       "2      1001990        Bindu  P00118542      F     26-35   35               1   \n",
       "3      1001425       Sudevi  P00237842      M      0-17   16               0   \n",
       "4      1000588         Joni  P00057942      M     26-35   28               1   \n",
       "...        ...          ...        ...    ...       ...  ...             ...   \n",
       "11246  1000695      Manning  P00296942      M     18-25   19               1   \n",
       "11247  1004089  Reichenbach  P00171342      M     26-35   33               0   \n",
       "11248  1001209        Oshin  P00201342      F     36-45   40               0   \n",
       "11249  1004023       Noonan  P00059442      M     36-45   37               0   \n",
       "11250  1002744      Brumley  P00281742      F     18-25   19               0   \n",
       "\n",
       "                State      Zone       Occupation Product_Category  Orders  \\\n",
       "0         Maharashtra   Western       Healthcare             Auto       1   \n",
       "1      Andhra Pradesh  Southern             Govt             Auto       3   \n",
       "2       Uttar Pradesh   Central       Automobile             Auto       3   \n",
       "3           Karnataka  Southern     Construction             Auto       2   \n",
       "4             Gujarat   Western  Food Processing             Auto       2   \n",
       "...               ...       ...              ...              ...     ...   \n",
       "11246     Maharashtra   Western         Chemical           Office       4   \n",
       "11247         Haryana  Northern       Healthcare       Veterinary       3   \n",
       "11248  Madhya Pradesh   Central          Textile           Office       4   \n",
       "11249       Karnataka  Southern      Agriculture           Office       3   \n",
       "11250     Maharashtra   Western       Healthcare           Office       3   \n",
       "\n",
       "        Amount  Status  unnamed1  \n",
       "0      23952.0     NaN       NaN  \n",
       "1      23934.0     NaN       NaN  \n",
       "2      23924.0     NaN       NaN  \n",
       "3      23912.0     NaN       NaN  \n",
       "4      23877.0     NaN       NaN  \n",
       "...        ...     ...       ...  \n",
       "11246    370.0     NaN       NaN  \n",
       "11247    367.0     NaN       NaN  \n",
       "11248    213.0     NaN       NaN  \n",
       "11249    206.0     NaN       NaN  \n",
       "11250    188.0     NaN       NaN  \n",
       "\n",
       "[11251 rows x 15 columns]>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info\n",
    "#this will show data inside file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f6efebae-43bb-4b1c-b1b9-52b635e15b63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11251 entries, 0 to 11250\n",
      "Data columns (total 15 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   User_ID           11251 non-null  int64  \n",
      " 1   Cust_name         11251 non-null  object \n",
      " 2   Product_ID        11251 non-null  object \n",
      " 3   Gender            11251 non-null  object \n",
      " 4   Age Group         11251 non-null  object \n",
      " 5   Age               11251 non-null  int64  \n",
      " 6   Marital_Status    11251 non-null  int64  \n",
      " 7   State             11251 non-null  object \n",
      " 8   Zone              11251 non-null  object \n",
      " 9   Occupation        11251 non-null  object \n",
      " 10  Product_Category  11251 non-null  object \n",
      " 11  Orders            11251 non-null  int64  \n",
      " 12  Amount            11239 non-null  float64\n",
      " 13  Status            0 non-null      float64\n",
      " 14  unnamed1          0 non-null      float64\n",
      "dtypes: float64(3), int64(4), object(8)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "#this will full info of file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f29320e9-b11c-4f0a-9bab-75228cecee56",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Status' ,'unnamed1'], axis= 1, inplace = True)\n",
    "#to delete row/column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a5b14eeb-1244-44a1-9b4c-032267866028",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11251 entries, 0 to 11250\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   User_ID           11251 non-null  int64  \n",
      " 1   Cust_name         11251 non-null  object \n",
      " 2   Product_ID        11251 non-null  object \n",
      " 3   Gender            11251 non-null  object \n",
      " 4   Age Group         11251 non-null  object \n",
      " 5   Age               11251 non-null  int64  \n",
      " 6   Marital_Status    11251 non-null  int64  \n",
      " 7   State             11251 non-null  object \n",
      " 8   Zone              11251 non-null  object \n",
      " 9   Occupation        11251 non-null  object \n",
      " 10  Product_Category  11251 non-null  object \n",
      " 11  Orders            11251 non-null  int64  \n",
      " 12  Amount            11239 non-null  float64\n",
      "dtypes: float64(1), int64(4), object(8)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d6b04c73-2788-48b6-964b-6b26453db11d",
   "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>User_ID</th>\n",
       "      <th>Cust_name</th>\n",
       "      <th>Product_ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>State</th>\n",
       "      <th>Zone</th>\n",
       "      <th>Occupation</th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11246</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11247</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11248</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11249</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11250</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11251 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       User_ID  Cust_name  Product_ID  Gender  Age Group    Age  \\\n",
       "0        False      False       False   False      False  False   \n",
       "1        False      False       False   False      False  False   \n",
       "2        False      False       False   False      False  False   \n",
       "3        False      False       False   False      False  False   \n",
       "4        False      False       False   False      False  False   \n",
       "...        ...        ...         ...     ...        ...    ...   \n",
       "11246    False      False       False   False      False  False   \n",
       "11247    False      False       False   False      False  False   \n",
       "11248    False      False       False   False      False  False   \n",
       "11249    False      False       False   False      False  False   \n",
       "11250    False      False       False   False      False  False   \n",
       "\n",
       "       Marital_Status  State   Zone  Occupation  Product_Category  Orders  \\\n",
       "0               False  False  False       False             False   False   \n",
       "1               False  False  False       False             False   False   \n",
       "2               False  False  False       False             False   False   \n",
       "3               False  False  False       False             False   False   \n",
       "4               False  False  False       False             False   False   \n",
       "...               ...    ...    ...         ...               ...     ...   \n",
       "11246           False  False  False       False             False   False   \n",
       "11247           False  False  False       False             False   False   \n",
       "11248           False  False  False       False             False   False   \n",
       "11249           False  False  False       False             False   False   \n",
       "11250           False  False  False       False             False   False   \n",
       "\n",
       "       Amount  \n",
       "0       False  \n",
       "1       False  \n",
       "2       False  \n",
       "3       False  \n",
       "4       False  \n",
       "...       ...  \n",
       "11246   False  \n",
       "11247   False  \n",
       "11248   False  \n",
       "11249   False  \n",
       "11250   False  \n",
       "\n",
       "[11251 rows x 13 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()\n",
    "#will show false if cell is not null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3d3d1c56-66cc-4a74-b685-1372911ade58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID              0\n",
       "Cust_name            0\n",
       "Product_ID           0\n",
       "Gender               0\n",
       "Age Group            0\n",
       "Age                  0\n",
       "Marital_Status       0\n",
       "State                0\n",
       "Zone                 0\n",
       "Occupation           0\n",
       "Product_Category     0\n",
       "Orders               0\n",
       "Amount              12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n",
    "#will show count of rows having null values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fa4f430c-f75f-4f14-be16-7a9731f35331",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace=True)\n",
    "#will delete all null values\n",
    "#(inplace=True) this will save changes in orginal file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bc077220-ced0-4a1d-a45a-9ecc4f530acd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID               int64\n",
       "Cust_name            object\n",
       "Product_ID           object\n",
       "Gender               object\n",
       "Age Group            object\n",
       "Age                   int64\n",
       "Marital_Status        int64\n",
       "State                object\n",
       "Zone                 object\n",
       "Occupation           object\n",
       "Product_Category     object\n",
       "Orders                int64\n",
       "Amount              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d4992598-33c4-476b-b12a-c63efc468342",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11239, 13)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "924c648f-53f9-422e-baa6-3fbfacaa32d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Amount'] = df['Amount'].astype('int')\n",
    "#will change data type but the condition is that there has to be some value( because null values data types can't be changed)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2b393b71-220d-418a-8346-d01d23e2a35a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User_ID              int64\n",
       "Cust_name           object\n",
       "Product_ID          object\n",
       "Gender              object\n",
       "Age Group           object\n",
       "Age                  int64\n",
       "Marital_Status       int64\n",
       "State               object\n",
       "Zone                object\n",
       "Occupation          object\n",
       "Product_Category    object\n",
       "Orders               int64\n",
       "Amount               int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "57c95935-d440-4540-be6a-f3985ad0f765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Cust_name', 'Product_ID', 'Gender', 'Age Group', 'Age',\n",
       "       'Marital_Status', 'State', 'Zone', 'Occupation', 'Product_Category',\n",
       "       'Orders', 'Amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fc4545a6-b0da-4194-8ce9-39b95aea66dc",
   "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>User_ID</th>\n",
       "      <th>Age</th>\n",
       "      <th>Marital_Status</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.123900e+04</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.003004e+06</td>\n",
       "      <td>35.410357</td>\n",
       "      <td>0.420055</td>\n",
       "      <td>2.489634</td>\n",
       "      <td>9453.610553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.716039e+03</td>\n",
       "      <td>12.753866</td>\n",
       "      <td>0.493589</td>\n",
       "      <td>1.114967</td>\n",
       "      <td>5222.355168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000001e+06</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.001492e+06</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5443.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.003064e+06</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.004426e+06</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12675.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.006040e+06</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>23952.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            User_ID           Age  Marital_Status        Orders        Amount\n",
       "count  1.123900e+04  11239.000000    11239.000000  11239.000000  11239.000000\n",
       "mean   1.003004e+06     35.410357        0.420055      2.489634   9453.610553\n",
       "std    1.716039e+03     12.753866        0.493589      1.114967   5222.355168\n",
       "min    1.000001e+06     12.000000        0.000000      1.000000    188.000000\n",
       "25%    1.001492e+06     27.000000        0.000000      2.000000   5443.000000\n",
       "50%    1.003064e+06     33.000000        0.000000      2.000000   8109.000000\n",
       "75%    1.004426e+06     43.000000        1.000000      3.000000  12675.000000\n",
       "max    1.006040e+06     92.000000        1.000000      4.000000  23952.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n",
    "#will describe values in mathematical standard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c273d2d9-7929-4715-8815-badee37d5c9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Orders</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "      <td>11239.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.410357</td>\n",
       "      <td>2.489634</td>\n",
       "      <td>9453.610553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.753866</td>\n",
       "      <td>1.114967</td>\n",
       "      <td>5222.355168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5443.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8109.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>12675.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>23952.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age        Orders        Amount\n",
       "count  11239.000000  11239.000000  11239.000000\n",
       "mean      35.410357      2.489634   9453.610553\n",
       "std       12.753866      1.114967   5222.355168\n",
       "min       12.000000      1.000000    188.000000\n",
       "25%       27.000000      2.000000   5443.000000\n",
       "50%       33.000000      2.000000   8109.000000\n",
       "75%       43.000000      3.000000  12675.000000\n",
       "max       92.000000      4.000000  23952.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Age','Orders','Amount']].describe()\n",
    "#function to describe specific columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c5ee57-ee20-4f00-a48f-1470a97f7321",
   "metadata": {},
   "source": [
    "***************************\n",
    "****************************"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b7ea3e4-3d6a-4f41-ae33-0ead9b19e59b",
   "metadata": {},
   "source": [
    "****Exploratry Data Analysis****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a5bde7bf-d25a-4ff4-a8e2-dd0b5f101570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = 'Gender', data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9b7883b1-39c3-4ebe-93dc-6202c948947f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "af = sns.countplot(x = 'Gender', data = df)\n",
    "\n",
    "for bars in af.containers:     #for showing values of count as per bars\n",
    "    af.bar_label(bars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbf54c91-3363-4f5e-9050-666362684505",
   "metadata": {},
   "source": [
    "******************************"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bd20a26c-f673-4dfd-b20f-b9d32fb002eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "F    74335853\n",
       "M    31913276\n",
       "Name: Amount, dtype: int32"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'])['Amount'].sum().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bcc05fec-e96a-4390-b97b-219a9c102555",
   "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>Gender</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F</td>\n",
       "      <td>74335853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>31913276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender    Amount\n",
       "0      F  74335853\n",
       "1      M  31913276"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by=['Amount'], ascending=False)\n",
    "#if (as_index=False) is applied then it will only work having (by) argument"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9b619de2-3ad5-47bc-8b08-18e622655500",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Amount'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_gen = df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by=['Amount'], ascending=False)\n",
    "sns.barplot(data = sales_gen, x = 'Gender', y = 'Amount',)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a2c3c42-a6af-4255-a437-6a2b5d2f4216",
   "metadata": {},
   "source": [
    "***from above graphs we can see that most of the buyers are female and even the purchasing power of females are greater than men***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c4a123e-0f83-464c-bded-9df540a0d2dd",
   "metadata": {},
   "source": [
    "***************"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a57eb22f-7be2-4c8f-a70c-a7e2a40e4a88",
   "metadata": {},
   "source": [
    "****AGE*****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "34613048-22ea-41a5-9518-3b6dce479ed3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sa = sns.countplot(data = df, x = 'Age Group')\n",
    "#without (hue)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e50ee486-f1b8-4fb5-b30f-d370a8d58636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#age group vs gender\n",
    "sa = sns.countplot(data = df, x = 'Age Group', hue = 'Gender')\n",
    "#with hue (is like Legend) that differentiate the same data in more categories manner \n",
    "for bars in sa.containers:\n",
    "    sa.bar_label(bars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb6751f-0f23-4589-9fb4-7887625f4c51",
   "metadata": {},
   "source": [
    "**Sales Age**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "68164775-a8f4-4a24-a001-1ffc5a864721",
   "metadata": {},
   "outputs": [],
   "source": [
    "#total amount vs age group\n",
    "sales_age = df.groupby(['Age Group'], as_index=False)['Amount'].sum().sort_values(by=['Amount'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "52b3b1fd-99a9-41f5-b5ba-b03507e24d48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26-35</td>\n",
       "      <td>42613442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36-45</td>\n",
       "      <td>22144994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18-25</td>\n",
       "      <td>17240732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46-50</td>\n",
       "      <td>9207844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>51-55</td>\n",
       "      <td>8261477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>55+</td>\n",
       "      <td>4080987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0-17</td>\n",
       "      <td>2699653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Age Group    Amount\n",
       "2     26-35  42613442\n",
       "3     36-45  22144994\n",
       "1     18-25  17240732\n",
       "4     46-50   9207844\n",
       "5     51-55   8261477\n",
       "6       55+   4080987\n",
       "0      0-17   2699653"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a33f5598-5139-4978-8901-0e7fa57de503",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age Group', ylabel='Amount'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_age = df.groupby(['Age Group'], as_index=False)['Amount'].sum().sort_values(by=['Amount'], ascending=False)\n",
    "sns.barplot(data = sales_age, x ='Age Group', y='Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39eb12b8-6190-4702-b8a6-5dfc764c730f",
   "metadata": {},
   "source": [
    "***From above graphs we can see that most of the buyers are of age group between 26-35 yrs female***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbae27a2-7339-4afe-8a05-d4e30d0a2bb9",
   "metadata": {},
   "source": [
    "****State****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "57f67e06-2e37-4f21-afe0-b45d5391acc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_state = df.groupby(['State'], as_index= False)['Orders'].sum().sort_values(by=['Orders'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6c40b77a-30a4-4fe6-ab22-86022ec6e695",
   "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>State</th>\n",
       "      <th>Orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>4807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>3810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Karnataka</td>\n",
       "      <td>3240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Delhi</td>\n",
       "      <td>2740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>2252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>2051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Himachal Pradesh</td>\n",
       "      <td>1568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Kerala</td>\n",
       "      <td>1137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Haryana</td>\n",
       "      <td>1109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Gujarat</td>\n",
       "      <td>1066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bihar</td>\n",
       "      <td>1062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Jharkhand</td>\n",
       "      <td>953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Uttarakhand</td>\n",
       "      <td>824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Rajasthan</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Punjab</td>\n",
       "      <td>495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Telangana</td>\n",
       "      <td>312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               State  Orders\n",
       "14     Uttar Pradesh    4807\n",
       "10       Maharashtra    3810\n",
       "7          Karnataka    3240\n",
       "2              Delhi    2740\n",
       "9     Madhya Pradesh    2252\n",
       "0     Andhra Pradesh    2051\n",
       "5   Himachal Pradesh    1568\n",
       "8             Kerala    1137\n",
       "4            Haryana    1109\n",
       "3            Gujarat    1066\n",
       "1              Bihar    1062\n",
       "6          Jharkhand     953\n",
       "15       Uttarakhand     824\n",
       "12         Rajasthan     555\n",
       "11            Punjab     495\n",
       "13         Telangana     312"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b450f674-fd97-4be4-b319-6cd9ff45d4a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Orders'>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Top 10 states by orders\n",
    "sales_state = df.groupby(['State'], as_index= False)['Orders'].sum().sort_values(by=['Orders'], ascending=False).head(10)\n",
    "#(.head) function should apply at end\n",
    "\n",
    "sns.set(rc={'figure.figsize':(15,5)}) #(width,height)\n",
    "\n",
    "#RC parameter is used to manage the size of the figure. We are passing the value of the dictionary (key/value pairs) to the parameter by\n",
    "#using the key as {'figure.figsize':(value)} and the dimensions required\n",
    "\n",
    "sns.barplot(data = sales_state, x = 'State', y = 'Orders')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "31eb42f3-4b97-4804-94e7-e49cdea066f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Amount'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Top 10 states by amount\n",
    "sales_state = df.groupby(['State'], as_index= False)['Amount'].sum().sort_values(by=['Amount'], ascending=False).head(10)\n",
    "#(.head) function should apply at end\n",
    "\n",
    "sns.set(rc={'figure.figsize':(15,5)}) #(width,height)\n",
    "\n",
    "#RC parameter is used to manage the size of the figure. We are passing the value of the dictionary (key/value pairs) to the parameter by\n",
    "#using the key as {'figure.figsize':(value)} and the dimensions required\n",
    "\n",
    "sns.barplot(data = sales_state, x = 'State', y = 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74201708-d743-4f75-8dc2-72abf4b060a6",
   "metadata": {},
   "source": [
    "***from above graphs we can see that most of the order ar from Uttar Pradesh, Maharashtra and Karnataka respectively***  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b47008e-d77b-48e4-a7de-1beaab06301f",
   "metadata": {},
   "source": [
    "**MARITAL STATUS**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "2931af77-a123-4019-9778-3035d8fb2790",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['User_ID', 'Cust_name', 'Product_ID', 'Gender', 'Age Group', 'Age',\n",
       "       'Marital_Status', 'State', 'Zone', 'Occupation', 'Product_Category',\n",
       "       'Orders', 'Amount'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c708d877-9ec0-40c3-b8ee-6e4c46150072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#shopping vs marital status\n",
    "svms = sns.countplot(data = df, x= 'Marital_Status')\n",
    "sns.set(rc={'figure.figsize':(1,5)})\n",
    "\n",
    "for bars in svms.containers:\n",
    "    svms.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "01c33ae7-85f6-4094-ba07-0faa105ece5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marital_Status', ylabel='Amount'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Marital status, gender vs amount\n",
    "smga = df.groupby(['Marital_Status', 'Gender'], as_index= False)['Amount'].sum().sort_values(by=['Amount'], ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(5,5)})\n",
    "\n",
    "sns.barplot(data = smga, x = 'Marital_Status',  y = 'Amount',hue = 'Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85555a2f-ec29-46ba-be2d-a921b7cf5a5d",
   "metadata": {},
   "source": [
    "***from above graphs we can see that most of the buyers are married(women) and they have high purchasing power***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "189930dd-7930-4c62-9e0c-a4ba2141153d",
   "metadata": {},
   "source": [
    "**OCCUPATION**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f53cc196-722a-4757-9217-4ed8fc4f1cf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABmUAAAHECAYAAADI/i5zAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAClXklEQVR4nOzdd3iUVd7G8XsmhfRCKKGFKiDSIRJQBCOIgK4i7tpAIYAgVdBQDCggTaoJEAQSAiJKEURF3ZVF17VgFrAg0qu0EEoK6Uwm7x+8jI6hhsnMJPl+rmuvJeec5+R35jj1zjOPoaCgoEAAAAAAAAAAAAAoVkZHFwAAAAAAAAAAAFAWEMoAAAAAAAAAAADYAaEMAAAAAAAAAACAHRDKAAAAAAAAAAAA2AGhDAAAAAAAAAAAgB0QygAAAAAAAAAAANgBoQwAAAAAAAAAAIAdEMoAAAAAAAAAAADYgaujCyipCgoKZDYXOLoMAAAAAAAAAADgYEajQQaD4YbjCGWKyGwu0IULmY4uAwAAAAAAAAAAOFj58t5ycblxKMPXlwEAAAAAAAAAANgBoQwAAAAAAAAAAIAd8PVlAAAAAACgyM6cSdLzzz+ladNmq2XL1pb2F17oo927dxUa//bbCWrcuIkkKSnptBYujNZPP+1QQYFZTZs219ChI1WtWnXL+NTUVC1evEDff/+tsrIy1ahRYw0aNFR33nlX8S8OAADAxghlAAAAAABAkSQlndaoUUOVkZFh1W42m3X48EE980xv3XdfuFVfnTp1JUk5OTl66aUhys/P18iRkXJ3d1dc3NsaNmygVqxYLV9fX5nNZo0bN0qnT5/WoEFDVb58kNaseU/Dhw/SsmWrVKNGiN3WCgAAYAuEMgAAAAAA4JaYzWZ9/vkmLVwYfdX+48d/V05Ojtq2vddyVsxf7dz5s06c+F1vvRWr1q3vliSFhNTSs88+oW+//Vpduz6sH3/crl9/3alZs6LVtu09kqRmzVqoe/cH9OmnH2vQoKHFs0AAAIBiwjVlAAAAAADALTl06IDmzJmhrl27a8KESYX6DxzYJ0mqV6/+NefIy8uTJHl7e1va/P0DJElpaamSpLvuaqJFi+IVGtrGMsbV1VUGg0GXLuXd7jIAAADsjjNlAAAAAADALalcOVirV3+oSpUq68cftxfqP3Bgv3x8fBQTM0ffffeNcnKy1bJlaw0fPkohIbUkSaGhbVSnTl3FxsZo7NgJ8vDwUEzMHHl6eql9+46SJE9PTzVp0kySZDKZdOrUSS1btkQFBQXq1u1v9louAACAzRDKAAAAAACAW+Ln5y8/P/9r9h84sF8ZGRkKCAjU9OmzlZR0WgkJSzV48AAtX/6eKlSoqHLlymn06CiNGTNSTz75mCTJ3d1db745V9WqVS8055w5M/TJJxslSX37DrBcmwYAAKAkcaqvL4uNjVXv3r2t2pKTkzVq1Ci1bt1abdq00csvv6wLFy5Y+s1ms2JiYtS+fXs1a9ZMEREROnbsmNUce/bsUa9evdS8eXN17NhR8fHxdlkPAAAAAABl0YsvDlVsbJyGDBmhZs1aqEuXbpozZ74yMzO0du37kqQff9yu4cMHqV69+po58y3Nnh2ju+8O06uvRuqXX34qNOff/tZD8+cvVt++A7RyZYJmzpxm72UBAADcNqcJZZYvX66YmBirtry8PEVEROj48eNKSEjQ4sWLtXv3bo0ZM8YyJjY2VqtXr9aUKVO0Zs0aGQwGDRgwwPLdtCkpKerbt69q1aql9evXa9iwYYqOjtb69evtuj4AAAAAAMqKO+5ooKZNm1u1VatWXTVr1tbBgwckSStXJqhChYqaNSta7drdq7Cwdpo2bbZq1aqjmJi5hea888671KJFK/XrN1C9evXRpk0blZx8xh7LAQAAsBmHhzJnzpxR//79FR0drdq1a1v1bdq0SSdPntSiRYvUpEkTNW/eXK+++qqOHDmijIwM5eXladmyZRo2bJg6dOighg0bat68eTpz5ow2b94sSVq7dq3c3d01ceJE1a1bVz179lSfPn20dOlSRywXAAAAAIBSzWQy6bPPPtGuXb8W6svNzVFAQIAkKSnptBo2bCR3d3dLv9FoVLNmLXTkyGFJ0uHDh/Tppx8Xmqdhw0YqKChQcnJy8SwCAACgmDg8lPntt9/k7++vjz/+WM2aNbPq++abbxQWFqYKFSpY2tq3b69///vf8vHx0d69e5WZmamwsDBLv5+fnxo1aqRt27ZJkrZv367Q0FC5uv5x+ZywsDAdOXJE58+fL+bVAQAAAABQtri6uio+frEWLbL+Nox9+/bq5MkTatGilSSpZs1a2rPnN8s3XUhSQUGBfvttp6pWrSpJ2r17l6ZPn6xff/3Faq7ExK1yd3dXzZq1incxAAAANuZ64yHFKzw8XOHh4VftO3r0qFq3bq2FCxdq48aNMplMuvfeexUZGSk/Pz8lJSVJkqpUqWJ1XKVKlXT69GlJUlJSkurXr1+oX5JOnTqloKCgItfu6urwTAsAAAAAAIdycTFa/v/K++T+/V/Q1KmTNW3aJHXp0lWnT5/S0qVvq169O/TII3+Tq6tR/fq9oIEDIxQZOVxPPvmsXF1d9MknH2nXrl81depMuboa1aXLQ1q9+l1NmhSlAQNeVIUKFfTf//5HGzd+oEGDhigw0N+RSwcAALhlDg9lricjI0MbN25U27ZtNWfOHKWlpWn69OkaPHiwVq5cqezsbEmyOtVZksqVK6e0tDRJUk5OzlX7JSk3N7fItRmNBgUGehf5eAAAAAAASgNfXw/L/195n/zcc88qKChA8fHxGjv2ZXl6eqpz584aNWqUAgIuBynt2oXq3XffVXR0tCZOjJKbm5saNGigd955R3fffbckKTDQW+++u1Lz5s3TkiWxSklJUd26dTVjxgw99thjDlkvAADA7XDqUMbNzU1eXl6aM2eO3NzcJEn+/v76+9//rl9//VUeHpdf+OXl5Vn+LV0OWzw9PSVJHh4eVqdCX+mXJC8vryLXZjYXKD09q8jHAwAAAABQGtSv31g//PCjJCklJdPS3q5dR7Vr19FqbEGB9ZiQkHqaM2d+oTn/PMbV1UuRkVGKjLz2GAAAAEfz8/O0nEF8PU4dygQHB8tsNlsCGUm64447JEknTpxQ9erVJUnJyckKCQmxjElOTlbDhg0tc/z1wn9Xfq5cufJt1WcymW/reAAAAAAAAAAAUHY4dSjTunVrvfPOO8rJybGcCbN//35JUs2aNXXHHXfIx8dHiYmJllAmPT1du3fvVq9evSRJoaGhWr16tfLz8+Xi4iJJ2rp1q2rXrn1b15MBAAAAAMAZGY0GGY0GR5dRKpjNBTKbCxxdBgAAKEWcOpR56qmntGrVKr388ssaMWKELl68qIkTJ6pNmza66667JEm9evXS7NmzVb58eVWrVk2zZs1ScHCwOnfuLEnq2bOn4uLiFBUVpf79+2vnzp1asWKFJk2a5MilAQAAAABgc0ajQQGBXnIx3virM3Bj+WazUlOyCGYAAIDNOHUoU758ea1atUrTp0/XP/7xD7m7u6tTp04aN26cZczw4cNlMpk0fvx45eTkKDQ0VPHx8XJ3d5ckBQUFKS4uTlOnTlWPHj1UsWJFjR49Wj169HDUsgAAAAAAKBZGo0EuRqPm/W+fTlzkOqi3o7qvl0be3UBGo4FQBgAA2IyhoKCAVxZFkJ9v1oULXFQQAAAAAOA8XF2NCgz01stbftLhVN6z3o46Ad6a80ALpaRkck1ZAABwQ+XLe8vF5cZnK3M+MwAAAAAAAAAAgB0QygAAAAAAAAAAANgBoQwAAAAAAAAAAIAdEMoAAAAAAAAAAADYAaEMAAAAAAAAAACAHRDKAAAAAAAAAAAA2AGhDAAAAAAAAAAAgB0QygAAAAAAAAAAANgBoQwAAAAAAAAAAIAdEMoAAAAAAAAAAADYAaEMAAAAAAAAAACAHRDKAAAAAAAAAAAA2AGhDAAAAAAAAAAAgB0QygAAAAAAAAAAANgBoQwAAAAAAAAAAIAdEMoAAAAAAAAAAADYAaEMAAAAAAAAAACAHRDKAAAAAAAAAAAA2AGhDAAAAAAAAAAAgB0QygAAAAAAAAAAANgBoQwAAAAAAAAAAIAdEMoAAAAAAAAAAADYAaEMAAAAAAAAAACAHRDKlDJnziTpoYc66scft19zzNq17+vee1vr9OlTVu1ZWZmaNWua/va3LurU6V699NJgHTly2GpMdna2Fi6M1hNPPKLOne/TwIF9tX37/4plLQAAAAAAAAAAlCaEMqVIUtJpjRw5RBkZGdccc/z471q8eMFV+yZOjNLXX3+lQYOGasKEyUpJSdGIES8qPT3NMmbWrGn68MN1+sc/ntG0aTNVsWJFvfzyMO3a9avN1wMAAAAAAAAAQGlCKFMKmM1mffrpx4qI6KXU1NRrjsvPz9fUqRPl7x9QqG/Xrp36/vtvFRX1urp1e0QdOoTrrbdilZ2dpQ0b1kmScnNztGXLF3ryyWf1j388rdDQME2cOE1BQRX08ccbiml1AAAAAAAAAACUDoQypcChQwc0Z84Mde3aXRMmTLrmuPffX6kLF87r2WefL9SXmLhVnp6eCg0Ns7QFBgaqefOW+uGH7yVJly6ZZDab5e3tbRnj6uoqb29vpaWl2m5BAAAAAAAAAACUQoQypUDlysFavfpDDRs2SuXKeVx1zOHDh7Rs2VKNG/eaPD09C/UfO3ZUVatWk6urq1V7tWo1dPz4MUmSj4+PHn74Ua1bt1q7du3UxYsX9f777+rIkcN68MFutl8YAAAAAAAAAACliOuNh8DZ+fn5y8/P/5r9JpNJU6dO1COPPKoWLVrp9OlThcZkZFyUl5d3oXYvLy9lZmZafu7Xb5D27durQYMiLG39+w/SAw90vs1VAAAAAAAAAABQuhHKlAHvvLNMFy+ma9CgYdccYzabZTAYCrUXFBTIaLx8QlVKygW98MLzcnV11YQJk1WxYiVt3fqdEhKWysPDQ0891avY1gAAAAAAAAAAQElHKFPK7d+/VytXJmjWrGi5ubnJZLp8XRjpchCTn58vFxcX+fj46vjx3wsdn52dLW9vH0nSJ59sVHLyGb3//gbVqBEiSWrZsrUkafHihera9WH5+wfYZ2EAAAAAAAAAAJQwhDKl3DfffK1Lly7ppZcGF+p78snH1Lx5Sy1YsEQhITX1v//9ILPZbDkzRpJOnjyuWrVqS5KSkk4rMLC8JZC5onnzlnr//ZU6ceIEoQwAAAAAAAAAANdAKFPKPfro47rnnvZWbd99940SEpZqxoy5Cgm5HLDcfXeY3nlnmRITt6pt23skSSkpKfr55x/13HOXrx9Ts2YtffLJRv3++1GFhNSyzPfrr7/IaDQqODjYPosCAAAAAAAAAKAEIpQp5SpUqKgKFSpatR0+fEiSVLduPVWpUlXS5bNdWrRopcmTJ2jw4OHy8/PXsmVL5OPjq0cf7SlJevjhR7V+/Vq9/PIIRUQMUKVKlbVtW6Lef3+lHn/87woKqmDfxQEAAAAAAAAAUIIYbzzEfmJjY9W7d+9r9o8fP17h4eFWbWazWTExMWrfvr2aNWumiIgIHTt2zGrMnj171KtXLzVv3lwdO3ZUfHx8sdRf0k2dOkvt23fQwoXRmjZtoipWrKjo6Fj5+flJkry9fRQbG69mzZpr4cK3NGbMSP3ww/d6+eWxGjHiFQdXDwAAAAAAAACAczMUFBQUOLoISVq+fLlmzJih0NBQrVy5slD/v//9bw0ZMkTVqlXTl19+aWlfsGCB3nvvPU2fPl2VK1fWrFmzdPz4cW3atEnu7u5KSUlR165d1alTJ/Xt21c///yzJk2apNdff109e/Yscr35+WZduJBZ5OMBAAAAALA1V1ejAgO99fKWn3Q4lfest6NOgLfmPNBCKSmZMpnMji4HAAA4ufLlveXicuPzYBz+9WVnzpxRVFSUduzYodq1a191THJysiZMmKC7775bJ0+etLTn5eVp2bJlioyMVIcOHSRJ8+bNU/v27bV582Z1795da9eulbu7uyZOnChXV1fVrVtXx44d09KlS28rlLlZRqNBRqOh2H9PWWA2F8hsdooMEQAAAAAAAACAW+bwUOa3336Tv7+/Pv74Yy1cuNAqdJGkgoICjR07Vo8++qi8vb314YcfWvr27t2rzMxMhYWFWdr8/PzUqFEjbdu2Td27d9f27dsVGhoqV9c/lhoWFqbFixfr/PnzCgoKKra1GY0GlQ/0ksHoVN8SV2IVmM26kJJFMAMAAAAAAAAAKJEcHsqEh4cXuk7Mny1fvlxnz57V22+/rcWLF1v1JSUlSZKqVKli1V6pUiWdPn3aMqZ+/fqF+iXp1KlTtxXKuLpeP2xxcTHKYDQq/d/fKj8lvci/B5JLoJ/8Ot0rNzcX5edz2jgAAAAAXM3NfGUGbg23KQAAsCWHhzLXs3fvXi1YsECrVq2Su7t7of7s7GxJKtRXrlw5paWlSZJycnKu2i9Jubm5Ra7NaDQoMND7psbmp6TLdO5CkX8X/uDn5+noEgAAAAAAZQjvQwEAgC05bSiTm5urV155RS+++KIaNmx41TEeHh6SLl9b5sq/rxzr6elpGZOXl1dobkny8vIqcn1mc4HS07OuO8bFxciLNxtLT8/mTBkAAAAAuAbeh9oe70MBAMDN8PPzvKkzbJ02lPnll1904MABLViwQAsXLpQkXbp0SSaTSS1atNCkSZNUq1YtSVJycrJCQkIsxyYnJ1uCnODgYCUnJ1vNfeXnypUr31aNJhMvyuwtP9/M7Q4AAAAAsBvehwIAAFty2lCmadOm+uKLL6zaVq5cqS+++EIrV65UUFCQ3N3d5ePjo8TEREsok56ert27d6tXr16SpNDQUK1evVr5+flycXGRJG3dulW1a9e+revJAAAAAAAAAAAA3AqnDWU8PDxUs2ZNqzZ/f3+5urpatffq1UuzZ89W+fLlVa1aNc2aNUvBwcHq3LmzJKlnz56Ki4tTVFSU+vfvr507d2rFihWaNGmSXdcDAAAAAAAAAADKNqcNZW7W8OHDZTKZNH78eOXk5Cg0NFTx8fFyd3eXJAUFBSkuLk5Tp05Vjx49VLFiRY0ePVo9evRwcOUAAAAAAAAAAKAsMRQUFBQ4uoiSKD/frAsXMq87xtXVqMBAb6Ws+0ymcxfsVFnp5FqhvAL/3k0pKZl8ly8AAAAAXMOV96Evb/lJh1Ov/54V11cnwFtzHmjB+1AAAHBTypf3louL8YbjbjwCAAAAAAAAJc6ZM0l66KGO+vHH7Vbt//3vfxQR0UudOt2rnj0fVnz8Yl26dOma88yfP1dDh75w1b7Vq9/VP/7xqMLD26lPn2f0zTf/sd0CAAAohQhlAAAAAAAASpmkpNMaOXKIMjIyrNq3bv1WUVGRuuOO+po+fY6eeaa3Vq9epblzZ151nnffXa41a967at97772jRYvmq1u3RzR16ixVr15d48eP0c8//2jz9QAAUFqU+GvKAAAAAAAA4DKz2azPP9+khQujr9q/cuVy3XnnXRo37jVJUmhoG6Wmpuqdd5Zp+PBR8vT0lCSdOnVS8+fP0/fffyMfH59C8+Tm5uiddxL05JPPqE+f/pKksLB2GjQoQgkJcYqOji2mFQIAULJxpgwAAAAAAEApcejQAc2ZM0Ndu3bXhAmTCvVHRU3U+PETrdrc3NxkNptlMpksbfPnz9XJk8cVHb1I9erVLzTPb7/tUkbGRd13X7ilzWAwqEOH+/XzzzuUm5tju0UBAFCKcKYMAAAAAABAKVG5crBWr/5QlSpVLnQtGUmqVq265d8ZGRnavj1R77//rh588CH5+vpa+gYMeFG1a9eVwWC46u85duyoJCkkJOQv89dQfn6+Tp48oTp16tlgRQAAlC6EMgAAAAAAAKWEn5+//Pz8bzju7Nlk9ejRTZJUpUo1RUQMtOq/UaCSkXFRkuTl5W3V7uXlJUnKzMy86ZoBAChL+PoyAAAAAACAMsbDw1PR0Ys0deos+fv7q1+/3jpy5PBNH282m6/RUyBJMhj4yAkAgKvhGRIAAAAAAKCM8fX1VatWoerQ4X7NnbtAUoHWrn3vpo/38bn8VWdZWVlW7VlZ2f/f72OzWgEAKE0IZQAAAAAAAMoAk8mkLVu+0P79e63a/fz8VLVqdZ05c+am5woJqSlJOnnyuFX7yZPH5e7urqpVq91+wQAAlEKEMgAAAAAAAGWAq6urYmNjtGjRfKv2pKQkHTt2RPXq3XHTczVp0kyenp766qstlraCggJ9/fVXat68pdzd3W1WNwAApYmrowsAAAAAAACAfUREvKDp0yfrzTenKDy8s86dO6vly+Pk7x+gp5/uddPzeHh46Kmnemn58ji5ubmpceOm+vTTj7Vv3x7FxLxdjCsAAKBkI5QBAAAAAAAoI7p3/5s8Pb20atUKbd78T3l4eKhNm3YaNGioAgPL39JcEREvyMXFRR9//KHef/9d1apVWzNmzFXTps2Lp3gAAEoBQ0FBQYGjiyiJ8vPNunAh87pjXF2NCgz0Vsq6z2Q6d8FOlZVOrhXKK/Dv3ZSSkimTyezocgAAAADAKV15H/rylp90OPX671lxfXUCvDXngRa8DwUAADelfHlvubjc+IoxnCkDAAAAAABgB0ajQUajwdFllApmc4HMZv7OGABQ8hDKAAAAAAAAFDOj0aCAQG+5EMrYRL65QKkpmQQzAIASh1AGAAAAAACgmBmNBrkYDXpn23klXbzk6HJKtGBfNz0XGiSj0UAoAwAocQhlAAAAAAAA7CTp4iWdSCOUAQCgrLrxVWcAAAAAAAAAAABw2whlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADswKlCmdjYWPXu3duq7csvv1TPnj3VokULhYeH680331ROTo6l32w2KyYmRu3bt1ezZs0UERGhY8eOWc2xZ88e9erVS82bN1fHjh0VHx9vl/UAAAAAAAAAAABc4TShzPLlyxUTE2PVtn37dg0dOlRdunTRxo0bNXHiRH3++eeaNGmSZUxsbKxWr16tKVOmaM2aNTIYDBowYIDy8vIkSSkpKerbt69q1aql9evXa9iwYYqOjtb69evtuj4AAAAAAAAAAFC2OTyUOXPmjPr376/o6GjVrl3bqm/16tUKCwvTCy+8oJo1a+q+++7TyJEj9fHHHysvL095eXlatmyZhg0bpg4dOqhhw4aaN2+ezpw5o82bN0uS1q5dK3d3d02cOFF169ZVz5491adPHy1dutQRywUAAAAAAAAAAGWUq6ML+O233+Tv76+PP/5YCxcu1MmTJy19ERERMhoL50Ymk0kZGRk6ceKEMjMzFRYWZunz8/NTo0aNtG3bNnXv3l3bt29XaGioXF3/WGpYWJgWL16s8+fPKygoqMi1u7peP9NycXF45lXqcJsCAAAAwLXxnsn2bHWbsje2x20KACiJHB7KhIeHKzw8/Kp9jRo1svo5Ly9PCQkJuuuuu1S+fHlt375dklSlShWrcZUqVdLp06clSUlJSapfv36hfkk6depUkUMZo9GgwEDvIh2LovPz83R0CQAAAACAMoT3oc6LvQEAlEQOD2Vulslk0ujRo3Xw4EGtWrVKkpSdnS1Jcnd3txpbrlw5paWlSZJycnKu2i9Jubm5Ra7HbC5QenrWdce4uBh5gWBj6enZys83O7oMAAAAlFJnziTp2Wf/oTffnKtWrVrfdN/XX3+lZcuW6tixowoICFD37o+oT5/+cnNzkyS9+OIA/fTTjmv+3h9++NH2i0GZxPtQ27PV+1D2xvb4jAAA4Ez8/Dxv6izOEhHKZGRk6KWXXlJiYqJiYmLUrFkzSZKHh4eky2fQXPm3dDls8fT0tIzJy8uzmu9KGOPl5XVbdZlMPPHbW36+mdsdAAAAxSIp6bRGjRqqjIyMQq87r9e3deu3Gjv2FXXr9ogGDRqm338/qrffXqjk5HMaMyZKkjRq1BhlZmZa/b5Tp05oypTX9be/9eA1LuDEeB/qvNgbAEBJ5PShTHJysgYMGKATJ05o6dKlVtePufK1ZcnJyQoJCbE6pmHDhpKk4OBgJScnF5pTkipXrlzc5QMAAABwcmazWZ9/vkkLF0bfUt8VK1cu15133qVx416TJIWGtlFqaqreeWeZhg8fJU9PT9WuXcfqGJPJpLfemqV69e7QiBGv2HZBAAAAAJyWU18RLS0tTc8//7wuXLig9957zyqQkaSGDRvKx8dHiYmJlrb09HTt3r1brVtf/jqB0NBQ7dixQ/n5+ZYxW7duVe3atYt8PRkAAAAApcehQwc0Z84Mde3aXRMmTLrpviuioiZq/PiJVm1ubm4ym80ymUxXPeajj9Zr//69euWVcZavOAMAAABQ+jn1mTLTp0/X8ePHFRcXp/Lly+vs2bOWvvLly8vd3V29evXS7NmzVb58eVWrVk2zZs1ScHCwOnfuLEnq2bOn4uLiFBUVpf79+2vnzp1asWKFJk26+hsqAAAAAGVL5crBWr36Q1WqVFk//rj9pvuuqFatuuXfGRkZ2r49Ue+//64efPAh+fr6FhqflZWl+Pgl6tKlmxo1amzbxQAAAABwak4bypjNZn322We6dOmSnn/++UL9W7ZsUfXq1TV8+HCZTCaNHz9eOTk5Cg0NVXx8vNzd3SVJQUFBiouL09SpU9WjRw9VrFhRo0ePVo8ePey9JAAAAABOyM/PX35+/rfc91dnzyarR49ukqQqVaopImLgVcdt2vSRMjIuqnfvvkUrGAAAAECJ5VShzIwZMyz/NhqN2rlz5w2PcXFxUWRkpCIjI685pmnTplqzZo1NagQAAACAq/Hw8FR09CJlZGRo5coE9evXW4sWxRe6nsyGDet0zz33KSSkpoMqBQAAAOAoTn1NGQAAAAAoKXx9fdWqVag6dLhfc+cukFSgtWvfsxpz4MB+nTjxux588CHHFAkAAADAoQhlAAAAAKCITCaTtmz5Qvv377Vq9/PzU9Wq1XXmzBmr9u+//0YeHh5q1+5ee5YJAAAAwEkQygAAAABAEbm6uio2NkaLFs23ak9KStKxY0dUr94dVu27d+9S/foNVa6chz3LBAAAAOAkCGUAAAAA4DZERLygbdsS9eabU7RtW6I+/3yTRowYJH//AD39dC+rsYcPH1KtWrUdVCkAAAAAR3N1dAEAAAAAUJJ17/43eXp6adWqFdq8+Z/y8PBQmzbtNGjQUAUGlrcae+HCefn6+jmoUgAAAACORigDAAAAAP+vZcvW+vbb7bfcFx7eSeHhnW44/5Yt391WfQAAAABKNkIZAAAAAE7JaDTIaDQ4uoxSwWwukNlc4OgyAAAAgDKPUAYAAACA0zEaDQoI9JKLkctg2kK+2azUlCyCGQAAAMDBCGUAAAAAOB2j0SAXo1Gz/veVjl9MdXQ5JVoN3wBF3n2/jEYDoQwAAADgYIQyAAAAAJzW8YupOpR63tFlAAAAAIBN8F0AAAAAAAAAAAAAdkAoAwAAAAAAAAAAYAeEMgAAAAAAAAAAAHZAKAMAAAAAAAAAAGAHhDIAAAAAAAAAAAB2QCgDAAAAAAAAAABgB4QyAAAAAAAAAAAAdkAoAwAAAAAAAAAAYAeEMgAAAAAAAAAAAHZAKAMAAAAAAAAAAGAHhDIAAAAAAAAAAAB2QCgDAAAAAAAAAABgB4QyAAAAAAAAAAAAdkAoAwAAAAAAAAAAYAeEMgAAAAAAAAAAAHZAKAMAAAAAAAAAAGAHhDIAAAAAAAAAAAB2QCgDAAAAAAAAAABgB4QyAAAAAAAAAAAAdkAoAwAAAAAAAAAAYAeEMgAAAAAAAAAAAHZAKAMAAAAAAAAAAGAHhDIAAAAAAAAAAAB2QCgDAAAAAAAAAABgB04VysTGxqp3795WbXv27FGvXr3UvHlzdezYUfHx8Vb9ZrNZMTExat++vZo1a6aIiAgdO3bsluYAAAAAAAAAAAAobk4TyixfvlwxMTFWbSkpKerbt69q1aql9evXa9iwYYqOjtb69estY2JjY7V69WpNmTJFa9askcFg0IABA5SXl3fTcwAAAAAAAAAAABQ3V0cXcObMGUVFRWnHjh2qXbu2Vd/atWvl7u6uiRMnytXVVXXr1tWxY8e0dOlS9ezZU3l5eVq2bJkiIyPVoUMHSdK8efPUvn17bd68Wd27d7/hHAAAAAAAAAAAAPbg8DNlfvvtN/n7++vjjz9Ws2bNrPq2b9+u0NBQubr+kR2FhYXpyJEjOn/+vPbu3avMzEyFhYVZ+v38/NSoUSNt27btpuYAAAAAAAAAAACwB4efKRMeHq7w8PCr9iUlJal+/fpWbZUqVZIknTp1SklJSZKkKlWqFBpz+vTpm5ojKCioyLW7ul4/03JxcXjmVepwmwIAAJQNvO6zPW7TsoF9tj1b3absje1xmwIASiKHhzLXk5OTI3d3d6u2cuXKSZJyc3OVnZ0tSVcdk5aWdlNzFJXRaFBgoHeRj0fR+Pl5OroEAAAAoETitTRQNNx3nBd7AwAoiZw6lPHw8FBeXp5V25UgxcvLSx4eHpKkvLw8y7+vjPH09LypOYrKbC5QenrWdce4uBh5gWBj6enZys83O7oMAAAAFDNeS9ser6XLBu47tmer+w57Y3s8rgEAnImfn+dNncXp1KFMcHCwkpOTrdqu/Fy5cmWZTCZLW0hIiNWYhg0b3tQct8Nk4onf3vLzzdzuAAAAQBHwWhooGu47zou9AQCURE795ZuhoaHasWOH8vPzLW1bt25V7dq1FRQUpIYNG8rHx0eJiYmW/vT0dO3evVutW7e+qTkAAAAAAAAAAADswalDmZ49eyojI0NRUVE6ePCgNmzYoBUrVmjgwIGSLl9LplevXpo9e7a2bNmivXv3auTIkQoODlbnzp1vag4AAAAAAAAAAAB7cOqvLwsKClJcXJymTp2qHj16qGLFiho9erR69OhhGTN8+HCZTCaNHz9eOTk5Cg0NVXx8vNzd3W96DgAAAAAAAAAAgOLmVKHMjBkzCrU1bdpUa9asueYxLi4uioyMVGRk5DXH3GgOAAAAAAAAAACA4ubUX18GAAAAAAAAAABQWhDKAAAAAAAAAAAA2AGhDAAAAAAAAAAAgB0QygAAAAAAAAAAANgBoQwAAAAAAAAAAIAdEMoAAAAAAAAAAADYAaEMAAAAAAAAAACAHRDKAAAAAAAAAAAA2AGhDAAAAAAAAAAAgB0QygAAAAAAAAAAANgBoQwAAAAAAAAAAIAdEMoAAAAAAAAAAADYgaujCwDKoo8//lBr176vpKRTqlw5WI8//g89/vjfZTAYJEkvvNBHu3fvKnTc228nqHHjJoXa9+7do4ED+2jMmPHq1u2RYq8fAAAAAAAAAHDrCGUAO/vkk42aOXOqnnjiSd17bwf9/POPeuutWcrNzdUzz/SW2WzW4cMH9cwzvXXffeFWx9apU7fQfHl5eZo69XXl5+fbawkAAAAAAAAAgCIoUiizceNGdejQQYGBgYX6zp49q40bN2rAgAG3XRxQGn366cdq0qSZXnopUpLUuvXdOn78mD78cJ2eeaa3jh//XTk5OWrb9t6rnhXzV3Fxi5SZmVncZQMAAAAAAAAAblORrikzbtw4HT9+/Kp9e/bsUUxMzG0VBZRmeXl58vHxsWrz9w9QWlqaJOnAgX2SpHr16t9wrl27duqDD9Zq1KjRti8UAAAAAAAAAGBTN32mzMCBA3Xw4EFJUkFBgYYMGSJ3d/dC486fP6+QkBDbVQiUMk8++YymTZukf/3rM91zz3367bdf9fnnn+qhh7pLkg4c2C8fHx/FxMzRd999o5ycbLVs2VrDh49SSEgtyzy5uTmaMmWinnuur+rWvcNBqwEAAAAAAAAA3KxbCmXWrVsnSfrwww/VqFEjlS9f3mqM0WiUn5+fHn/8cdtWCZQi99/fSTt2bNMbb7xmabv77rYaMeJlSZdDmYyMDAUEBGr69NlKSjqthISlGjx4gJYvf08VKlSUJC1aNF9eXp7q1auPzp5NdshaAAAAAAAAAAA376ZDmZYtW6ply5aWnwcPHqwaNWoUS1FAaTZ27Mv69ddfNHjwcN155106dOiAli1bqgkTxmjatNl68cWh6tOnn5o2bS5JatashRo3bqpevf6utWvf1+DBw/Xjj9v18ccfasmSFXJ1LdKloQAAAAAAAAAAdlakT3OnT59u6zqAMuHXX3/R//63VWPGjNcjjzwmSWrRopWqVq2u0aNf0vfff6t77mlf6Lhq1aqrZs3aOnjwgLKysjR9+mQ9++zzqlWrtkwmk8xmsyTJbDbLZDIR1AAAAAAAAACAEyrSJ7cXLlzQ1KlT9Z///EfZ2dkqKCiw6jcYDNq9e7dNCgRKk6Sk05KkJk2aWbU3b375LLQjRw4pLS1VISG11LhxE6sxubk5CggI0N69u3X69CklJCxVQsJSqzEzZryhGTPe0Lffbi/GVQAAAAAAAAAAiqJIoczEiRP19ddfq3v37goODpbRaLR1XUCpVLNmLUnSL7/8pFq1alvaf/31F0lSlSrVFBsbreDgKlq48I/AZd++vTp58oSeeeY5NWx4p+Li3rGa99y5cxo7dpT69h1w1TNtAAAAAAAAAACOV6RQ5ptvvtGrr76qJ5980tb1AKVa/foN1bFjuBYsmKeLF9PVqFFjHTlyWAkJS1S/fkN16HC/srOzNGPGG5o6daIefPAhnT59WvHxb6tu3Xrq2vVhubq6qmHDRlbznj59SpJUpUrVQn0AAAAAAAAAAOdQpFDG3d1dNWrUsHUtQJnw+utTtWJFvD76aIPi4xercuVgdev2iPr0GSBXV1c9/PCj8vDw0HvvrdS4ca/Iw8NT993XUYMGDeVaMQAAAAAAAABQghXpE97OnTtr06ZNateuna3rAUo9Nzc39e8/SP37D7rmmE6duqhTpy43PWeVKlW5jgwAAAAAAAAAOLkihTKNGjXSW2+9pePHj6tZs2by8PCw6jcYDBoyZIhNCgSKi9FokNFocHQZpYLZXCCzucDRZQAAAAAAAACAUytSKDN58mRJ0rZt27Rt27ZC/YQycHZGo0HlA71kMBodXUqpUGA260JKFsEMAAAAAAAAAFxHkUKZvXv32roOwK6MRoMMRqNSNr8r04Uzji6nRHMtX1mBnXvJaDQQygAAAAAAAADAdXDVcJRppgtndOncSUeXAQAAAAAAAAAoA4oUyowbN+6GY6ZPn16UqQEAAAAAAAAAAEqlIoUyiYmJhdqysrKUmpqqgIAANWnS5LYLAwAAAAAAAAAAKE2KFMp8+eWXV20/fPiwhg0bpscee+x2agIAAAAAAAAAACh1jLacrE6dOhoyZIgWLFhgy2kBAAAAAAAAAABKPJuGMpLk4+Ojkye5cDoAAAAAAAAAAMCfFSmUOXXqVKH/HT9+XNu2bdNbb72lunXr2rTIS5cuad68eerYsaNatGihZ555Rj/++KOlf8+ePerVq5eaN2+ujh07Kj4+3up4s9msmJgYtW/fXs2aNVNERISOHTtm0xoBAAAAAAAAAACup0jXlAkPD5fBYCjUXlBQIE9PT82fP/+2C/uzRYsWaf369ZoxY4Zq1KihpUuXasCAAfrss8/k7u6uvn37qlOnTpo0aZJ+/vlnTZo0SQEBAerZs6ckKTY2VqtXr9b06dNVuXJlzZo1SwMGDNCmTZvk7u5u01oBAAAAAAAAAACupkihzLRp0wqFMgaDQT4+PgoLC5OPj49Nirtiy5Ytevjhh3XvvfdKksaOHat169bp559/1tGjR+Xu7q6JEyfK1dVVdevW1bFjx7R06VL17NlTeXl5WrZsmSIjI9WhQwdJ0rx589S+fXtt3rxZ3bt3t2mtAAAAAAAAAAAAV1OkUObxxx+3dR3XFRAQoK+++kq9evVSlSpVtGbNGrm7u+vOO+/UBx98oNDQULm6/rGUsLAwLV68WOfPn9fJkyeVmZmpsLAwS7+fn58aNWqkbdu2EcoAAAAAAAAAAAC7KFIoI0kXLlxQQkKCEhMTlZ6ersDAQLVu3Vp9+vRRUFCQLWtUVFSURo4cqQceeEAuLi4yGo2Kjo5WSEiIkpKSVL9+favxlSpVknT52jdJSUmSpCpVqhQac/r06duqy9X1+pfkcXEp0iV7cB22uk3ZG9vjNgUAALbEawvb4zYtG9hn2+N9qPPiNgUAlERFCmWSkpL05JNP6sKFC2revLkaNWqks2fPKiEhQRs3btQHH3ygypUr26zIQ4cOyc/PTwsXLlTlypW1bt06jRkzRu+++65ycnIKXRemXLlykqTc3FxlZ2dL0lXHpKWlFbkmo9GgwEDvIh+PovHz83R0CbgG9gYAAMC58XoNKBruO86LvQEAlERFCmVmzZolV1dXffbZZ6pRo4al/fjx44qIiNC8efM0Y8YMmxR48uRJRUZGavny5WrdurUkqUmTJjp48KDmz58vDw8P5eXlWR2Tm5srSfLy8pKHh4ckKS8vz/LvK2M8PYv+5G02Fyg9Peu6Y1xcjLxAsLH09Gzl55tvex72xvZstTcAAAASr9eKA6/XygbuO7bH+1DnxeMaAMCZ+Pl53tRZnEUKZb799lu9+uqrVoGMJNWoUUNDhgzRzJkzizLtVe3cuVOXLl1SkyZNrNqbNWum//73v6pataqSk5Ot+q78XLlyZZlMJktbSEiI1ZiGDRveVm0mE0/89pafb+Z2d1LsDQAAgHPj9RpQNNx3nBd7AwAoiYr05Zv5+fkKDAy8al/58uWVkZFxW0X92ZVrwezbt8+qff/+/apZs6ZCQ0O1Y8cO5efnW/q2bt2q2rVrKygoSA0bNpSPj48SExMt/enp6dq9e7flzBsAAAAAAAAAAIDiVqRQpkGDBvroo4+u2rdx40bVr1//tor6s6ZNm6p169YaM2aMfvjhBx09elRvvfWWtm7dqhdeeEE9e/ZURkaGoqKidPDgQW3YsEErVqzQwIEDJV2+lkyvXr00e/ZsbdmyRXv37tXIkSMVHByszp0726xOAAAAAAAAAACA6ynS15cNHjxY/fr1U2pqqh555BFVqFBB586d0yeffKLvv/9eMTExNivQaDQqNjZWb731lsaNG6e0tDTVr19fy5cvV/PmzSVJcXFxmjp1qnr06KGKFStq9OjR6tGjh2WO4cOHy2Qyafz48crJyVFoaKji4+Pl7u5uszoBAAAAAAAAAACup0ihzD333KOZM2dq5syZ+u677yztFStW1PTp021+Boq/v79ef/11vf7661ftb9q0qdasWXPN411cXBQZGanIyEib1gUAAAAAAAAAAHCzihTKSNLJkyfVoEEDrVixQmlpadq7d6+io6OVmppqw/IAAAAAAAAAAABKhyKFMnFxcVqwYIGee+451a1bV5JUtWpV/f7775ozZ448PT315JNP2rRQAAAAAAAAoLT48cftGj580DX7IyJeUETEC/r996OaP3+edu78WS4urmrfvoOGDh0pX19fy9iDBw9o0aL5+u23nTIaXdSu3b0aNGiYKlSoYI+lAABuQZFCmbVr12rkyJHq37+/pS04OFhjx45V+fLl9c477xDKAAAAAAAAANfQoEFDvf12QqH2pUsXae/e39SpUxddvHhRI0YMVoUKFTRhwmRduHBBsbExSk4+o3nzFkqSkpPPaMSIQapRo6Zee22KcnJytGTJQr300mAtX/6eXF2L/EU5AIBiUKRH5TNnzuiuu+66al+TJk20cOHC2yoKAAAAAAAAKM28vX3UuHETq7ZvvvmPduz4n954Y4ZCQmpq5coEXbyYrmXLVikwMFCSVLFiJUVGjtAvv/ysZs2a66OPNig7O0czZ86Tn5+/JCkgIEDDhw/Sjh3b1KZNWzuvDABwPcaiHFSjRg19//33V+1LTExUcHDwbRUFAAAAAAAAlCW5uTmaN2+W2rW7V/ff30mSlJi4VU2btrAEMpLUpk1beXl564cfvpMkPfHEU4qNXWoJZCTJzc1NknTpUp4dVwAAuBlFOlPm6aef1rRp02QymdSpUycFBQXpwoUL+ve//6133nlHr7zyiq3rBAAAAAAAAEqtNWve0/nz5xQT87al7dixo3rggc5W44xGo6pUqarjx49JkgIDAy2hTW5urg4c2Ke5c99U9eohCg0Ns98CAAA3pUihzLPPPqukpCQlJCRo+fLllnYXFxc9//zz6tOnj43KAwAAAAAAAEq3S5cu6YMP1uiBBx5U9eo1LO0ZGRfl5eVdaLyXl5cyMzMLtT///FM6ceK43N3LacqUN1WuXLlirRsAcOuKfKWvl19+WS+88IJ+/vlnpaamys/PT02bNrU6nRIAAAAAAADA9X311b914cJ5Pf10b6v2goICGQyGqxxRIIOh8FUJXn55jCTps882aezYUYqKmqguXboVR8kAgCIqcigjSb6+vmrfvr2tagEAAAAAAADKnK++2qLatevojjvqW7X7+Phc9YyYrKxsVaxYuVD7la8rCw0N09mzyUpIWEooAwBOpnCkDgAAAAAAAMAuTCaTtm37QeHhnQv11ahRUydPHrdqM5vNOn36lGrVqi1J2rFjm7Zu/bbQsQ0a3Knk5DPFUzQAoMgIZQAAAAAAAAAHOXTooHJyctSkSbNCfXffHaaff/5RKSkplrbExK3KysrU3XdfPivms88+0ZQprysr648zakwmk3bs2Ka6de8o/gUAAG4JoQwAAAAAAADgIIcPH5Qk1apVp1Bfjx5/V7ly5TRy5BB9/fVX+uSTjZo8eYLCwtqpceOmkqRnn31Oubm5euWVEfr22//q22+/1ssvD9fRo4c1ePBwu64FAHBjhDIAAAAAAACAg1y4cF7S5Ws3/1VAQIBiYt6Wv3+AJk8eryVLYnX//Q9o8uTpljF16tTTwoVx8vDw1LRpkzRp0ngZjQYtWLBULVq0sts6AAA3x9XRBQAAAAAAAABl1bPPPq9nn33+mv116tRTdHTsdedo0KCh5s6db+vSAADFgFAGAAAAAAAAZZ7RaJDRaHB0GSWe2Vwgs7nA0WUAgNMilAEAAAAAAECZZjQaFBjoTShjA2ZzgVJSMglmAOAaCGUAAAAAB9i161ctXrxAe/b8Jk9PL7Vp01ZDhoxQYGB5SdJ///sfLV8ep99/Pyp//wB16/aInnsuQm5ubpKkoUNf0M8//3jN+b/9drtd1gEAQGlw5SyZbYkXdfGiydHllFi+vq4KbeMro9FAKAMA10AoAwAAANjZ3r17NHz4ILVuHapp02br3LmzWrx4gcaNO663316mrVu/VVRUpLp1e0QvvjhMv/9+VG+/vVDnzp3TmDFRkqSXXx6rzMxMq3lPnTqhKVNe19/+1sMRywIAoMS7eNGk1NR8R5cBACjFCGUAAAAAO4uNjdYdd9TX9Olz5OLiIkny9vZWdPQcnTp1UitXLtedd96lceNekySFhrZRamqq3nlnmYYPHyVPT0/Vrl3Hak6TyaS33pqlevXu0IgRr9h9TQAAAACAGyOUAQAAAOwoLS1VP/20Q1FREy2BjCR16BCuDh3CJUlRUROVn2/91Slubm4ym80yma7+lSoffbRe+/fv1dtvL7N8xRkAAAAAwLkQygAAAAB2dOjQQRUUFCgwsLwmTRqvb7/9r6QCtW/fUS+9FCk/Pz9Vq1bdMj4jI0Pbtyfq/fff1YMPPiRfX99Cc2ZlZSk+fom6dOmmRo0a23E1AAAAAIBbQSgDAAAA2FFKSookafr0yQoLa6fp02frxInf9fbbC3Xy5AktWhQvo9EoSTp7Nlk9enSTJFWpUk0REQOvOuemTR8pI+Oievfua59FAAAAAACKxOjoAgAAAICyxGS6JElq0KChxo6doNat79Zjjz2hV14Zq99++1XbtiVaxnp4eCo6epGmTp0lf39/9evXW0eOHC4054YN63TPPfcpJKSm3dYBAAAAALh1hDIAAACAHXl5eUmS2rVrb9Xepk07SdKBA/ssbb6+vmrVKlQdOtyvuXMXSCrQ2rXvWR134MB+nTjxux588KHiLRwAAAAAcNsIZQAAAAA7ql49RJJ06VKeVbvJZJIklSvnoS1bvtD+/Xut+v38/FS1anWdOXPGqv3777+Rh4eH2rW7txirBgAAAADYAqEMAAAAYEe1atVWlSpVtWXLF1bt3333tSSpWbPmio2N0aJF8636k5KSdOzYEdWrd4dV++7du1S/fkOVK+dRvIUDAAAAAG4boQwAAABgRwaDQYMHD9euXb/qtdfGadu2H/TBB6sVHT1XHTuGq379hoqIeEHbtiXqzTenaNu2RH3++SaNGDFI/v4BevrpXlbzHT58SLVq1XbQagAAAAAAt8LV0QUAAAAAZc3993fSjBnltHz5Uo0ZM0q+vn567LGeGjDgRUlS9+5/k6enl1atWqHNm/8pDw8PtWnTToMGDVVgYHmruS5cOC9fXz9HLAMAAAAAcIsIZQAAAAAHuOee9rrnnvbX7A8P76Tw8E43nGfLlu9sWRYAAAAAoBjx9WUAAAAAAAAAAAB2wJkyAAAAKNOMRoOMRoOjyygVzOYCmc0Fji4DAAAAAJwWoQwAAADKLKPRoIBAL7kYOYHcFvLNZqWmZBHMAAAAAMA1EMoAAACgzDIaDXIxGjVz20Ydv3jO0eWUaDV8K2h06GMyGg2EMgAAAABwDYQyAPD/cnJy9OCD98lsNlu1u7u768svv5cknT2brNjYGCUmbpXJZNKdd96lIUOGq379hpbxixbN16pVKwrNP3DgUPXu3adY1wAAKJrjF8/pUFqSo8sAAAAAAJRyhDIA8P8OHjwgs9msiROnKji4qqX9ynUGsrIyNWTIALm5uSkycpzc3ctp+fI4jRw5RCtWrFGFChUkSQcO7FerVndrwIAXreYPDg6232IAAAAAAAAAOJ0SE8ps3LhRS5Ys0fHjxxUSEqKhQ4eqa9eukqQ9e/Zo6tSp2rVrlwICAtS7d2/169fPcqzZbNaCBQu0bt06paenq1WrVnr99ddVs2ZNRy0HgBM6eHCf3Nzc1LHjA3J1LfzwuGbNe0pLS9WqVestAUzDhneqX7/e+umn7erc+aH/n2e/Hnuspxo3bmLX+gEAAAAAAAA4txJxRdOPPvpIr776qp588klt2rRJ3bp106hRo/TTTz8pJSVFffv2Va1atbR+/XoNGzZM0dHRWr9+veX42NhYrV69WlOmTNGaNWtkMBg0YMAA5eXlOXBVAJzNgQP7VatW7asGMpL0n/98qY4dH7AEMpIUFFRBGzd+bglkLlw4rwsXzuuOO+rbpWYAAAAAAAAAJYfTnylTUFCg6OhoPf/883r++eclSUOGDNGPP/6o//3vf/rf//4nd3d3TZw4Ua6urqpbt66OHTumpUuXqmfPnsrLy9OyZcsUGRmpDh06SJLmzZun9u3ba/PmzerevbsjlwfAiRw4sF8Gg1EvvTRYu3btlJubu+6//wENHfqS3N3L6ejRw+rSpauWLl2kTZs2KjU1VU2aNNPIkaNVt249SdL+/fskSd9887Xeemu2zp07qzp16uqFF4aobdt7HLk8AAAAAAAAAA7m9KHM4cOHdfLkST3yyCNW7fHx8ZKkAQMGKDQ01Oov28PCwrR48WKdP39eJ0+eVGZmpsLCwiz9fn5+atSokbZt23ZboYyr6/VPNHJxKREnIpUotrpN2RvbK+m3qdls1uHDB2U0umjIkOHq12+Adu/erfj4JTp27IimT5+t/Px8rV37nqpWra5XX31NeXl5Wrr0bQ0bNlDvvrtGlSpV0uHDByRJaWkpevXV13TpUp7WrVutMWNGau7cGIWFtXPwSgEAf1bSn7+cEa/XnBe3adnAPtsej2vOy5a3KftjW9yeAHBtTh/KHD16VJKUlZWlfv36affu3apevbpefPFFhYeHKykpSfXrW39NUKVKlSRJp06dUlJSkiSpSpUqhcacPn26yHUZjQYFBnoX+XgUjZ+fp6NLwDWU9L3Jz8/XkiVLVKFCBdWtW1eSFB5+n0JCqioyMlK//rpDkmQwGLR8+TJ5e1++/4eFtdaDDz6oTZs26OWXX9YTT/RQ8+ZN1L59exmNl1+EPvRQJz366KNatmyJunbt7JgFAgBgJyX9NUFpxt4ARcN9x3mxN86LvQHKruzsbLVs2VJms9mq3d3dXb/++muh8StWrNC0adO0ZcsWVa9e/apzTp8+Xbt379bKlSuLpWZ7c/pQJiMjQ5I0ZswYDR06VK+88or+9a9/afDgwUpISFBOTo7c3d2tjilXrpwkKTc3V9nZ2ZJ01TFpaWlFrstsLlB6etZ1x7i4GHkSsrH09Gzl55tvPPAG2Bvbs9XeOFL9+o0lSSkpmZa25s3vliQdOHBYktSiRSvl5Ul5eZfHeHr6q1at2tq5c5dSUjLl5RWgpk1bKy0t22ru1q3v1saNG6zmBgA4Hq8JbI/Xa86rNLxew41x37E9Htecly0f19gf2+I5Byi7du3aKbPZrDfemG51ooTBYCz0udjvv/+uOXPmSpLS0rLl7V34c7N33lmu5cuXq0WLVk7/uZqfn+dNnSno9KGMm5ubJKlfv37q0aOHJOnOO+/U7t27lZCQIA8PD+Xl5Vkdk5ubK0ny8vKSh4eHJCkvL8/y7ytjPD1v78nWZOLJxd7y883c7k6qpO/N2bPJ2rr1O4WFtVOlSpUt7ZmZl8OVoKCKCgwsr9zc3ELrvHTJJHf3cjKZzPr++2916VKeOnQItxqTnZ0jPz//En0bASh5cnJy9OCD9131L5S+/PJ7SdJ///sfLV8ep99/Pyp//wB16/aInnsuwvIa7K/mz5+rffv2asGCJcVeP0qmkv6aoDRjb4Ci4b7jvNgb58XeAGXX3r175ebmpvbt77e65Ihk/Xl6fn6+Jk9+Tf7+/kpOzin0uHHq1EnNnz9P33//jXx8fFRQUFBqHlec/gseg4ODJanQV5TVq1dPJ06cUHBwsJKTk636rvxcuXJlSxp3tTFX5gaAvLw8zZw5VR9//KFV+5dffiGj0ahmzZorLKydtm//n1JTUy39v/9+VMePH1OzZs0lSVu2fKFp0yYpPT3dMiY7O1tbt36nFi1a2WMpAGBx8OABmc1mTZw4VW+/nWD535VAZevWbxUVFak77qiv6dPn6Jlnemv16lWaO3fmVed7993lWrPmPXsuAQAAAABQghw4sF+1atUuFMj81fvvr9SFC+f17LPPX7V//vy5OnnyuKKjF6levfpXHVNSOf2ZMo0aNZK3t7d++eUXtW7d2tK+f/9+hYSEqGXLllq9erXy8/Pl4uIiSdq6datq166toKAg+fr6ysfHR4mJiQoJCZEkpaena/fu3erVq5dD1gTA+VSrVl1dunTTqlUr5ObmprvuaqKdO3/WypUJ6tHjCYWE1FKfPv31zTf/0ahRQ9SnzwCZTCYtWbJQlSpV1iOPPCZJevbZ5/Sf/2xRZOQI9e7dR/n5+Xr33RXKzs5Sv34DHblEAGXQwYP75Obmpo4dH7jqC+KVK5frzjvv0rhxr0mSQkPbKDU1Ve+8s0zDh4+ynFX8179QAgAAAADgag4c2C+DwaiXXhqsXbt2ys3NXfff/4CGDn1JXl6Xr9F8+PAhLVu2VHPmxOj06VNXnWfAgBdVu3ZdGQwGe5ZvF04fynh4eKh///5auHChKleurKZNm+rTTz/Vd999p+XLl6tevXqKi4tTVFSU+vfvr507d2rFihWaNGmSpMtfz9GrVy/Nnj1b5cuXV7Vq1TRr1iwFBwerc2cuuA3gD6NHR6lGjRB9/vmnWrEiXhUqVFRExEA980xvSZeDm0WLlmnRovl6443X5OJiVOvWbTR8+CjLk0qdOvW0cOFSLVkSq2nTJuvSpUtq3ryFxo2boGrVrn6xMgAoLjf6C6WoqInKzzdZtbm5uclsNstk+qP98l8onVB09CLFxb1drDUDAAAAAEoms9msw4cPymh00YsvDlOfPv21Z89uJSQs1dGjR7RgwRKZzWZNnTpRjzzyqFq0aHXNUKZOnXp2rt5+nD6UkaTBgwfL09NT8+bN05kzZ1S3bl3Nnz9fbdq0kSTFxcVp6tSp6tGjhypWrKjRo0dbrj8jScOHD5fJZNL48eOVk5Oj0NBQxcfHy93d3VFLAuCEypUrpz59+qtPn/7XHFO7dh3NnDnvuvM0bNhIc+cusHV5AHDLbvQXSn8OizMyMrR9e6Lef/9dPfjgQ/L19bX0lea/UAIAAAAA2EZBQYFmznxLQUEVVLNmLUlS8+YtFRQUpMmTJygxcav27PlNFy+ma9CgYY4t1oFKRCgjSX379lXfvn2v2te0aVOtWbPmmse6uLgoMjJSkZGRxVUeABszGg0yGvnw73aZzQUymwscXQYAB7iZv1AyGi9fXvDs2WT16NFNklSlSjVFRFh/3WJp/gslAAAAAIBtuLi4qGXL1oXa27a9V5J08OB+rVyZoFmzouXm5iaTySSz2Szp8nvYP1+ipDQrMaEMgLLDaDQoMNBTRmPpfxAubmZzvlJSsglmgDLoZv5CqW3beyRJHh6eio5epIyMDK1cmaB+/Xpr0aJ41a5dx4ErAAAAAACUJGfPJmvr1u8UFtZOlSpVtrTn5uZKkjZsWKdLly7ppZcGFzr2yScfU/PmLbVgwRK71esohDIAnM7ls2RcdODL2cpOOe7ockosz8AauiP8FRmNBkIZoAy68V8oHbCEMr6+vmrVKlSS1KJFK/3jH3/T2rXvacyY8fYrGABuQk5Ojh588D7LX1Re4e7uri+//F6S9PvvRzV//jzt3PmzXFxc1b59Bw0dOtLqaxn/bP78udq3b2+Z+AAAAACgOOXl5WnmzKnq06e/+vcfZGn/8ssvZDQa9dprb8jT09PqmO+++0YJCUs1Y8ZchYSE2LtkhyCUAeC0slOOK/P8IUeXAQAl0o3+QikgIEBbtnyhGjVCVL9+Q0u/n5+fqlatrjNnzti9ZgC4kYMHD8hsNmvixKkKDq5qab/ytbcXL17UiBGDVaFCBU2YMFkXLlxQbGyMkpPPaN68hYXme/fd5Vqz5j01b97SbmsAAAAorapVq64uXbpp1aoVcnNz0113NdHOnT9r5coE9ejxhFq0aFXomMOHL3/2V7duPVWpUrVQf2lEKAMAAFAK3egvlJo1a66RI4cqJKSm1QeVSUlJOnbsiFq3vtsRZQPAdR08uE9ubm7q2PEBuboWfju7ceMHungxXcuWrVJgYKAkqWLFSoqMHKFffvlZzZo1lySdOnVS8+fP0/fffyMfHx97LgEAAKBUGz06SjVqhOjzzz/VihXxqlChoiIiBuqZZ3o7ujSnQSgDAABQCt3oL5RCQmopIuIFTZ8+WW++OUXh4Z117txZLV8eJ3//AD39dC9HLwEACjlwYL9q1ap91UBGkhITt6pp0xaWQEaS2rRpKy8vb/3ww3eWUGb+/Lk6efKEoqMXKS7ubXuUDgAAUCaUK1dOffr0V58+/W9qfLduj6hbt0euO6a0fc0soQwAAEApdaO/UOre/W/y9PTSqlUrtHnzP+Xh4aE2bdpp0KChCgws7+DqAaCwAwf2y2Aw6qWXBmvXrp1yc3PX/fc/oKFDX5KXl7eOHTuqBx7obHWM0WhUlSpVdfz4MUvbgAEvqnbtujIYDPZeAgAAgN1cvm4zr3dul9lcYNPrNRPKAAAAlFI38xdK4eGdFB7e6abnLG1/oQSg5DCbzTp8+KCMRhe9+OIw9enTX3v27FZCwlIdPXpECxYsUUbGRXl5eRc61svLS5mZmZaf69SpZ8/SAQAA7M5oNKh8oJcMRqOjSynxCsxmXUjJslkwQygDAABgB/yFkm3Y+i+UAJQcBQUFmjnzLQUFVVDNmrUkSc2bt1RQUJAmT56gxMStKigouMbZLwUyGPhAAgAAlB1Go0EGo1Fp/9ot04UsR5dTYrmW95J/l0YyGg2EMgAAACWF0WhQQKCnXIwuji6lxMs35ys1JZtgBiiDXFxc1LJl60LtbdveK0k6ePCAfHx8rM6IuSIrK1sVK1Yu9hoBAACcjelClkxnMxxdBv6EUAYAAKCYGY0GuRhdtHjbmzp98bijyymxqvjW0MDQMTb9CyUAJcfZs8nauvU7hYW1U6VKfwQsubm5kqSAgADVqFFTJ09aP86azWadPn1KHTrcb9d6AQAAgKshlAEAALCT0xeP61jaQUeXAQAlUl5enmbOnKo+ffqrf/9BlvYvv/xCRqNRzZo117lzZ/Xee+8oJSVFgYGBkqTExK3KysrU3XeHOap0AAAAwIJQBgAAAADg9KpVq64uXbpp1aoVcnNz0113NdHOnT9r5coE9ejxhEJCaqlHj79r/fo1GjlyiPr2HaD09DTFxsYoLKydGjdu6uglAAAAAIQyAAAAAICSYfToKNWoEaLPP/9UK1bEq0KFioqIGKhnnukt6fJXmMXEvK3o6LmaPHm8vLy8df/9D2jo0JccWzgAAADw/whlAAAAAAAlQrly5dSnT3/16dP/mmPq1Kmn6OjYm55zwYIltigNAAAAuCmEMgAAAACAW2Y0GmQ0GhxdRolnNhfIbC5wdBkAAACwE0IZAAAAAMAtMRoNCgj0kovR6OhSSrx8s1mpKVkEMwAAAGUEoQwAAAAA4JYYjQa5GI2anbhNJy5edHQ5JVZ1X1+90iZURqOBUAYAAKCMIJQBAAAAABTJiYsXdSg11dFlAAAAACUG55oDAAAAAAAAAADYAWfKAAAAm3n11Ujt379XH3zwiaXtxx+3a9myJTp48IDc3d3VuHFTDR48XNWr17CMOXjwgBYtmq/fftspo9FF7drdq0GDhqlChQqOWAYAAAAAAECx4EwZAABgE//612f673+/smrbtWunRo4cIn9/f73++hsaOTJSp06d1ODB/ZX6/193k5x8RiNGDFJmZoZee22KXnllnHbt2qmXXhosk8nkgJUAAAAAAAAUD0IZAECJ8+qrkXriiUeu2mcymTRgwPOKj1983Tn27t2jDh3a6LPPPrnuONycc+fO6q23ZqtSpcpW7StXJqhWrdp644031bbtvbr//k6aPTtGaWmpltv+o482KDs7RzNnzlO7dvcqPLyTRo+O0tGjh7VjxzZHLAcAAAAAAKBYEMoAAEqUq52NcUVubo5ef32c9uz57bpz5OXlaerU15Wfn18cJZZJM2a8obvvbqNWrUKt2u+88y79/e9Py2j84yVHhQoV5OXlrVOnTkiSnnjiKcXGLpWfn79ljJubmyTp0qU8O1QPAAAAAABgH1xTBgBQYlzrbAxJ+uWXnzR37ps6e/bsDeeJi1ukzMzM4iixTPrkk43at2+vVq5cq4UL37Lq69Onf6HxP/64XRcvpqt27bqSpMDAQAUGBkqScnNzdeDAPs2d+6aqVw9RaGhYsdcPAAAAAABgL5wpAwAoMa51NoYkjRkzSpUrV9GyZe9ed45du3bqgw/WatSo0cVVZpmSlHRa8+fP08svj1FAQMANx6ekpOjNN6eoUqXK6tr14UL9zz//lAYNitCxY8c0fPgolStXrhiqBgAAAAAAcAzOlAEAlAjXOxtDkhYuXKq6detdd47c3BxNmTJRzz3XV3Xr3lFMlZYdBQUFmj59stq2baeOHR+44fhz585q1KihSklJUXR0rLy8vAqNefnlMZKkzz7bpLFjRykqaqK6dOlm89oBAAAAAAAcgVAGAOD0rpyN8eqrr13zbIwbBTKStGjRfHl5eapXrz46ezbZxlWWPRs2rNWhQwe0YsVqmUwmSZeDGkkymUwyGo2Wa8kcOnRQkZEjlJ2drTlzYnTnnXdddc4rX1cWGhqms2eTlZCwlFAGAAAAAACUGoQyAACndqtnY1zLjz9u18cff6glS1bI1ZWnP1v46qstSk1N1aOPPlSor2PHMPXtO0D9+g3Ujh3bNG7cK/Lx8dHChUtUp451gLZjxzbl5eWqbdt7rdobNLhTu3fvKtY1AAAAAAAA2BOfSgEAnNqtnI1xLVlZWZo+fbKeffZ51apVWyaTSWazWZJkNptlMpkIaopg9OhXlZWVZdW2bNlS7du3R2++OVcVKlTU/v17NWbMSFWpUlVz5y5QxYqVCs3z2Wef6IcfvtO6dR/Ly8tb0uW93bFjG18zBwAAAAAAShU+gQIAOLWbPRvjevbu3a3Tp08pIWGpEhKWWvXNmPGGZsx4Q99+u92mdZcFISG1CrX5+/vLzc1NDRs2kiSNHv2STCaTIiJe0JkzZ3TmzBnL2MDAQFWrVl3PPvucvv76S73yygg988xzkgq0bt0aHT16WPPmLbTTagAAAAAAAIofoQwAwKndzNkYN9Kw4Z2Ki3vHqu3cuXMaO3aU+vYdoHvuaW/TmnHZyZMntH//PknShAljC/V37fqwoqImqk6delq4ME6LFy/UtGmTdOlSnho3bqoFC5aqceMm9i4bAAAAAACg2BDKAACc2s2cjXEjXl7ehcaePn1KklSlStWbngc3FhU10fLvatWq3/QZSA0aNNTcufOLqSoAAAAAAADnQCgDAEApYTQaZDQaHF1GqWA2F8hsLnB0GQAAAAAAoJQhlAEAlDh/Phvjam7m7IwqVaqWquvIGI0GBQZ6ymh0cXQppYLZnK+UlGyCGQAAAAAAYFMlKpQ5cuSIHn/8cU2YMEGPP/64JGnPnj2aOnWqdu3apYCAAPXu3Vv9+vWzHGM2m7VgwQKtW7dO6enpatWqlV5//XXVrFnTUcsAgBKNszFsx5ZnY1zeFxf967sZupB23CZzllXl/Wuoyz1jZTQaCGUAAAAAAIBNlZhQ5tKlS3rllVesLvackpKivn37qlOnTpo0aZJ+/vlnTZo0SQEBAerZs6ckKTY2VqtXr9b06dNVuXJlzZo1SwMGDNCmTZvk7u7uqOUAQInE2Ri2VRxnY1xIO66zKQdtNh8AAAAAAABsp8SEMvPnz5e3t7dV29q1a+Xu7q6JEyfK1dVVdevW1bFjx7R06VL17NlTeXl5WrZsmSIjI9WhQwdJ0rx589S+fXtt3rxZ3bt3d8RSAKDEunI2RuLXb+oiZ2PcFl//GmrTYQxnYwAAAAAAcIvy8/P13nsrtWnTRp09e1Y1aoTomWd6q0uXblcdv3bt+4qJmaN16z5WlSpVLe1Hjx5RbGyMfvpph1xdXdWiRSsNGjREISG17LQSlEUlIpTZtm2b1qxZo40bN6pjx46W9u3btys0NFSurn8sIywsTIsXL9b58+d18uRJZWZmKiwszNLv5+enRo0aadu2bYQyAFBEF9OOK/U8Z2MAAAAAAAD7W7x4odaufU/9+w9Sw4aNtHXrd3rjjddkMBj14IMPWY09fvx3LV68oNAcp06d1Isv9pOPj69GjRqtwMDy+vTTjzVwYITi41eqatVq9loOyhinD2XS09M1evRojR8/XlWqVLHqS0pKUv369a3aKlWqJEk6deqUkpKSJKnQcZUqVdLp06dvuzZXV+N1+11crt+PW2er25S9sT1b3qbsj22xN86NxzXnxX3HebE3zo3HNefFfcd5sTfOjcc158V9x3lxe6I4ZWVlaf36tXrqqWfVp0+EpMt/qL9//15t2LBG3br9cbZMfn6+pk2bKH//ACUnn5GLi9Hyme4HH6xWbm6Oli9/V9WqVZck3XPPPerf/3nFxb2tyZOn2n9xNsT90LZseXs6fSgzceJENW/eXI888kihvpycnELXhSlXrpwkKTc3V9nZ2ZJ01TFpaWm3Vdfl6yp433ggbMrPz9PRJeAa2Bvnxd44N/bHebE3zou9cW7sj/Nib5wXe+Pc2B/nxd44L/YGxcnXt5zWrl2jChUqWH0+6+XloZSUbKu2JUuWKDU1RYMGDdTkyZPl7+9p6T958nfdcccdaty4gdX8bdrcrXXr1vHZL6zY8nHNqUOZjRs3avv27frkk0+u2u/h4aG8vDyrttzcXEmSl5eXPDw8JEl5eXmWf18Z4+l5ezei2Vyg9PSs645xcTHyJGRj6enZys833/Y87I3t2WpvJPbH1tgb58bjmvPivuO82BvnxuOa8+K+47zYG+fG45rz4r7jvGy5N8DVVK5cQ5J04UKGLlw4r02bPtb333+vceMmKCUlU5J0+PAhLViwQPPmLdCpUyclSWlp2fL2vtzv7e2nvXv36ezZVLm6ulnmPnToiC5evKijR0/J39/fziuzHR7XbOtmHtf8/Dxv6owapw5l1q9fr/Pnz1tdR0aSXn/9dcXHx6tq1apKTk626rvyc+XKlWUymSxtISEhVmMaNmx42/WZTDy52Ft+vpnb3UmxN86LvXFu7I/zYm+cF3vj3Ngf58XeOC/2xrmxP86LvXFe7A3s5YsvPtfkyRMkSW3b3qOOHTvJZDLLZDJp0qTX9PDDj6pp0xY6ceKEJOv/Nrt27a4vvvhcr78+QS+8MFg+Pj76178+0w8/fC9JysjIlLe3r2MWBqdjy8c1pw5lZs+erZycHKu2Bx98UMOHD1e3bt306aefavXq1crPz5eLi4skaevWrapdu7aCgoLk6+srHx8fJSYmWkKZ9PR07d69W7169bL7egAAAAAAAAAAttGoUWMtWLBEv/9+TPHxb+vFFyO0ZMkKrVq1QhcvpmvQoGHXPDY0NEyvvfaGYmLm6Mknv5AktWp1t3r37qu4uLdv+5uWgGtx6lCmcuXKV20PCgpStWrV1LNnT8XFxSkqKkr9+/fXzp07tWLFCk2aNEnS5WvJ9OrVS7Nnz1b58uVVrVo1zZo1S8HBwercubM9lwIAAAAAAAAAsKHq1WuoevUaat68papVq64RI17U6tXvauXKBM2aFS03NzeZTCaZzZfPcDCbzVZ/4P/gg13VqVMXnTx5Qh4eHqpYsZLi4xfLaDTK29vHkUtDKebUocyNBAUFKS4uTlOnTlWPHj1UsWJFjR49Wj169LCMGT58uEwmk8aPH6+cnByFhoYqPj5e7u7uDqwcAAAAAAAAAHCrUlIu6IcfvldYWDsFBpa3tN95ZyNJ0ooV8bp06ZJeemlwoWOffPIxNW/eUgsWLNHRo0e0Z89v6tr1YdWo8celL/bt26N69e6wBDeArZW4UGbfvn1WPzdt2lRr1qy55ngXFxdFRkYqMjKyuEsDAAAAAAAAABSjrKwsTZ06US+8MFjPPRdhaf/hh62SpLFjJygkpKbVMd99940SEpZqxoy5lstcHDlySFOnTlSjRo1Vs2at/287rMTErerbd4B9FoMyqcSFMgAAAAAAAACAsqlatep66KHuWr48TkajUXfeeZf27t2tFSuW6e6726pz54dkMBisjjl8+JAkqW7deqpSpaokqW3be1WtWnVNmhSl/v1fVFZWpmJjY1S1ajX94x9P231dKDsIZQAAAAAAAAAAJcbo0VGqUSNEn376sZYtW6KgoAr6+9+f0vPP9ysUyFyLh4eH5syZr5iYOZo8ebzc3cspLKydBg4cIi8v72JeAcoyQhkAAAAAAAAAQInh7u6u55/vp+ef73dT47t1e0Tduj1SqL169RqaOfMtG1cHXB+hDAAAAAAAAADglhmNBhmNN3dmCq7PbC6Q2Vzg6DJgB4QyAAAAAAAAAIBbYjQaVD7QWwZCGZsoMBfoQkomwUwZQCgDAAAAAAAAAH9SUFCgjz/+UBs2rNWpUycVEFBe9957n/r3Hyhvbx9J0i+//KTFixfq4MED8vHx0X333a8XXnjR6nokBw8e0KJF8/XbbztlNLqoXbt7NWjQMFWoUMFRS7MZo9Egg9Gg1E+TZDp/ydHllGiuQW4K6B4so9FAKFMGEMoAAAAAAAAAwJ+89947WrIkVk8/3VutWoXq5Mnjiot7W4cPH9Jbby3UkSOHNHLkEDVp0lyTJ0/X2bPJio2N0alTJzVz5jxJUnLyGY0YMUg1atTUa69NUU5OjpYsWaiXXhqs5cvfk6tr6fho1nT+kkzJuY4uAygxSsc9HwAAAAAAAABswGw2a+XK5frb3x7XoEFDJUmhoW3k5xeg114bq3379ujrr7+SZND06bPl5eUlScrPN2n27BlKSjqt4OAq+uijDcrOztHMmfPk5+cvSQoICNDw4YO0Y8c2tWnT1lFLBOBAhDIAAAAAAAAA8P8yMzPVpUtXPfDAg1btNWqESJJOnjyhvLw8ubq6ysPDw9Lv7x8gSUpLS1NwcBU98cRTat++gyWQkSQ3NzdJ0qVLecW8CgDOyujoAgAAAAAAAADAWfj6+mrkyNFq2rS5Vft///uVJKl27bp6+OFHZTBI8+fPU1paqg4fPqSEhKWqW7ee6tW7Q5IUGBiohg0bSZJyc3O1a9dOzZ37pqpXD1FoaJhd1wTAeXCmDAAAAAAAAABcx6+//qJVq1aoffuOqlOnriRp0KBhmjdvptate1+SFBxcRQsXLpWLi0uh459//imdOHFc7u7lNGXKmypXrpxd6wfgPDhTBgAAAAAAAACu4ZdfflJk5AhVrVpd48ZNkCStXJmgOXNm6LHHeio6epEmTZomT09PjRgxWBcunC80x8svj9G8eQvUocP9Gjt2lP71r8/svQwAToIzZQAAAAAAAADgKv79739p2rRJqlGjpubOnS8/P3+ZTCatWBGvBx/sqlGjxljGtmjRSk8++Zjee2+lhg59yWqeK19XFhoaprNnk5WQsFRdunSz51IAOAnOlAEAAAAAAACAv3jvvXc0adJ4NWrUWAsXLlVQUAVJUmpqqnJyctSkSTOr8eXLBykkpJaOHDksSdqxY5u2bv220LwNGtyp5OQzxb8AAE6JUAYAAAAAAAAA/mTjxvWKjY3R/fd30rx5C+Xj42PpCwwMlJ+fv3755SerY1JTU3X8+O+qUqWqJOmzzz7RlCmvKysr0zLGZDJpx45tqlv3DvssBIDT4evLAAAAAAAAAOD/nT9/TvPnz1VwcBX17Pmk9u3ba9VfrVp19ev3gubNmyVvb2/df38npaWlauXK5TIajXr66V6SpGeffU5ff/2lXnllhJ555jlJBVq3bo2OHj2sefMWOmBlAJwBoQwAAAAAAAAA/L+tW79Tbm6ukpJOa8iQ/oX6X331dfXs+aR8fHy1evW7+uyzT+TvH6BmzZpr+vTZljNl6tSpp4UL47R48UJNmzZJly7lqXHjplqwYKkaN25i72UBcBKEMgAAAAAAAADw/x5++FE9/PCjNxzXpUs3denS7bpjGjRoqLlz59uqNAClANeUAQAAAAAAAAAAsAPOlAEAAAAAAADgtIxGg4xGg6PLKBXM5gKZzQWOLgMo0whlAAAAAAAAADglo9GgwEBvQhkbMZsLlJKSSTADOBChDAAAAAAAAACndOUsmaNfpSknNd/R5ZRoHgEuqnW/v4xGA6EM4ECEMgAAAAAAAACcWk5qvrLPmxxdBgDcNqOjCwAAAAAAAAAAACgLCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA6cPpRJTU3Va6+9pvvuu08tW7bU008/re3bt1v69+zZo169eql58+bq2LGj4uPjrY43m82KiYlR+/bt1axZM0VEROjYsWP2XgYAAAAAAAAAACjjnD6UGTVqlH755RfNnTtXH3zwge666y7169dPhw4dUkpKivr27atatWpp/fr1GjZsmKKjo7V+/XrL8bGxsVq9erWmTJmiNWvWyGAwaMCAAcrLy3PgqgAAAAAAAAAAQFnj6ugCrufYsWP67rvv9P7776tly5aSpKioKP33v//Vpk2b5OHhIXd3d02cOFGurq6qW7eujh07pqVLl6pnz57Ky8vTsmXLFBkZqQ4dOkiS5s2bp/bt22vz5s3q3r27I5cHAAAAAAAAAADKEKc+UyYwMFBLlixR48aNLW0Gg0EFBQVKS0vT9u3bFRoaKlfXP7KlsLAwHTlyROfPn9fevXuVmZmpsLAwS7+fn58aNWqkbdu22XUtAAAAAAAAAACgbHPqM2X8/PwsZ7hc8fnnn+v333/Xvffeq3nz5ql+/fpW/ZUqVZIknTp1SklJSZKkKlWqFBpz+vTp267P1fX6mZaLi1NnXiWSrW5T9sb2bHmbsj+2xd44Nx7XnBf3HefF3jg3HtecF/cd58XeODce15wX9x3nxd44Nx7XnBf3Hedly9vTqUOZv9qxY4deffVVPfDAAwoPD9f06dPl7u5uNaZcuXKSpNzcXGVnZ0vSVcekpaXdVi1Go0GBgd63NQdunZ+fp6NLwDWwN86LvXFu7I/zYm+cF3vj3Ngf58XeOC/2xrmxP86LvXFe7I1zY3+cF3vjvGy5NyUmlPn3v/+tV155Rc2aNdPcuXMlSR4eHsrLy7Mal5ubK0ny8vKSh4eHJCkvL8/y7ytjPD1v70Y0mwuUnp513TEuLkbuSDaWnp6t/Hzzbc/D3tierfZGYn9sjb1xbjyuOS/uO86LvXFuPK45L+47zou9cW48rjkv7jvOi71xbjyuOS/uO87rZvbGz8/zps6oKRGhzLvvvqupU6eqc+fOmj17tuXMl+DgYCUnJ1uNvfJz5cqVZTKZLG0hISFWYxo2bHjbdZlMtrmD4Obl55u53Z0Ue+O82Bvnxv44L/bGebE3zo39cV7sjfNib5wb++O82Bvnxd44N/bHebE3zsuWe+P0Xyz33nvv6Y033tCzzz6rt956y+qryEJDQ7Vjxw7l5+db2rZu3aratWsrKChIDRs2lI+PjxITEy396enp2r17t1q3bm3XdQAAAAAAAAAAgLLNqUOZI0eOaNq0aercubMGDhyo8+fP6+zZszp79qwuXryonj17KiMjQ1FRUTp48KA2bNigFStWaODAgZIuX0umV69emj17trZs2aK9e/dq5MiRCg4OVufOnR28OgAAAAAAAAAAUJY49deX/etf/9KlS5e0efNmbd682aqvR48emjFjhuLi4jR16lT16NFDFStW1OjRo9WjRw/LuOHDh8tkMmn8+PHKyclRaGio4uPjrc64AQAAAAAAAAAAKG5OHcoMGjRIgwYNuu6Ypk2bas2aNdfsd3FxUWRkpCIjI21dHgAAAAAAAAAAwE1z6q8vAwAAAAAAAAAAKC0IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsoMyEMmazWTExMWrfvr2aNWumiIgIHTt2zNFlAQAAAAAAAACAMqLMhDKxsbFavXq1pkyZojVr1shgMGjAgAHKy8tzdGkAAAAAAAAAAKAMKBOhTF5enpYtW6Zhw4apQ4cOatiwoebNm6czZ85o8+bNji4PAAAAAAAAAACUAWUilNm7d68yMzMVFhZmafPz81OjRo20bds2B1YGAAAAAAAAAADKCkNBQUGBo4sobl988YWGDRumX375RR4eHpb2ESNGKCcnR4sXL77lOQsKCmQ2X/+mMxgko9Eoc1aOCszmW/4d+IPBaJTRy0Nms1m2+C/2yt7kZ12UzPm3P2FZZnSRi5evzfZG+mN/LmWnymw22WbSMshodJWbZ0Cx7E0Oe3PbjEZXedhwf67sTVYOe3O7jEZXeXkUz30nPSdV+QXsT1G5GFzlV0x7k5qTKVMBrwluh6vBRQEe3jZ/XEvNyZapgNfSt8PVYFSAh2cx3XdyZLrB+yJcm6vRoAAP273Pkf7Ym7ScPJlK/9v9YuVqMMjfw93mj2sXc/KVz97cFheDQb4eLsXzXifHrAIe14rMYDTIw8NYTJ8RsDe3y2A0yM3Tdvvzx+drJomX0rfHRXLxci2W+445K4/7zm0wGA0yet3c6wGj0SCDwXDDOV1tVJtTy87OliS5u7tbtZcrV05paWlFmtNgMMjF5cY3sCQZvTxuPAg3xWi07cldLl6+Np2vLLP13kiSm2eAzecsi4pjbzzYG5ux9f54eQTYdL6yrDjuO37sj00Ux94EeHjbfM6yytb7E+DhadP5yrLiue/wXscWimNv/D3cbzwIN8XW++Pr4WLT+cqyYnmv41EmvlSm2BXPZwTsja3Y/vO1MvERs10Ux33H6MVrAluw5d6UiUezK2fH5OXlWbXn5ubK05M3eQAAAAAAAAAAoPiViVCmSpUqkqTk5GSr9uTkZAUHBzuiJAAAAAAAAAAAUMaUiVCmYcOG8vHxUWJioqUtPT1du3fvVuvWrR1YGQAAAAAAAAAAKCvKxBf+ubu7q1evXpo9e7bKly+vatWqadasWQoODlbnzp0dXR4AAAAAAAAAACgDykQoI0nDhw+XyWTS+PHjlZOTo9DQUMXHx8vdnQsdAQAAAAAAAACA4mcoKCgocHQRAAAAAAAAAAAApV2ZuKYMAAAAAAAAAACAoxHKAAAAAAAAAAAA2AGhDAAAAAAAAAAAgB0QygAAAAAAAAAAANgBoQwAAAAAAAAAAIAdEMoAAAAAAAAAAADYAaEMAAAAAAAAAACAHRDKOEjv3r01duzYq/aNHTtWvXv3ttnvatCggTZs2CBJKigo0Icffqjz589LkjZs2KAGDRrY7Hfh2goKCrRhwwb17t1bYWFhaty4sTp16qTJkyfrzJkzNvs9p06d0qeffmqz+ZxRRkaGmjVrpnbt2ikvL++Wjz9w4ID+85//2L6wW5CYmKgGDRroxIkT1xzz58cJZ7uvmkwmrVixQo8//rhatGihNm3aqG/fvtq6datd69ixY4e2b99erL8jKytLq1atsvxs68fov+rdu7caNGhw1f9NnTq12H7vld99recmSQoPD7eq584771Tr1q3Vu3fvYt+H4lbc+2pL4eHhmj9/vqPLwE24cp9JSEi4av9rr72mBg0a3NZ+/vm/B2d7rnC0vz5mNW7cWF26dFFcXJxNf8/1HjtPnDihBg0aKDEx0aa/syy42vNh48aNFR4erqlTpyonJ+em5/rqq6908ODBW/rdzvoarLj8+Xlw/vz513wtcuV/13oNe+TIEY0aNUpt27a17NekSZN07tw5m9Z7q3taWhX1fdGt/nfN5wiX2fNznOv563vJlJQUrVu3rsjH/3VdJfn+dbufFfyVrZ/H58+fr/DwcMvPZeHzm6IaO3bsDZ+Lbsef/zv/632irL/fupnPe/78WbO93MznaLfiRp9/FDdXh/1mOMS2bds0duxYbdmyxdGllCn5+fkaMmSIfvzxRw0aNEivvfaavL29deDAAcXGxqpnz57auHGjKlSocNu/a8yYMapWrZq6d+9ug8qd06effqqgoCCdO3dOmzdvvuW1Dhw4UD169FDHjh2Lp0AbmT9/vlxcXBxdRiF5eXnq27evTp8+rWHDhqlFixbKycnR+vXrFRERoenTp+uxxx6zSy3PPPOMpk+frtatWxfb71i2bJk2bNigZ599VpIUFRWl/Pz8Yvt9ktS1a1dFRUUVavf09CzW33szIiIiFBERIenyG/TU1FTNnTtX/fv31z//+U8FBwc7uMKisce+omxyc3PTP//5T/Xt29eq3WQy6YsvvpDBYLDZ7+rWrZvat29vs/lKgz8/ZuXk5OiXX37R+PHj5enpaXlcL05VqlTRt99+K39//2L/XaXRX58Ps7Ky9O2332r69OnKz8/Xa6+9dsM5Tp48qUGDBumdd95RvXr1bur3OutrMHuJiIjQU089Zfn5iSeeULdu3Sz3JUkqX758oePOnTunp59+Wvfdd5+WLl2qwMBAHTlyRLNmzVLv3r310Ucfyd3d/bbrK8qellZFfV90q88Xf/0cgecbx2rRooW+/fZby/1w5syZOnHihP7+978Xab4/P+aV9PvX7X5W8FfF/TxeFj6/KaqoqCi9/PLLlp/vvfdevfrqq+rWrdttz/3X/87/ep8qy5zp856/Km37RChTxhQUFDi6hDIpISFB33zzjdauXau77rrL0l61alXdfffd6tatm5YtW6bRo0c7sMqSY/369br33nt15swZrV69utS+gAkICHB0CVcVExOjvXv36tNPP7X6AD4qKkpZWVmaNm2aOnfuLG9vbwdWaTt/fdz09fUt9t/p4eGhihUrFvvvKQovLy+r2ipVqqRJkybpvvvu0xdffKHnnnvOgdUVnT32FWVT27Zt9c033+j06dOqUqWKpf2HH36Ql5eXTcNWDw8PeXh42Gy+0uCvj1k1atRQYmKi1q9fb5dQxsXFxWkfz0uCqz0f1qxZU7t27dKnn356U6FMUd7/OOtrMHvx9va2eh3n4uJS6L50Nf/85z9lMpn05ptvWgLnatWqqWrVquratau++eYbPfDAA7ddH+9p/1DU90W3+nzx19uc5xvHcnd3t7o/3u594s+PeSX9/mXrzwp4HnccX1/fQu/RfH19bbIff/3v/K/3qbLsZj/vcYTStk98fZmTu3jxoiZMmKCwsDC1atVKzz33nH799VdLf0FBgeLi/q+9Ow+rMf8fP/60lbKN3Uj2ESMtGCQk2cLYDUVG2frYM0JjK5Q2E9XYayzJHkr2bMMge0OWUUTMDMY2WSrl90e/c387rYcZxLwe19V1cZb7fp97e++v9wqsra0xNDSkSZMmjBw5ktu3b2fb1smTJ5XGMisrK7VpZmFhYXTo0IFGjRrRu3dvLly4oLz36tUrZYqlsbExvXv35siRI8r7Bw4cYMCAAZiamtKoUSP69u3LL7/8orxvZ2fH999/T79+/WjatCnbtm0DMjJLa2trjIyMsLa2ZtWqVaSnp/9rx66geP36NWvXrqV79+5qHTIqOjo6hISEMGHCBAB+//13Jk2ahLm5OSYmJgwdOpSrV68C/zdVLy4uTm0b9vb2ODs7Y2dnR3R0NFu3blWbEvspiYuL48KFC5ibm9O5c2eio6PVjkdO0w8zTyVv164dd+7cITAwUHnt8ePHuLm5YWFhgZGRETY2NmqhmAICAhgyZAirV6+mVatWmJiYMHHiRO7fv8/kyZMxNTXFwsKCrVu3Kt9JS0tj5cqVdOrUiUaNGtGpUyc2btyY7fccPHiQjh07YmRkhL29vdq9m9dUypSUFHx8fGjdujWmpqZ88803HD169C2O6JtJTU1l06ZN9O3bN8cZEePHj2fFihUUL15co+NqZ2fH8uXLadOmDY0aNWLw4MHEx8crnzl8+DC9e/fG2NgYMzMzpk6dypMnTwCU6couLi5MnTpVmVq+aNEizM3NadeuHU+fPs1xWm3W6cgXL17E3t4eU1NTWrZsycyZM3n+/DkBAQEEBgZy584dZZps1tAEcXFxODo60rx5c5o0acK4ceO4e/eu8r6dnR1eXl58//33NG3alMaNGzNlyhSePXv21udBk+srv3SlpKTg4eGBmZkZTZs2Zf78+W/9DC5aNGOMh2r0a7t27fDw8KBLly40b96cEydOaJTm27dvM3r0aJo0aULz5s1xcnJSC3eSX76xbds2unbtSqNGjWjdujXu7u5K2IK0tDR8fHywsLDA0NCQzp07s27dOuW7mc+r6ll7+PBhunXrhqGhIV27duXgwYNq58DPz49WrVphbGzM2LFjcXd3/+Ah0FJSUpg/fz7t27fH0NCQ5s2bM3HiRB49ekR6ejpmZmZqobRWrVqFgYEB58+fV14bP348zs7OjBo1KlsnW3x8PAYGBly5cgXIeIb17t0bIyMjOnTowIIFC9RCRRgYGODn54elpSXm5uZq9/d/hZGREVWrVmX37t1qr+/cuRNra2u1mTJnz55l4MCBGBkZ0bZtW9zc3EhKSlLe//vvv5kyZQpNmzbFzMyMlStXqm0zaziZ3377jVGjRtG8eXMMDQ3p0KEDq1atejc/9COSuSPs6dOnzJo1CwsLCxo2bIi5uTmzZs1SQmNp8jzILC0tjQkTJmBhYcHNmzezhT3RJE+4ePEiAwcOxNjYGCsrK8LDw/nyyy8lBFom2traFC6cUZXNq0yUmJiodAIMHjxYyfs1qb98yHAWH6tChQrx7NmzbNdq7dq1iYyMpEWLFspr+eUfz58/Z+7cubRq1QpTU1MGDhxITExMrudUk/JYTnXSj1le9aKpU6dmmzXxxx9/0KBBA44fP/5G+UVO7QhZv/9vlPs/JfkdjxcvXjBt2jTMzc1p1KgRPXv2ZO/evcr7dnZ2eHh4MHnyZExMTGjTpg3Lli1TGpIzh/CZOnUqW7duJTo6Wjkn+eVtWameeTndXz179sTFxUXt80eOHMHQ0JCHDx/+q8ftn8qvrSC/8rvquC5fvpzmzZvTq1cvbt26lS182Zo1a+jUqRNGRkZ06dKF7du3A7mHOsstzFNO7Tc5hc3KnCfllMa0tDT+/PNPnJycaNq0Kc2bN8fR0ZGbN2/+84NagOWVj+zZswcDAwP27NmjfN7Z2RlLS0tiY2OzXef5hcXKr4z+qXiT9h7ICFlqb2+PkZERrVq1YunSpWqf16SuuGPHDgYPHqx85sCBAxw4cIBOnTphYmLCsGHDlGdN1vP0T9urPzTplCnAXr9+zfDhw7l58yZLly5l48aNmJiYYGNjQ2xsLJDRmLJ06VKcnZ3Zs2cPixYt4saNG3h6embbnqmpqfJw37Rpk9qUv/Xr1zN//ny2bNmClpaW0kEA4OHhwdq1a5k0aRIRERFYWFgwatQorl+/zsWLFxk9ejQdO3YkPDycTZs2Ub58eSZNmqR2o4WFhTF48GDWrVuHhYUFGzZswMvLi9GjRxMZGcmECRNYvnw5vr6+7+hofjiJiYncvXuXli1b5voZPT09tLS0SEpKwsbGhj///JPFixezfv16dHV1GTRoEHfv3qVZs2ZUq1aN8PBw5bv37t3jxIkT9OrVi4CAAExNTbG2tmbz5s3v4+e9d5s3b0ZXV5c2bdrQvn17tLS01BpWNfl+lSpVcHBwICAggLS0NBwcHDh9+jReXl5s3bqV+vXrM2TIELUO0NOnT3P69GlWrVrFggUL2LNnD926daNBgwZs2bKFNm3aMHPmTB49egSAp6cnixYtYsyYMURERDB48GBmz57NmjVr1NITFBTEjBkz2Lx5M9ra2tjY2PDixYt8f4eLiws///wzPj4+bN26FWtraxwdHd/5Wjm3b9/m8ePHmJiY5Ph+pUqVMDIyAtDouJ47d45Tp06xbNkyVq5cyd27d3FzcwPg4cOHjBkzhj59+rBz504CAwM5deoU3t7eAEqDy/fff68W2iQ8PJxVq1axcOFCSpcune9vSkxMxM7OjnLlyrFhwwYCAwM5efIkM2fOVMLeVKlShaNHj6qNcoeMac/9+/dHS0uLVatW8dNPP/HXX38xaNAgtULamjVrqFChAps2bWLu3Lns3LkzW2Pqm8jv+tIkXap0eHp6sm7dOu7evftW68L8+eefzJ49W7kvVdatW8f06dNZsWIFjRs3zjfNf//9N7a2tjx//pyVK1eycuVK7ty5w9ixYwHyzTeuXLnC9OnTGTt2LHv27MHDw4Pt27cra0eEhoaye/du/Pz82LNnD4MGDcLV1TXP3+zj48O0adMICwtDX1+fSZMmKQ2nvr6+bNiwgZkzZxIWFkalSpWy3d8fgre3Nzt27MDd3Z09e/bg5eXFsWPHWLx4MYULF8bCwoJjx44pnz9+/DiFChXixIkTQEbB9tixY1hZWdGnTx+io6PVGrW2bdtGw4YNqV+/PkeOHGH8+PH069ePHTt2MGvWLHbt2oWzs7NamjZs2IC/vz8//vgjtWvXfj8HooCxtrZW65RJSUlh//79aqM3r1y5wpAhQzA3Nyc8PBxfX18uXbqEg4OD0ggzYcIEYmJiWLJkCcHBwRw8eJA7d+7kuM8XL15gb2+Prq4uoaGhREZGYm1tjYeHB5cvX363P7gAi4mJISIigv79+wMZYUNiYmLw9/dnz549uLi4EBYWxoYNG9S+l9fzQCU9PZ3Jkydz4cIFQkJCqFmzZo5pyCtP+PPPP/n222/R09Njy5YtzJw5Ez8/Pwmv+P+9evWKQ4cOsX37dnr06AHkXSb6/PPPlXUWAgICcHBw0Lj+It5c165dqVq1Kt9++y09evRg3rx57N+/n6SkJOrWravMvtEk/3BycuLgwYN4eHiwbds2atWqxdChQylevHi2c6ppeSxrnfRjl1e9qFevXsTExJCQkKB8Pjw8nMqVK9O8eXO17eSXX+TVjgBoXJ/Kq9z/KdHkeCxcuJCrV6+ybNkydu7cSZs2bXByclJrEA4NDUVHR4ctW7bg5OTEjz/+yPLly7Ptb9q0aVhbWythfUDzvC2rnJ6ZvXv3Zs+ePWodOtu3b8fS0rLAhRDKr61A0/L7oUOH2LBhAx4eHsoAAJWgoCB8fX0ZOnQoO3bsYODAgbi4uKiVrzX1T9pvMqcxOTkZOzs70tLSCAkJYc2aNZQtW5ZvvvnmX12/uCDJLx/p1KkTPXr0YM6cOTx58oTIyEgiIyPx8fHBwMAg23WeF03K6J8KTdt7VOEOQ0JC6NGjB5GRkdja2vLDDz8o685oWlecO3cuAwcOZMeOHdStW5fvvvuOxYsX4+Pjw5IlS4iJicnx2Qf/Tnv1hyThyz6giIgItV5blZSUFBo3bsyJEyc4d+4cx48fVzK7iRMncvbsWVavXo2npyfVq1fH09NT6VXX09PD2to6x4XCtLS0lDiY5cqVU5tu7OHhocQLHTp0KGPGjOGvv/5CW1ubjRs3Mn36dKXwNX78eNLT03n27BlaWlpMnz5dLfzD4MGDcXBw4K+//lIaMBs0aMDXX3+tfGbRokWMHDmSbt26ARmhJJKSknBzc2P8+PFoa2u//YEtYFSjvLMWWBwdHdVGUFStWpWBAwfy6NEjwsLClM/7+vrSvn171q5di7OzM7169WLr1q04OTkBsGPHDipXrkyLFi0oXLgwxYoVo3jx4gWugPRvePXqFREREVhaWiqjXC0sLNi+fTvfffedRiFgypUrp4Rg+Oyzzzh8+DCXLl0iIiKCevXqARkLL1+4cIGgoCAWLFgAZDS0zJ07l9KlS1OnTh0aNGhAsWLFlDUChgwZwsaNG0lISKBYsWKsW7eOqVOnKtd9zZo1uX37NkuWLGHQoEFKeqZPn67EY/b29sbCwoIdO3bkGQ84ISGBHTt2sHnzZho1agRkzJa6cuUKQUFB73StHNUslfxi6h49elSj4/rq1Su8vb2VKfN2dnb4+PgAGQ1TKSkpVK1aFT09PfT09FiyZInSMKWatqqa1qxKm62t7RvFP964cSNlypTB09OTYsWKARkFg+joaEqUKIGurm6u09ZDQ0PR1dXF19dXmSXi7+9Pu3btCA8Px9bWFoA6deowceJEAGrVqkVkZCRnz57NNU055Q+mpqYEBweTlJSU7/WVX7q6d+9OWFiYMoIOMvIBTUZhL126lODgYCDj/KWkpFCnTh0WLFhA1apVlc9ZWFgondGapHnnzp38/fff+Pn5KdeDu7s727dvJzk5Od98IzExkUKFClGtWjWqVq1K1apVCQoKomTJkgDcunULXV1d9PX1qVixIoMGDaJ27drUqlUr1986YcIEzMzMlH/36NGDa9euUb9+fUJDQ3FxcaFjx44AzJgxg3PnzuV7/N61Ro0a0bFjR5o1awZklAtatWqlzLi0tLRkypQppKSkULhwYaKjo+nQoQMnT57E0dGRM2fOkJycTOvWrdHW1qZChQpEREQwcuRI0tPTCQ8PZ9iwYQAsWbKEvn37YmNjA0D16tVxc3Pj22+/JTExkWrVqgHQo0cP5Vn1X2VtbU1QUJASwuzYsWOULVuWL7/8UvlMUFAQZmZmjBo1Csi4R1SznqKjo6lYsSJHjx5l5cqVyjpa8+fPx9LSMsd9vnjxgsGDB2Nra6vcB2PGjGHp0qVcvXqVBg0avONfXTBkfmalpqaSmpqKsbGxUq41NzenadOm1K9fH4Bq1aoREhKi3DMquT0PTE1NgYxygouLC+fPnyckJAQ9Pb1c05RXnrBhwwZKly6Nu7s7xYoVo27dusyYMYP//e9//+JR+XhkzQ9fvnxJ1apVGTp0KI6OjhqViVRl4jJlylCiRAmKFCmiUf1FvLnPPvuMsLAwVq9ezd69e5VBFsWLF2fEiBGMHj0ayD//SE1N5dChQ6xYsUIpJ6vW5Hz69Gm2c7po0SKNymNZ66Qfs/zqRc2aNUNfX5+IiAjGjBkDZNxPPXr0yNbIrEl+kVs7Avw75f6PSX7tOJocj1u3blGyZEmqV69OqVKlGD9+PE2bNlWrY9WuXRtXV1cKFSpEnTp1iIuLY/Xq1QwfPlxtv6VKlaJ48eIUK1ZMqa9omrdlVaRIkWz3V/fu3fHx8WH//v1069aNpKQk9u/fr5zXgiK/ewLQuPzu4OCgDKzIOnNi5cqVDB48mG+++QaAgQMH8vLly7caPPHZZ5+9dftN5jRu2rSJR48eMX/+fKU+6+7uzsmTJ9m4caMyyO1Tokk9ZObMmXTv3p1p06Zx8uRJRo0apZShs17necmvjJ61o/tjpml7j4qNjY2yvsyoUaMIDg7m4sWLmJmZaVxX7NWrF506dQJgwIABHDhwACcnJ2Wwr7m5OdeuXcu276SkpH+lvfpDkk6ZD6hdu3ZMmjQp2+u+vr48fvyYS5cuAWSLu5uSkkJycrKyjQsXLuDv709CQgJxcXH89ttvVK5c+Y3SkrlRSjWy/OXLl9y9e5fU1NRsvaSqDgHIuFmXL1/OjRs3uHnzpjL6MnOmVKNGDeXfDx8+5I8//mDhwoUEBgYqr6enp5OcnExiYiJ16tR5o/QXZGXLlgUypjBn5ubmpow2WbNmDQcOHODatWvUrFlTLUPW1tbGyMhIKUD17NmTwMBAzp49S+PGjZWRglkL15+iw4cPc//+fbXRWV26dGHfvn1ERkbSt2/fN97mtWvXKFWqlFJghozQC02bNuXnn39WXitfvrzarAsdHR21h7iqIzE5OZn4+HhSU1Np0qSJ2r6aNm2qjNzL/JpK6dKlqVmzZo4ZTmaqmXJZwwqlpqZqNDPkn1Bdm1mv56w0Pa4VKlRQi2FcqlQpUlNTgYyKc7du3XB0dOTzzz+nZcuWtG3bNt/QfJmfN5q4evUqDRs2VAqwAF999RVfffVVvt+9du0ahoaGaovWli9fnlq1aqlVerI+00qVKsXTp09z3W5O+YOqAqzJ9ZVfum7cuEFqaqpaQ7m2trZGjbQDBgxQpvgXLlyYzz77LMf1WDKfB03SfPXqVWrWrKl2PXzxxRdMmjRJo3xDFbamT58+1KxZk5YtW2JlZYWhoSGQUWHav38/bdq0wdDQEHNzc6ytrSlfvnyuvzXzrA5VI0VqaipxcXG8fPkyW97YpEkTJazXh9KjRw+OHz/ODz/8wM2bN4mLiyM+Pl551rRq1Yq0tDTOnDmDlpYWxYsXZ8CAAYwaNYqUlBQOHTqEmZmZUjnp3r0727dvZ+TIkZw4cYIHDx4oHWOxsbHExMSohW5UjRaLi4tTCtpvek9+igwNDdHX12f37t3Y29uzc+dO5TiqxMbGkpCQoDTyZxYXF6fMxMx831aoUAF9ff0c91muXDlsbW3ZuXMnV65cISEhQSmjfYrhYnOT+Zn16tUrbt68iZ+fH7a2tmzZsgVbW1sOHDjA9u3buXXrFteuXeP27dvZZrnk9jxQ2bVrF6mpqdSuXTvfWNd55QmxsbHZ8qTMZYX/GlV+mJ6ezoULF5g3bx4tW7bE0dGRokWLvlWZSNXAnF/9RbydMmXKMHbsWMaOHctff/3FiRMnlBmTZcuWxdbWNt/8QzVrPHM+q6WlpYRQytpIqml57FPKjzSpF/Xs2VPplLl8+TLXrl3D398/27b+aX7xb5T7Pyb5teNocjyGDx+Oo6MjZmZmmJqaYm5uTteuXdXK1M2aNVMLcWpiYsLy5cuV8kBeNM3bNPHZZ5/Rrl07tm3bRrdu3di1axelSpVSOkwLivzuifr162tcfs/tOD18+JB79+5hbGys9vrQoUOB7M+mdylzGmNjY0lKSlIGZakkJydnC33/qdCkHlKyZEnc3d0ZMmQIDRs2fOsBLvmV0T+lThlN23tUsg5wLF26tNJerWldMfM2VG0emes32traOc5sUbVp/NP26g9JOmU+oBIlSuRYMCxRogSPHz8mPT2dkiVL5hh7UlXgXL58OQEBAfTu3ZtmzZphZ2dHVFRUjjNl8qKaepbZ69ev1SqEOTl16hQODg5YWFjQtGlTunbtyosXL5RRUCqZR9OoCnYuLi45hvQqCL2V/ybVqOzo6Gi1MCWZO85UvdCvX79WK3ippKWlKWs2VKtWjWbNmhEREUGpUqW4cuVKgRul8q6o7oVx48Zle2/9+vVKp0zWKaR5FfZzO+bp6enKMQdyvBdy6whT7T/rdlXXfubtZr330tLS1CqUeW1/7dq12UZ1vOvOOX19fSpUqMC5c+eyhS4AuHnzJrNnz6Zx48YaHdf8fuv8+fMZPXo0R44c4ZdffmHixIk0btyY1atX5/qdnBYdzeuaKFq0aI5p1URe92zmaya/35lVbvmDap+Q9/WlabqyynxuclOmTBmNGjUynwdN0pzXedAk39DS0mL16tXExsZy9OhRjh49yvr16+nZsyfz5s2jZs2a7N27l+joaI4dO0ZUVBRLlixh3rx59OrVK8f95nTeXr9+rRyngjhd3dXVlZ07d9KzZ0/atm3L//73P4KCgpTQBSVKlKBZs2YcO3aM4sWL06JFC5o2bcrr16+5cOEChw8fZsiQIcr2+vTpQ1BQEBcvXiQ8PBwrKyulQSU9PZ1hw4blePwyN0rLQsAZVCHMbG1tiYqKUsImqKSnp/P111/j6OiY7bvlypVTwmJkbSDL7b598OAB33zzDWXLlsXKygozMzMaNWr0SYTseRNZn1l16tShTJkyDBw4kF9++YXQ0FCuXr3K119/TadOnZg4cSIzZszItp3cngcqlSpV4ocffmDo0KH4+/vn2GCX17ZUihQp8p/qNMtP5vywVq1aVKlSBXt7e4oUKYKrq+tblYk0rb+IN7d8+XKqVauGtbU1kNEx0rVrV7p06UL//v05fPgwtra2+eYfqnjvmpbPNC33fEr5kSb1ol69ehEYGEhMTAy7du3C1NQ0xxnC/zS/0LQ+9abl4YIqv3YcTY6Hqakphw8f5tixYxw/fpzNmzcTEBDAihUrlFmZWfN31fMup7abrJ9zdHTUKG/TVJ8+fXB0dOTBgwfKrHtN6g3vU373xNy5cwHNyu+5RW5RXcP5PZsy7+NtOh41ac/InMb09HRq1arF4sWLs31OV1f3jff/MdC0HnLp0iWKFi3KjRs3uHPnDtWrV3+rfeVVRv+UaNreM2XKFCD3tmTQ/Bzl9CzRJP//t9qrP6RPf2j9R6xevXokJSWRkpJCjRo1lL/ly5cTFRUFwOLFixkzZgyurq70798fExMTbt68mWtG86YNjzVq1KBYsWJqsWAB+vbty4oVKwgKCqJ58+YEBgYqMRZ///13IPfMrnz58pQvX55bt26p/a5Lly59kp0LRYoUYfDgwWzbti3XEdSqY1avXj1u3LihNpMiOTmZixcvqoVj6t27N/v27SMiIiLXwvWn5uHDh8qi79u2bVP769u3L7/++iuXLl2iWLFi/P3332rfvXXrVq7bNTAw4OnTp9lmp5w5c+aNQmBlVrt2bYoWLZptrYrTp09TsWJFtamgFy9eVPuNN2/e5Isvvshz+6r37927p3YPhYWFsWXLlrdKs6YKFy5M3759CQsLyzE+7YoVKzh//jz169f/x8f1/PnzeHh4ULt2bYYMGcKyZcuUEFuZ75H8ZL0mkpKS1BalrFu3LrGxsWqjJfbt20ebNm148eJFns/NevXqERMTozZy48GDByQkJLyzGX+aXF/5patOnTpoa2tz5swZ5f1Xr169s1kemqS5bt263Lx5U+1cxcbG0rx5c5KTk/PNNw4fPkxgYCBffvklI0aMYPXq1YwbN46dO3cCKKFUzM3NmTx5MhEREZiZmSnvv4kaNWpQvHhxzp8/r/Z6TEzMG2/r3/To0SPWrVuHq6sr33//Pb1796ZBgwbEx8er5cnt2rXj2LFjnDhxghYtWqCtrY2pqSmbN28mPj5eLRxWnTp1MDU1ZefOncpCjSpffPEF8fHxaufkzz//xNvbO9taGyKjU+bChQts3rwZfX39bM+IL774gt9++03teKalpTFv3jx+//13JdRZ5tCHT58+zTWPi4iI4PHjx6xfv55Ro0bRoUMHJSRBQexQ/BAuXrzI4cOHlU6U7t27U716dW7duvXGx+irr77C2NiYSZMmERwc/NbPg/r163Pp0iW1BpgLFy681bY+RS1atMDe3p5169Zx5MgRjcpEWfPxt6m/CM1cuHCBRYsW8erVK7XXCxUqRIkSJZTZqfnlH6rnY+Y66KtXr2jbti2RkZHZzumHKI99SJrWi/T09GjWrBm7d+9m586duQ5C0SS/yKs8/C7qUx8zTY6Hv78/Z86cwcrKiunTp7Nnzx709fXVwqJlbYM5e/Ys1apVyzGsUObzExsb+4/ytpzOdatWrahYsSKbNm3izJkzauXBgkCTe+L58+f/uPxesmRJKlWqlO3cjBs3jrlz5yqNxJnXssqrLSInWeuu6enp+c7AqVevHnfv3qVUqVLKM1VPT4/58+dz6tSpN9r/x0KTesjVq1dZuHAhs2bNwtDQkMmTJyt1/jdpG82vjP4p0bS9J68wvSrvuq74Ltqr3zfplCnAWrduTYMGDZgwYQLHjx8nISEBLy8vtmzZohQuVXHJr1+/Tnx8PH5+fuzduzfXRYtUveRXrlzR6CbQ0dFh0KBBLFy4kKioKG7duoWfnx/Xr1/H0tKSzz//nKtXr3L69GkSExPZsmULCxcuBMg1DYUKFWLYsGGsWbOGNWvWcOvWLfbv34+bmxtaWlqfzAiazIYNG4alpSW2trYsWbKEK1eukJiYyIEDB3BwcGDLli20aNGCr7/+mtKlSysL+V65cgVnZ2eeP3+uLEgLGYuWPX/+nJCQkGyF6xIlSnDnzh3++OOP9/0z36nt27fz6tUrhg0bRr169dT+HB0dKVKkCOvWraNx48b88ssvHDhwgNu3b+Pv75+tQFyiRAlu3rzJgwcPMDc3x8DAgO+++46TJ08SFxeHm5sb165d49tvv32rtJYqVYpvvvkGf39/IiIiSEhIYO3atYSGhuLg4KBWAJg5cybHjx/n8uXLODk58fnnn+c4IiGzL774AktLS2bNmkVUVBS3b98mKCiIpUuX5hrG5t/k6OhIjRo1GDBgANu2bePWrVv8+uuvTJs2jS1btjBnzhzatm37j49ryZIlCQ0NxcfHh4SEBK5evUpkZCQ1a9ZUwgLq6uqqhfXJiampKRs2bODSpUtcu3aNyZMnq43GsLW15dGjR8yaNYu4uDhOnz6Nr68v5ubm6OjooKury5MnT5TpsZnZ2NiQlJTEpEmTuHLlCjExMYwfP56yZcuqzYz7N2lyfeWXLl1dXQYNGoS/vz979+4lLi6OWbNmvbOFIDVJ89dff02ZMmVwdnbmypUrXLx4EVdXV+rVq4eenl6++UbRokX58ccfWblyJbdv3+bXX3/l4MGDyjTzv/76i9mzZxMVFcWdO3c4cuQIsbGxOU5Dz4+Ojg52dnb4+/uzf/9+bty4ga+vb7ZK3ruUkJDAkSNH1P4uX75MqVKliIqKUu6ZGTNmcOnSJbU8uV27dly5coXz58/TokULAMzMzAgPD8fExCRb6KU+ffqwdu1atLS0aNWqlfL68OHD2bt3LwEBAdy4cYPjx4/j4uLC06dP8w3f9F/UoEEDatSowQ8//JDj88HBwYHLly8zc+ZMrl+/zoULF5g0aRI3btygZs2aVK9enc6dOzN79mx++eUX5XmWW3mrSpUqvHjxgl27dnH37l2OHj2qrGNSUBa3fB+eP3/O/fv3uX//Pvfu3eP06dN4eHhQqVIl+vXrR9GiRdm1a5fy3JgwYQL3799/62PUv39/GjdujIuLy1ttw9bWlr///psZM2YQFxfH8ePHmT17NvDmg6s+VePHj6dmzZrMmjWLqlWr5lsmUtV/rl27xt9///1W9RehmdGjR5OYmMjQoUM5evQod+7c4dy5c3h6enL+/HllLcb88o9atWrRsWNH3NzcOH78ODdu3GDmzJmkpKRgZmaW7Zx+iPLYh6RpvQgyBvOtX7+eR48e5VrH0CS/yKsd4V3Upz5mmhyPhIQEZs2axfHjx7lz5w67d+/m7t27auXS06dP4+/vz40bN9i8eTNr165V1vXLSldXl3v37nH79m0qVKjwj/K2rPcXZDTU9uzZkyVLlmBoaFjgOts0uSfCwsL+lfL7iBEjWLVqlVIPXrt2LVFRUbRv355KlSqhr6/PTz/9xPXr1/n111+ZMWNGnm1cWdtvGjduzM6dOzl16hQ3btzA1dU126DTrLp3706ZMmUYM2YM58+fJy4uDhcXFw4fPpzvgM+PVX75SEpKCs7OzjRr1oxvvvmGuXPncvnyZZYtWwbkfJ3nJr8y+qdGk/YeVSjfvLzruuK7aK9+3wrWfEOhpkiRIgQHB+Pj44OTkxMvXrygTp06BAQEKFNavb29mT17Nn369KFEiRIYGxvj5uaGq6ur2sJJKvXq1cPCwoIJEyYwceJEtZiuuZk4cSJFixbF1dWVp0+fYmBgwLJly6hTpw7jxo3jwYMHyjS+unXr4uHhgbOzMzExMbmOTHJwcEBbW5s1a9bg5eVF+fLl6d27t1rsv09J4cKFWbBgAbt27WLLli2sXr2ap0+fUqFCBZo2bUpISIiyfkVISAheXl5K+JgmTZqwbt06tcZ2HR0drK2tiYiIUMIDqAwYMIApU6bQvXt3jh8/nu/05o9FWFgYLVu2zPGa0tfXp0OHDkRGRnLkyBFu376Ns7MzhQoVokuXLgwZMkRtZLGdnR1eXl789ttvhIeH89NPP+Hl5cXYsWNJSUmhYcOGrFy5Mltsyjcxbdo0ypYty/z583nw4AE1atRg5syZyoKAKqNGjcLFxYWHDx/SvHlzVqxYoVHHpJ+fH35+fsyaNYsnT56gr6/PnDlz6NOnz1unWVM6OjqEhIQQHBzM8uXLuXv3Ltra2jRs2JBVq1YpsWz/6XGtW7cuAQEBBAYGEhoaSuHChWnRogXLly9XQpI4ODiwYsUK4uPjmTZtWo7bcXV1xc3NjQEDBlCuXDns7e15/vy58n7lypUJDg7G19eXXr16Ubp0abp06aJURjt27MjGjRvp3r07ISEhatvW19dnzZo1+Pr60r9/f7S0tDA3N8fHx+edru+T3/WlSbq+++47tLW1mT17Ns+ePcPa2jrf9XreZZp1dHQICgrC09MTGxsbtLS0aNeuHZMnTwbyzzfMzc1xd3cnODgYPz8/ihcvjoWFBVOnTgUyFqx99eoVc+bM4cGDB1SsWBFbW1tGjhz5Vr9n/PjxpKamMn36dF68eIGlpSVWVlZKDN13LSIigoiICLXXKleuzMKFC/H09FQ6uZo3b87EiRNZsmQJz58/R1dXl88//xwDAwOePHmiTOFv2bIlfn5+OV4D1tbWzJ07l549e6rlKZ07d8bPz4+lS5eydOlSypQpg6WlJc7Ozu/2x3/ErK2tWbx4cY4NYyYmJqxYsYKFCxfSu3dvdHR0aNGiBVOmTFHyBS8vL7y9vXFyciI9PZ3+/furzfzLrHPnzly6dAkvLy+SkpLQ09OjX79+REVFERMToyy6+akLDg4mODgYyCiPlS1bliZNmuDr60vlypXx9PQkICCAtWvXUrFiRdq2bcuQIUOIiop6q1F0hQoVYs6cOfTo0YPAwMBs+X5+ypcvz4oVK/Dw8KBHjx5UqVIFGxsbvL298w3T8F+hra3NnDlzGDx4sFIeyqtMVLZsWfr06YO3tzcJCQlvXX8R+WvQoAGbNm1i0aJFuLi48OjRI0qUKMFXX33F+vXrlcZBTfKPefPmKc+75ORkjI2NCQ4OVkLFZD6n06dP/yDlsQ9F03rR1KlT6dSpE7Nnz6Z9+/Y5rgEImuUXebUjFC1a9J3Upz5WmhwPNzc3vLy8cHZ25vHjx+jp6TFp0iR69OihbMfKyorffvuNHj16UKlSJaZOnZpr3t2zZ0/27dtHt27d2Ldv3z/K27I+M6dPnw5kdPAtWbKkwM2SgTdrK/in5fdBgwaRnJyMv78/9+/fp2bNmvj5+SkDnXx8fHB3d6dnz55UrVqVcePGKQ3BOcnafuPk5MSTJ08YPnw4Ojo69OvXjy5duuR53kqVKkVISAje3t4MGzaMtLQ0GjRoQFBQ0CfbKZNfPuLn50diYiJLliwBMmZVjBs3Dj8/P1q3bo2hoaHadd6hQ4dc96VJGf1Toml7T37eR13xXbRXv0+FXheUOTtCCCGEEB+Jffv20aRJE7U4wg4ODlSpUgUPD48PmLJ/X2JiIh06dGDXrl2f5GgwIQqS69ev8+TJE5o0aaK8dvbsWWxsbDh06NAnt/aiEEKI7Ozs7NDT08PT0/NDJ0Vx6tQphg8fzs8//5xrB19B918qvwshCj6ZKSOEEEII8YaCgoIIDQ1l8uTJlCxZkqioKE6cOKGMyP8U/P7778TExBAaGkrr1q2lQ0aI9+DPP/9kxIgRuLu789VXX3Hv3j3mzZtHs2bNpENGCCHEexcXF8e1a9dYsmQJvXr1+mg7ZOC/UX4XQnw8ZKaMEEIIIcQbSkxMxNPTk1OnTvHy5Uvq1q2Lo6NjnlPfPzaxsbEMHDiQmjVrEhgYqNGCjkKIfy40NJQ1a9aQmJhIqVKlaNeuHZMmTdIo7LAQQoiPX0GaKRMVFcV3332HkZERgYGBH3VIwP9C+V0I8fGQThkhhBBCCCGEEEIIIYQQQoj3oPCHToAQQgghhBBCCCGEEEIIIcR/gXTKCCGEEEIIIYQQQgghhBBCvAfSKSOEEEIIIYQQQgghhBBCCPEeSKeMEEIIIYQQQgghhBBCCCHEeyCdMkIIIYQQQgjxAb1+/fpDJ0EIIYQQQgjxnkinjBBCCCGEEKLAO3PmDGPHjsXc3JxGjRphZWXF9OnTiYuL+9BJ+0euX7+OjY2N2msGBgYEBAR8oBQJIYQQQggh3iXplBFCCCGEEEIUaMuWLWPgwIE8f/4cFxcXgoKCcHR0JDY2ll69ehEZGfmhk/jWdu3axblz59Re27BhA/369ftAKRJCCCGEEEK8S0U/dAKEEEIIIYQQIjcHDx5k/vz5jBo1ivHjxyuvN2vWjJ49e/Ldd98xdepU6tWrxxdffPEBU/rvMTEx+dBJEEIIIYQQQrwjMlNGCCGEEEIIUWAFBgZSq1Ytxo0bl+29YsWK4ebmRpEiRVi+fDkAqamp/Pjjj7Rv3x4jIyO6du3Kli1b1L4XGRlJ7969MTY2pm3btvj4+JCSkgJAQEAABgYG2faVOaRYYmIiBgYGREZG4ujoiLGxMRYWFgQEBJCenq585+XLl8yfP5+OHTtiaGhI48aNsbe35/Lly8q+AgMDs20/a/iye/fu4eLigoWFBUZGRvTt25eoqKhs6Vu7di3Tpk2jWbNmmJqaMm7cOB48ePBmB1wIIYQQQgjxTkmnjBBCCCGEEKJAevjwIRcvXsTS0pJChQrl+JmyZcvSsmVLpZNiypQpLFu2jL59+7J06VIsLCz4/vvv2bZtGwDr169n4sSJNGjQgMDAQEaOHEloaCiurq5vnD5XV1dKlixJQEAAPXv2ZNGiRXh7eyvvT548mc2bNzNixAiCg4OZOnUq165dw8nJidevX9OvXz/69u0L5B6y7MGDB/Tt25fo6GicnJwICAhAT0+P0aNHEx4ervZZPz8/0tPT+eGHH5g8eTKHDh3Cw8PjjX+XEEIIIYQQ4t2R8GVCCCGEEEKIAunOnTsAVKtWLc/P1ahRg6ioKM6cOUNkZCTTpk1j8ODBAJiZmXH37l1OnjxJ9+7dCQgIoEOHDri7uyvfT05OZuvWrcpsGU19+eWX+Pr6AtCmTRueP39OSEgIo0aNonjx4jx79owZM2bQpUsXICPk2rNnz/D09OT+/ftUqVKFKlWqALmHLPvpp594+PAhu3btQl9fHwALCwuGDBmCt7c33bp1o3DhjLF29erVY968ecp3Y2Ji2L179xv9JiGEEEIIIcS7JTNlhBBCCCGEEAXS69evgYwwZXkpUqQIAKdOnQKgQ4cOau8vWLCAefPmcePGDR48eED79u3V3h8yZAjbt29HS0vrjdLXvXt3tf936tSJ1NRUzp8/j5aWFkFBQXTp0oV79+5x6tQpNmzYwMGDB4GMMGuaiI6OxtTUVOmQybzv+/fvEx8fr7yWtWOnSpUqvHjx4o1+kxBCCCGEEOLdkpkyQgghhBBCiAJJT08P+L8ZM7m5ffs2urq6SidO+fLlc/zc48eP83z/TVWqVEnt/+XKlQPg6dOnAPz88894eHgQHx9PiRIlMDAwoESJEsD/dTjl58mTJznOFKpQoYLavgB0dHTUPlO4cGGN9yOEEEIIIYR4P2SmjBBCCCGEEKJAKl++PCYmJuzduzfXzoWkpCSOHTuGlZUVpUuXBjLWosksPj6e06dP5/r+48ePOXbsGM+ePVPWrklLS1Pef/bsWY77VnXyqPz1119Kum/dusXo0aOpX78++/bt4+zZs6xbtw5LS0sNf32GMmXK8ODBg2yv379/H8hYU0cIIYQQQgjx8ZBOGSGEEEIIIUSBNWbMGOLj41mwYEG299LS0pg1axYvX75k2LBhNGnSBID9+/erfc7Pz485c+ZQu3ZtypYtS1RUlNr7ERERDB8+nOTkZEqWLAnA77//rrx/9uzZHNN24MABtf/v2bMHHR0djI2NuXjxIsnJyYwcOZLq1asrn/n555+B/5spo1oPJjdfffUV586d4/bt22qvh4eHU7FiRWrUqJHn94UQQgghhBAFi4QvE0IIIYQQQhRYrVu3ZurUqXh7exMbG0uvXr2oVKkSiYmJrFu3jsuXL+Pu7k79+vUB6Ny5M76+vrx8+ZKGDRty9OhR9u3bx4IFCyhSpAhjx45l9uzZuLq60qFDB27evMmCBQuwsbGhXLlyWFhYMG/ePGbMmMHw4cP5448/CAwMVMKOZbZ7924qVKiAhYUF0dHRrF27FicnJ3R1dWnYsCFFixbFx8cHBwcHUlJSCAsL49ChQwA8f/4cQJm9s2PHDoyNjbOtHWNvb094eDj29vaMGTOGsmXLsm3bNk6cOIGHh0e+nTpCCCGEEEKIgkU6ZYQQQgghhBAFmr29PaampqxatQovLy8ePnxIxYoVMTc3x93dnbp16yqf9fHxITAwkDVr1vDo0SNq1arFggUL6Ny5MwADBw5EV1eXoKAgNm/eTOXKlXFwcGDEiBEA1KpVCy8vLxYvXsyIESOoU6cOc+bMYc6cOdnSNX78eKKjo9mwYQOff/45M2fOxMbGBoAaNWowf/58AgMD+d///keZMmUwMTFhzZo12NnZcfr0aQwMDOjYsSPbt29n6tSp9O3bF1dXV7V9VKxYkXXr1jF//nzc3d1JTU2lfv36LFq0CCsrq3d0xIUQQgghhBDvSqHXsvKjEEIIIYQQQmgsMTERKysr5s2bR+/evT90coQQQgghhBAfEZnrLoQQQgghhBBCCCGEEEII8R5Ip4wQQgghhBBCCCGEEEIIIcR7IOHLhBBCCCGEEEIIIYQQQggh3gOZKSOEEEIIIYQQQgghhBBCCPEeSKeMEEIIIYQQQgghhBBCCCHEeyCdMkIIIYQQQgghhBBCCCGEEO+BdMoIIYQQQgghhBBCCCGEEEK8B9IpI4QQQgghhBBCCCGEEEII8R5Ip4wQQgghhBBCCCGEEEIIIcR7IJ0yQgghhBBCCCGEEEIIIYQQ74F0ygghhBBCCCGEEEIIIYQQQrwH/w8e1cPm8B9bGAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "OA = sns.countplot(data = df, x = 'Occupation')\n",
    "for bars in OA.containers:\n",
    "    OA.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "28914c19-0dac-4a76-9011-d6ceb46bef70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABmUAAAHECAYAAADI/i5zAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAACk9klEQVR4nOzdd3iUVd7G8XsmhfRCKKGFKiDSJRIQBBFEQFcRdy2AQgBBqqChGFRAmvQECFJCQEQpgqigu7LoWjELuIpIr9JCKCmkM5m8f/AyOoaaTGYmyfdzXV5Lzjlz8jtzdoZkbs7zGPLy8vIEAAAAAAAAAACAImV0dAEAAAAAAAAAAAClAaEMAAAAAAAAAACAHRDKAAAAAAAAAAAA2AGhDAAAAAAAAAAAgB0QygAAAAAAAAAAANgBoQwAAAAAAAAAAIAdEMoAAAAAAAAAAADYAaEMAAAAAAAAAACAHbg6uoDiKi8vT2ZznqPLAAAAAAAAAAAADmY0GmQwGG45jlCmgMzmPF26lO7oMgAAAAAAAAAAgIOVLestF5dbhzJcvgwAAAAAAAAAAMAOCGUAAAAAAAAAAADsgMuXlTDnziXohRee0dSps9S8eQtL+4sv9tHevXvyjX/nnTg1bNhIkpSQcFYLF0bpf//bpbw8sxo3bqqhQ0eqSpWqlvHJyclavHiBfvjhO2VkpKtBg4YaNGio7r77nqJfHAAAAAAAAAAAxRihTAmSkHBWo0YNVVpamlW72WzW0aOH9dxzvfXAAx2s+mrVqi1JysrK0ssvD1Fubq5GjoyQu7u7li17R8OGDdTKlWvk6+srs9msceNG6ezZsxo0aKjKlg3S2rXva/jwQVq+fLWqVQux21oBAAAAAAAAAChuCGVKALPZrM8/36yFC6Ou23/y5O/KyspSq1ZtLKdi/mr37p916tTvmjcvRi1a3CdJCgmpoZ49n9J3332tLl0e1U8/7dSvv+7WzJlRatXqfklSkybN1K3bQ9qy5RMNGjS0aBYIAAAAAAAAAEAJwD1lSoAjRw5p9uzp6tKlm15/fWK+/kOHDkiS6tSpe8M5cnJyJEne3t6WNn//AElSSkqyJOmeexpp0aJYhYa2tIxxdXWVwWDQlSs5hV0GAAAAAAAAAAAlGidlSoCKFYO1Zs1HqlChon76aWe+/kOHDsrHx0fR0bP1/fffKisrU82bt9Dw4aMUElJDkhQa2lK1atVWTEy0xo59XR4eHoqOni1PTy+1bdtekuTp6alGjZpIkkwmk86cOa3ly5coLy9PXbv+zV7LBQAAAAAAAACgWCKUKQH8/Pzl5+d/w/5Dhw4qLS1NAQGBmjZtlhISzioubqkGDx6gFSveV7ly5VWmTBmNHh2pMWNG6umnn5Akubu76+2356hKlar55pw9e7o+/XSTJKlv3wGWe9MAAAAAAAAAAIDrc6rLl8XExKh3795WbYmJiRo1apRatGihli1b6pVXXtGlS5cs/WazWdHR0Wrbtq2aNGmi8PBwnThxwmqOffv2qVevXmratKnat2+v2NhYu6zHWbz00lDFxCzTkCEj1KRJM3Xu3FWzZ89Xenqa1q37QJL00087NXz4INWpU1czZszTrFnRuu++ML32WoR++eV/+eb829+6a/78xerbd4BWrYrTjBlT7b0sAAAAAAAAAACKFacJZVasWKHo6GirtpycHIWHh+vkyZOKi4vT4sWLtXfvXo0ZM8YyJiYmRmvWrNHkyZO1du1aGQwGDRgwwHKPlKSkJPXt21c1atTQhg0bNGzYMEVFRWnDhg12XZ8j3XVXPTVu3NSqrUqVqqpevaYOHz4kSVq1Kk7lypXXzJlRat26jcLCWmvq1FmqUaOWoqPn5Jvz7rvvUbNm96pfv4Hq1auPNm/epMTEc/ZYDgAAAAAAAAAAxZLDQ5lz586pf//+ioqKUs2aNa36Nm/erNOnT2vRokVq1KiRmjZtqtdee03Hjh1TWlqacnJytHz5cg0bNkzt2rVT/fr1NXfuXJ07d05bt26VJK1bt07u7u6aMGGCateurR49eqhPnz5aunSpI5ZrdyaTSZ999qn27Pk1X192dpYCAgIkSQkJZ1W/fgO5u7tb+o1Go5o0aaZjx45Kko4ePaItWz7JN0/9+g2Ul5enxMTEolkEAAAAAAAAAAAlgMNDmd9++03+/v765JNP1KRJE6u+b7/9VmFhYSpXrpylrW3btvr3v/8tHx8f7d+/X+np6QoLC7P0+/n5qUGDBtqxY4ckaefOnQoNDZWr6x+3zwkLC9OxY8d08eLFIl6d47m6uio2drEWLbI+hXTgwH6dPn1KzZrdK0mqXr2G9u37zXLCSJLy8vL022+7VblyZUnS3r17NG3aJP366y9Wc8XHb5e7u7uqV69RtIsBAAAAAAAAAKAYc731kKLVoUMHdejQ4bp9x48fV4sWLbRw4UJt2rRJJpNJbdq0UUREhPz8/JSQkCBJqlSpktXjKlSooLNnz0qSEhISVLdu3Xz9knTmzBkFBQUVuHZXV4dnWvm4uBgt/3utvv79X9SUKZM0depEde7cRWfPntHSpe+oTp279Nhjf5Orq1H9+r2ogQPDFRExXE8/3VOuri769NOPtWfPr5oyZYZcXY3q3PkRrVnzniZOjNSAAS+pXLly+uab/2jTpg81aNAQBQb6O3LpAAAAAAAAAAA4NYeHMjeTlpamTZs2qVWrVpo9e7ZSUlI0bdo0DR48WKtWrVJmZqYkWV1yS5LKlCmjlJQUSVJWVtZ1+yUpOzu7wLUZjQYFBnoX+PFFxdfXw/K/1+p7/vmeCgoKUGxsrMaOfUWenp7q1KmTRo0apYCAq0FK69aheu+99xQVFaUJEyLl5uamevXq6d1339V9990nSQoM9NZ7763S3LlztWRJjJKSklS7dm1Nnz5dTzzxhEPWCwAAAAAAAABAceHUoYybm5u8vLw0e/Zsubm5SZL8/f3197//Xb/++qs8PK4GEDk5OZY/S1fDFk9PT0mSh4eH1SW5rvVLkpeXV4FrM5vzlJqaUeDHF5W6dRvqxx9/kiQlJaVb2lu3bq/Wrdtbjc3Lsx4TElJHs2fPzzfnn8e4unopIiJSERE3HgMAAAAAAAAAQGni5+dpuZLVzTh1KBMcHCyz2WwJZCTprrvukiSdOnVKVatWlSQlJiYqJCTEMiYxMVH169e3zPHXG9Bf+7pixYqFqs9kMhfq8QAAAAAAAAAAoPRw6lCmRYsWevfdd5WVlWU5CXPw4EFJUvXq1XXXXXfJx8dH8fHxllAmNTVVe/fuVa9evSRJoaGhWrNmjXJzc+Xi4iJJ2r59u2rWrFmo+8ncLqPRIKPRUOTfpzQwm/NkNuc5ugwAAAAAAAAAAArEqUOZZ555RqtXr9Yrr7yiESNG6PLly5owYYJatmype+65R5LUq1cvzZo1S2XLllWVKlU0c+ZMBQcHq1OnTpKkHj16aNmyZYqMjFT//v21e/durVy5UhMnTizy+o1Gg8oGeslgvPWRJdxantmsS0kZBDMAAAAAAAAAgGLJqUOZsmXLavXq1Zo2bZr+8Y9/yN3dXR07dtS4ceMsY4YPHy6TyaTx48crKytLoaGhio2Nlbu7uyQpKChIy5Yt05QpU9S9e3eVL19eo0ePVvfu3Yu8fqPRIIPRqNR/f6fcpNQi/34lmUugn/w6tpHRaCCUAQAAAAAAAAAUS4a8vDw+4S6A3FyzLl26+c3tXV2NCgz0VtL6z2S6cMlOlZVMruXKKvDvXZWUlM69fAAAAAAAAAAATqVsWW+5uNz6qllcVwsAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQA7OXcuQY880l4//bTzhmPWrftAbdq00NmzZ6zaMzLSNXPmVP3tb53VsWMbvfzyYB07dtRqTGZmphYujNJTTz2mTp0e0MCBfbVz53+LZC0AAAAAAAAAgDtHKAPYQULCWY0cOURpaWk3HHPy5O9avHjBdfsmTIjU119/pUGDhur11ycpKSlJI0a8pNTUFMuYmTOn6qOP1usf/3hOU6fOUPny5fXKK8O0Z8+vNl8PAAAAAAAAAODOEcoARchsNmvLlk8UHt5LycnJNxyXm5urKVMmyN8/IF/fnj279cMP3yky8k117fqY2rXroHnzYpSZmaGNG9dLkrKzs7Rt2xd6+ume+sc/nlVoaJgmTJiqoKBy+uSTjUW0OgAAAAAAAADAnSCUAYrQkSOHNHv2dHXp0k2vvz7xhuM++GCVLl26qJ49X8jXFx+/XZ6engoNDbO0BQYGqmnT5vrxxx8kSVeumGQ2m+Xt7W0Z4+rqKm9vb6WkJNtuQQAAAAAAAACAAiOUAYpQxYrBWrPmIw0bNkplynhcd8zRo0e0fPlSjRv3hjw9PfP1nzhxXJUrV5Grq6tVe5Uq1XTy5AlJko+Pjx599HGtX79Ge/bs1uXLl/XBB+/p2LGjevjhrrZfGAAAAAAAAADgjrneegiAgvLz85efn/8N+00mk6ZMmaDHHntczZrdq7Nnz+Qbk5Z2WV5e3vnavby8lJ6ebvm6X79BOnBgvwYNCre09e8/SA891KmQqwAAAAAAAAAA2AKhDOBA7767XJcvp2rQoGE3HGM2m2UwGPK15+XlyWi8etgtKemSXnzxBbm6uur11yepfPkK2r79e8XFLZWHh4eeeaZXka0BAAAAAAAAAHB7CGUABzl4cL9WrYrTzJlRcnNzk8l09b4w0tUgJjc3Vy4uLvLx8dXJk7/ne3xmZqa8vX0kSZ9+ukmJief0wQcbVa1aiCSpefMWkqTFixeqS5dH5e8fYJ+FAQAAAAAAAACui1AGcJBvv/1aV65c0csvD87X9/TTT6hp0+ZasGCJQkKq67///VFms9lyMkaSTp8+qRo1akqSEhLOKjCwrCWQuaZp0+b64INVOnXqFKEMAAAAAAAAADgYoQzgII8//qTuv7+tVdv333+ruLilmj59jkJCrgYs990XpnffXa74+O1q1ep+SVJSUpJ+/vknPf/81fvHVK9eQ59+ukm//35cISE1LPP9+usvMhqNCg4Ots+iAAAAAAAAAAA3RCgDOEi5cuVVrlx5q7ajR49IkmrXrqNKlSpLunrapVmzezVp0usaPHi4/Pz8tXz5Evn4+Orxx3tIkh599HFt2LBOr7wyQuHhA1ShQkXt2BGvDz5YpSef/LuCgsrZd3EAAAAAAAAAgHyMtx5iPzExMerdu/cN+8ePH68OHTpYtZnNZkVHR6tt27Zq0qSJwsPDdeLECasx+/btU69evdS0aVO1b99esbGxRVI/UFSmTJmptm3baeHCKE2dOkHly5dXVFSM/Pz8JEne3j6KiYlVkyZNtXDhPI0ZM1I//viDXnllrEaMeNXB1QMAAAAAAAAAJMmQl5eX5+giJGnFihWaPn26QkNDtWrVqnz9//73vzVkyBBVqVJFX375paV9wYIFev/99zVt2jRVrFhRM2fO1MmTJ7V582a5u7srKSlJXbp0UceOHdW3b1/9/PPPmjhxot5880316NGjwPXm5pp16VL6Tce4uhoVGOitpPWfyXThUoG/FyTXcmUV+PeuSkpKl8lkdnQ5AAAAAAAAAABYlC3rLReXW5+Dcfjly86dO6fIyEjt2rVLNWvWvO6YxMREvf7667rvvvt0+vRpS3tOTo6WL1+uiIgItWvXTpI0d+5ctW3bVlu3blW3bt20bt06ubu7a8KECXJ1dVXt2rV14sQJLV26tFChDIo/o9Ego9Hg6DJKBLM5T2azU+S7AAAAAAAAAOC0HB7K/Pbbb/L399cnn3yihQsXWoUukpSXl6exY8fq8ccfl7e3tz766CNL3/79+5Wenq6wsDBLm5+fnxo0aKAdO3aoW7du2rlzp0JDQ+Xq+sdSw8LCtHjxYl28eFFBQUFFv0g4HaPRoLKBXjIYneoKfsVWntmsS0kZBDMAAAAAAAAAcBMOD2U6dOiQ7z4xf7ZixQqdP39e77zzjhYvXmzVl5CQIEmqVKmSVXuFChV09uxZy5i6devm65ekM2fOFCqUcXW9+Qf6t3NUCXfGVs+pi4tRBqNRSVvfk+nSOZvMWVq5lq2owE695ObmotxcLi0HAAAAAAAAADfi8FDmZvbv368FCxZo9erVcnd3z9efmZkpSfn6ypQpo5SUFElSVlbWdfslKTs7u8C1GY0GBQZ6F/jxKBg/P0+bzme6dE5XLpy+9UDckq33BgAAAAAAAABKGqcNZbKzs/Xqq6/qpZdeUv369a87xsPDQ9LVe8tc+/O1x3p6elrG5OTk5Jtbkry8vApcn9mcp9TUjJuOcXEx8kG1jaWmZtrkNAZ7Y3u22hsAAAAAAAAAKG78/Dxv60pPThvK/PLLLzp06JAWLFighQsXSpKuXLkik8mkZs2aaeLEiapRo4YkKTExUSEhIZbHJiYmWoKc4OBgJSYmWs197euKFSsWqkaTiQ+g7S0318zz7qTYGwAAAAAAAAC4OacNZRo3bqwvvvjCqm3VqlX64osvtGrVKgUFBcnd3V0+Pj6Kj4+3hDKpqanau3evevXqJUkKDQ3VmjVrlJubKxcXF0nS9u3bVbNmzULdTwYAAAAAAAAAAOBOOG0o4+HhoerVq1u1+fv7y9XV1aq9V69emjVrlsqWLasqVapo5syZCg4OVqdOnSRJPXr00LJlyxQZGan+/ftr9+7dWrlypSZOnGjX9QAAAAAAAAAAgNLNaUOZ2zV8+HCZTCaNHz9eWVlZCg0NVWxsrNzd3SVJQUFBWrZsmaZMmaLu3burfPnyGj16tLp37+7gygEAAAAAAAAAQGliyMvLy3N0EcVRbq5Zly6l33SMq6tRgYHeSlr/mUwXLtmpspLJtVxZBf69q5KS0m1y35Jre3N+7WxduXDaBhWWXm7lqqj806/YbG8AAAAAAAAAoLgpW9ZbLi7GW4679QgAKOHOnUvQI4+0108/7bRq/+ab/yg8vJc6dmyjHj0eVWzsYl25cuWG88yfP0dDh7543b41a97TP/7xuDp0aK0+fZ7Tt9/+x3YLAAAAAAAAAFAsEMoAKNUSEs5q5MghSktLs2rfvv07RUZG6K676mratNl67rneWrNmtebMmXHded57b4XWrn3/un3vv/+uFi2ar65dH9OUKTNVtWpVjR8/Rj///JPN1wMAAAAAAADAeRX7e8oAQEGYzWZ9/vlmLVwYdd3+VatW6O6779G4cW9IkkJDWyo5OVnvvrtcw4ePkqenpyTpzJnTmj9/rn744Vv5+Pjkmyc7O0vvvhunp59+Tn369JckhYW11qBB4YqLW6aoqJgiWiEAAAAAAAAAZ8NJGQCl0pEjhzR79nR16dJNr78+MV9/ZOQEjR8/warNzc1NZrNZJpPJ0jZ//hydPn1SUVGLVKdO3Xzz/PbbHqWlXdYDD3SwtBkMBrVr96B+/nmXsrOzbLcoAAAAAAAAAE6NkzIASqWKFYO1Zs1HqlChYr57yUhSlSpVLX9OS0vTzp3x+uCD9/Tww4/I19fX0jdgwEuqWbO2DAbDdb/PiRPHJUkhISF/mb+acnNzdfr0KdWqVccGKwIAAAAAAADg7AhlAJRKfn7+8vPzv+W48+cT1b17V0lSpUpVFB4+0Kr/VoFKWtplSZKXl7dVu5eXlyQpPT39tmsGAAAAAAAAULxx+TIAuAkPD09FRS3SlCkz5e/vr379euvYsaO3/Xiz2XyDnjxJksHA2zAAAAAAAABQWvBpIADchK+vr+69N1Tt2j2oOXMWSMrTunXv3/bjfXyuXuosIyPDqj0jI/P/+31sVisAAAAAAAAA50YoAwB/YTKZtG3bFzp4cL9Vu5+fnypXrqpz587d9lwhIdUlSadPn7RqP336pNzd3VW5cpXCFwwAAAAAAACgWCCUAYC/cHV1VUxMtBYtmm/VnpCQoBMnjqlOnbtue65GjZrI09NTX321zdKWl5enr7/+Sk2bNpe7u7vN6gYAAAAAAADg3FwdXQAAOKPw8Bc1bdokvf32ZHXo0EkXLpzXihXL5O8foGef7XXb83h4eOiZZ3ppxYplcnNzU8OGjbVlyyc6cGCfoqPfKcIVAAAAAAAAAHA2hDIAcB3duv1Nnp5eWr16pbZu/ac8PDzUsmVrDRo0VIGBZe9orvDwF+Xi4qJPPvlIH3zwnmrUqKnp0+eoceOmRVM8AAAAAAAAAKdkyMvLy3N0EcVRbq5Zly6l33SMq6tRgYHeSlr/mUwXLtmpspLJtVxZBf69q5KS0mUymQs/3//vzfm1s3XlwmkbVFh6uZWrovJPv2KzvQEAAAAAAACA4qZsWW+5uNz6jjGclAHglIxGg4xGg6PLKPbM5jyZzWTvAAAAAAAAgDMglAHgdIxGgwIDPWU0uji6lGLPbM5VUlImwQwAAAAAAADgBAhlADidq6dkXHToy1nKTDrp6HKKLc/Aarqrw6syGg2EMgAAAAAAAIATIJQB4LQyk04q/eIRR5cBAAAAAAAAADZx67vOAAAAAAAAAAAAoNAIZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7MCpQpmYmBj17t3bqu3LL79Ujx491KxZM3Xo0EFvv/22srKyLP1ms1nR0dFq27atmjRpovDwcJ04ccJqjn379qlXr15q2rSp2rdvr9jYWLusBwAAAAAAAAAA4BqnCWVWrFih6Ohoq7adO3dq6NCh6ty5szZt2qQJEybo888/18SJEy1jYmJitGbNGk2ePFlr166VwWDQgAEDlJOTI0lKSkpS3759VaNGDW3YsEHDhg1TVFSUNmzYYNf1AQAAAAAAAACA0s3hocy5c+fUv39/RUVFqWbNmlZ9a9asUVhYmF588UVVr15dDzzwgEaOHKlPPvlEOTk5ysnJ0fLlyzVs2DC1a9dO9evX19y5c3Xu3Dlt3bpVkrRu3Tq5u7trwoQJql27tnr06KE+ffpo6dKljlguAAAAAAAAAAAopVwdXcBvv/0mf39/ffLJJ1q4cKFOnz5t6QsPD5fRmD83MplMSktL06lTp5Senq6wsDBLn5+fnxo0aKAdO3aoW7du2rlzp0JDQ+Xq+sdSw8LCtHjxYl28eFFBQUEFrt3V9eaZlouLwzOvEsdWzyl7Y3u2fE7ZH9vi+QQAAAAAAACcg8NDmQ4dOqhDhw7X7WvQoIHV1zk5OYqLi9M999yjsmXLaufOnZKkSpUqWY2rUKGCzp49K0lKSEhQ3bp18/VL0pkzZwocyhiNBgUGehfosSg4Pz9PR5eAG2BvnBd7AwAAAAAAADgHh4cyt8tkMmn06NE6fPiwVq9eLUnKzMyUJLm7u1uNLVOmjFJSUiRJWVlZ1+2XpOzs7ALXYzbnKTU146ZjXFyMfBhqY6mpmcrNNRd6HvbG9my1NxL7Y2u23BtHOHcuQT17/kNvvz1H997b4rb7vv76Ky1fvlQnThxXQECAunV7TH369Jebm5sk6aWXBuh//9t1w+/7448/2X4xAAAAAAAAKJH8/Dxv64o1xSKUSUtL08svv6z4+HhFR0erSZMmkiQPDw9JV0/QXPuzdDVs8fT0tIzJycmxmu9aGOPl5VWoukym4vshZ3GVm2vmeXdS7I3zKs57k5BwVqNGDVVaWlq+ddysb/v27zR27Kvq2vUxDRo0TL//flzvvLNQiYkXNGZMpCRp1KgxSk9Pt/p+Z86c0uTJb+pvf+tebJ8zAAAAAAAAOC+nD2USExM1YMAAnTp1SkuXLrW6f8y1y5YlJiYqJCTE6jH169eXJAUHBysxMTHfnJJUsWLFoi4fAFAAZrNZn3++WQsXRt1R3zWrVq3Q3Xffo3Hj3pAkhYa2VHJyst59d7mGDx8lT09P1axZy+oxJpNJ8+bNVJ06d2nEiFdtuyAAAAAAAABAklPf/TklJUUvvPCCLl26pPfff98qkJGk+vXry8fHR/Hx8Za21NRU7d27Vy1aXL2MTWhoqHbt2qXc3FzLmO3bt6tmzZoFvp8MAKBoHTlySLNnT1eXLt30+usTb7vvmsjICRo/foJVm5ubm8xms0wm03Uf8/HHG3Tw4H69+uo4yyXOAAAAAAAAAFty6pMy06ZN08mTJ7Vs2TKVLVtW58+ft/SVLVtW7u7u6tWrl2bNmqWyZcuqSpUqmjlzpoKDg9WpUydJUo8ePbRs2TJFRkaqf//+2r17t1auXKmJE6//QR4AwPEqVgzWmjUfqUKFivrpp5233XdNlSpVLX9OS0vTzp3x+uCD9/Tww4/I19c33/iMjAzFxi5R585d1aBBQ9suBgAAAAAAAPh/ThvKmM1mffbZZ7py5YpeeOGFfP3btm1T1apVNXz4cJlMJo0fP15ZWVkKDQ1VbGys3N3dJUlBQUFatmyZpkyZou7du6t8+fIaPXq0unfvbu8lAQBuk5+fv/z8/O+476/On09U9+5dJUmVKlVRePjA647bvPljpaVdVu/efQtWMAAAAAAAAHAbnCqUmT59uuXPRqNRu3fvvuVjXFxcFBERoYiIiBuOady4sdauXWuTGgEAxYeHh6eiohYpLS1Nq1bFqV+/3lq0KDbf/WQ2blyv++9/QCEh1R1UKQAAAAAAAEoDp76nDAAAheHr66t77w1Vu3YPas6cBZLytG7d+1ZjDh06qFOnftfDDz/imCIBAAAAAABQahDKAABKFJPJpG3bvtDBg/ut2v38/FS5clWdO3fOqv2HH76Vh4eHWrduY88yAQAAAAAAUAoRygAAShRXV1fFxERr0aL5Vu0JCQk6ceKY6tS5y6p97949qlu3vsqU8bBnmQAAAAAAACiFCGUAACVOePiL2rEjXm+/PVk7dsTr8883a8SIQfL3D9Czz/ayGnv06BHVqFHTQZUCAAAAAACgNHF1dAEAANhat25/k6enl1avXqmtW/8pDw8PtWzZWoMGDVVgYFmrsZcuXZSvr5+DKgUAAAAAAEBpQigDAHBqzZu30Hff7bzjvg4dOqpDh463nH/btu8LVR8AAAAAAABwuwhlAAB3xGg0yGg0OLqMEsFszpPZnOfoMgAAAAAAAGAnhDIAgNtmNBoUGOgpo9HF0aWUCGZzrpKSMglmAAAAAAAASglCGQDAbbt6SsZF8V+/rcspJx1dTrHm619NLduNkdFoIJQBAAAAAAAoJQhlAAB37HLKSSVfPOzoMgAAAAAAAIBixejoAgAAAAAAAAAAAEoDQhkAAAAAAAAAAAA7IJQBAAAAAAAAAACwA0IZAAAAAAAAAAAAOyCUAQAAAAAAAAAAsANCGQAAAAAAAAAAADsglAEAAAAAAAAAALADQhkAAAAAAAAAAAA7IJQBAAAAAAAAAACwA0IZAAAAAAAAAAAAOyCUAQAAAAAAAAAAsANCGQAAAAAAAAAAADsglAEAAAAAAAAAALADQhkAAAAAAAAAAAA7IJQBAAAAAAAAAACwA0IZAAAAAAAAAAAAOyCUAQAAAAAAAAAAsANCGQAAAAAAAAAAADsglAEAAAAAAAAAALADQhkAAAAAAAAAAAA7IJQBAAAAAAAAAACwA0IZAAAAAAAAAAAAOyCUAQAAAAAAAAAAsANCGQAAAAAAAAAAADtwqlAmJiZGvXv3tmrbt2+fevXqpaZNm6p9+/aKjY216jebzYqOjlbbtm3VpEkThYeH68SJE3c0BwAAAAAAAAAAQFFzmlBmxYoVio6OtmpLSkpS3759VaNGDW3YsEHDhg1TVFSUNmzYYBkTExOjNWvWaPLkyVq7dq0MBoMGDBignJyc254DAAAAAAAAAACgqLk6uoBz584pMjJSu3btUs2aNa361q1bJ3d3d02YMEGurq6qXbu2Tpw4oaVLl6pHjx7KycnR8uXLFRERoXbt2kmS5s6dq7Zt22rr1q3q1q3bLecAAAAAAAAAAACwB4eflPntt9/k7++vTz75RE2aNLHq27lzp0JDQ+Xq+kd2FBYWpmPHjunixYvav3+/0tPTFRYWZun38/NTgwYNtGPHjtuaAwAAAAAAAAAAwB4cflKmQ4cO6tChw3X7EhISVLduXau2ChUqSJLOnDmjhIQESVKlSpXyjTl79uxtzREUFFTg2l1db55pubg4PPMqcWz1nLI3tmfL55T9sS32xrnxnAIAAAAAAJQeDg9lbiYrK0vu7u5WbWXKlJEkZWdnKzMzU5KuOyYlJeW25igoo9GgwEDvAj8eBePn5+noEnAD7I3zYm+cG/sDAAAAAABQejh1KOPh4aGcnByrtmtBipeXlzw8PCRJOTk5lj9fG+Pp6XlbcxSU2Zyn1NSMm45xcTHyYZuNpaZmKjfXXOh52Bvbs9XeSOyPrbE3zs2W+wMAAAAAAADH8PPzvK0rojh1KBMcHKzExESrtmtfV6xYUSaTydIWEhJiNaZ+/fq3NUdhmEx8iGZvublmnncnxd44L/bGubE/AAAAAAAApYdTX8g+NDRUu3btUm5urqVt+/btqlmzpoKCglS/fn35+PgoPj7e0p+amqq9e/eqRYsWtzUHAAAAAAAAAACAPTh1KNOjRw+lpaUpMjJShw8f1saNG7Vy5UoNHDhQ0tV7yfTq1UuzZs3Stm3btH//fo0cOVLBwcHq1KnTbc0BAAAAAAAAAABgD059+bKgoCAtW7ZMU6ZMUffu3VW+fHmNHj1a3bt3t4wZPny4TCaTxo8fr6ysLIWGhio2Nlbu7u63PQcAAAAAAAAAAEBRc6pQZvr06fnaGjdurLVr197wMS4uLoqIiFBERMQNx9xqDgAAAAAAAAAAgKLm1JcvAwAAAAAAAAAAKCkIZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7MDV0QUAAIDi75NPPtK6dR8oIeGMKlYM1pNP/kNPPvl3GQwGSdKLL/bR3r178j3unXfi1LBho3zt+/fv08CBfTRmzHh17fpYkdcPAAAAAABgD4QyAACgUD79dJNmzJiip556Wm3atNPPP/+kefNmKjs7W88911tms1lHjx7Wc8/11gMPdLB6bK1atfPNl5OToylT3lRubq69lgAAAAAAAGAXBQplNm3apHbt2ikwMDBf3/nz57Vp0yYNGDCg0MUBAADnt2XLJ2rUqIlefjlCktSixX06efKEPvpovZ57rrdOnvxdWVlZatWqzXVPxfzVsmWLlJ6eXtRlAwAAAAAA2F2B7ikzbtw4nTx58rp9+/btU3R0dKGKAgAAxUdOTo58fHys2vz9A5SSkiJJOnTogCSpTp26t5xrz57d+vDDdRo1arTtCwUAAAAAAHCw2z4pM3DgQB0+fFiSlJeXpyFDhsjd3T3fuIsXLyokJMR2FQIAAKf29NPPaerUifrXvz7T/fc/oN9++1Wff75FjzzSTZJ06NBB+fj4KDp6tr7//ltlZWWqefMWGj58lEJCaljmyc7O0uTJE/T8831Vu/ZdDloNAAAAAABA0bmjUGb9+vWSpI8++kgNGjRQ2bJlrcYYjUb5+fnpySeftG2VAADAaT34YEft2rVDb731hqXtvvtaacSIVyRdDWXS0tIUEBCoadNmKSHhrOLilmrw4AFaseJ9lStXXpK0aNF8eXl5qlevPjp/PtEhawEAAAAAAChKtx3KNG/eXM2bN7d8PXjwYFWrVq1IigIAAMXH2LGv6Ndff9HgwcN199336MiRQ1q+fKlef32Mpk6dpZdeGqo+ffqpceOmkqQmTZqpYcPG6tXr71q37gMNHjxcP/20U5988pGWLFkpV9cC3fIOAAAAAADA6RXoU49p06bZug4AAFAM/frrL/rvf7drzJjxeuyxJyRJzZrdq8qVq2r06Jf1ww/f6f772+Z7XJUqVVW9ek0dPnxIGRkZmjZtknr2fEE1atSUyWSS2WyWJJnNZplMJoIaAAAAAABQIhToE45Lly5pypQp+s9//qPMzEzl5eVZ9RsMBu3du9cmBQIAAOeVkHBWktSoUROr9qZNr56uPXbsiFJSkhUSUkMNGzayGpOdnaWAgADt379XZ8+eUVzcUsXFLbUaM336W5o+/S19993OIlwFAAAAAACAfRQolJkwYYK+/vprdevWTcHBwTIajbauCwAAFAPVq9eQJP3yy/9Uo0ZNS/uvv/4iSapUqYpiYqIUHFxJCxf+EbgcOLBfp0+f0nPPPa/69e/WsmXvWs174cIFjR07Sn37DrjuSRsAAAAAAIDiqEChzLfffqvXXntNTz/9tK3rAQAAxUjduvXVvn0HLVgwV5cvp6pBg4Y6duyo4uKWqG7d+mrX7kFlZmZo+vS3NGXKBD388CM6e/asYmPfUe3addSly6NydXVV/foNrOY9e/aMJKlSpcr5+gAAAAAAAIqrAoUy7u7uqlatmq1rAQAAxdCbb07RypWx+vjjjYqNXayKFYPVtetj6tNngFxdXfXoo4/Lw8ND77+/SuPGvSoPD0898EB7DRo0lHvFAAAAAACAUqVAn4R06tRJmzdvVuvWrW1dDwAAKGbc3NzUv/8g9e8/6IZjOnbsrI4dO9/2nJUqVeY+MgAAAAAAoMQpUCjToEEDzZs3TydPnlSTJk3k4eFh1W8wGDRkyBCbFAgAAG6P0WiQ0WhwdBklgtmcJ7M5z9FlAAAAAACAEqZAocykSZMkSTt27NCOHTvy9RPKAABgX0ajQYGBnjIaXRxdSolgNucqKSmTYAYAAAAAANhUgUKZ/fv327oOAABQCFdPybjoX99P16WUk44up1gr619Nne8fK6PRQCgDAAAAAABsirvrAgBQglxKOanzSYcdXQYAAAAAAACuo0ChzLhx4245Ztq0aQWZGgAAAAAAAAAAoEQqUCgTHx+fry0jI0PJyckKCAhQo0aNCl0YAAAAAAAAAABASVKgUObLL7+8bvvRo0c1bNgwPfHEE4WpCQAAAAAAAAAAoMQx2nKyWrVqaciQIVqwYIEtpwUAAAAAAAAAACj2bBrKSJKPj49Onz5t62kBAAAAAAAAAACKtQKFMmfOnMn338mTJ7Vjxw7NmzdPtWvXtmmRV65c0dy5c9W+fXs1a9ZMzz33nH766SdL/759+9SrVy81bdpU7du3V2xsrNXjzWazoqOj1bZtWzVp0kTh4eE6ceKETWsEAAAAAAAAAAC4mQLdU6ZDhw4yGAz52vPy8uTp6an58+cXurA/W7RokTZs2KDp06erWrVqWrp0qQYMGKDPPvtM7u7u6tu3rzp27KiJEyfq559/1sSJExUQEKAePXpIkmJiYrRmzRpNmzZNFStW1MyZMzVgwABt3rxZ7u7uNq0VAAAAAAAAAADgegoUykydOjVfKGMwGOTj46OwsDD5+PjYpLhrtm3bpkcffVRt2rSRJI0dO1br16/Xzz//rOPHj8vd3V0TJkyQq6urateurRMnTmjp0qXq0aOHcnJytHz5ckVERKhdu3aSpLlz56pt27baunWrunXrZtNaAQAAAAAAAAAArqdAocyTTz5p6zpuKiAgQF999ZV69eqlSpUqae3atXJ3d9fdd9+tDz/8UKGhoXJ1/WMpYWFhWrx4sS5evKjTp08rPT1dYWFhln4/Pz81aNBAO3bsIJQBAAAAAAAAAAB2UaBQRpIuXbqkuLg4xcfHKzU1VYGBgWrRooX69OmjoKAgW9aoyMhIjRw5Ug899JBcXFxkNBoVFRWlkJAQJSQkqG7dulbjK1SoIOnqvW8SEhIkSZUqVco35uzZs4Wqy9X15rfkcXEp0C17cBO2ek7ZG9uz5XPK/tgWe+PceF9zXjynAAAAAADA1goUyiQkJOjpp5/WpUuX1LRpUzVo0EDnz59XXFycNm3apA8//FAVK1a0WZFHjhyRn5+fFi5cqIoVK2r9+vUaM2aM3nvvPWVlZeW7L0yZMmUkSdnZ2crMzJSk645JSUkpcE1Go0GBgd4FfjwKxs/P09El4AbYG+fF3jg39sd5sTcAAAAAAMDWChTKzJw5U66urvrss89UrVo1S/vJkycVHh6uuXPnavr06TYp8PTp04qIiNCKFSvUokULSVKjRo10+PBhzZ8/Xx4eHsrJybF6THZ2tiTJy8tLHh4ekqScnBzLn6+N8fQs+IctZnOeUlMzbjrGxcXIBzo2lpqaqdxcc6HnYW9sz1Z7I7E/tsbeODfe15yXLV87AAAAAACgZPPz87ytq24UKJT57rvv9Nprr1kFMpJUrVo1DRkyRDNmzCjItNe1e/duXblyRY0aNbJqb9Kkib755htVrlxZiYmJVn3Xvq5YsaJMJpOlLSQkxGpM/fr1C1WbycQHNfaWm2vmeXdS7I3zYm+cG/vjvNgbAAAAAABgawW6WHpubq4CAwOv21e2bFmlpaUVqqg/u3YvmAMHDli1Hzx4UNWrV1doaKh27dql3NxcS9/27dtVs2ZNBQUFqX79+vLx8VF8fLylPzU1VXv37rWcvAEAAAAAAAAAAChqBQpl6tWrp48//vi6fZs2bVLdunULVdSfNW7cWC1atNCYMWP0448/6vjx45o3b562b9+uF198UT169FBaWpoiIyN1+PBhbdy4UStXrtTAgQMlXb2XTK9evTRr1ixt27ZN+/fv18iRIxUcHKxOnTrZrE4AAAAAAAAAAICbKdDlywYPHqx+/fopOTlZjz32mMqVK6cLFy7o008/1Q8//KDo6GibFWg0GhUTE6N58+Zp3LhxSklJUd26dbVixQo1bdpUkrRs2TJNmTJF3bt3V/ny5TV69Gh1797dMsfw4cNlMpk0fvx4ZWVlKTQ0VLGxsXJ3d7dZnQAAAAAAAAAAADdToFDm/vvv14wZMzRjxgx9//33lvby5ctr2rRpNj+B4u/vrzfffFNvvvnmdfsbN26stWvX3vDxLi4uioiIUEREhE3rAgAAAAAAAAAAuF0FCmUk6fTp06pXr55WrlyplJQU7d+/X1FRUUpOTrZheQAAAAAAAAAAACVDgUKZZcuWacGCBXr++edVu3ZtSVLlypX1+++/a/bs2fL09NTTTz9t00IBAABw+376aaeGDx90w/7w8BcVHv6ifv/9uObPn6vdu3+Wi4ur2rZtp6FDR8rX19cy9vDhQ1q0aL5++223jEYXtW7dRoMGDVO5cuXssRQAAAAAAEqMAoUy69at08iRI9W/f39LW3BwsMaOHauyZcvq3XffJZQBAABwoHr16uudd+LytS9dukj79/+mjh076/LlyxoxYrDKlSun11+fpEuXLikmJlqJiec0d+5CSVJi4jmNGDFI1apV1xtvTFZWVpaWLFmol18erBUr3pera4EPXgMAAAAAUOoU6Lfoc+fO6Z577rluX6NGjbRw4cJCFQUAAIDC8fb2UcOGjazavv32P9q16796663pCgmprlWr4nT5cqqWL1+twMBASVL58hUUETFCv/zys5o0aaqPP96ozMwszZgxV35+/pKkgIAADR8+SLt27VDLlq3svDIAAAAAAIovY0EeVK1aNf3www/X7YuPj1dwcHChigIAAIBtZWdnae7cmWrduo0efLCjJCk+frsaN25mCWQkqWXLVvLy8taPP34vSXrqqWcUE7PUEshIkpubmyTpypUcO64AAAAAAIDir0AnZZ599llNnTpVJpNJHTt2VFBQkC5duqR///vfevfdd/Xqq6/auk4AAAAUwtq17+vixQuKjn7H0nbixHE99FAnq3FGo1GVKlXWyZMnJEmBgYGW0CY7O1uHDh3QnDlvq2rVEIWGhtlvAQAAAAAAlAAFCmV69uyphIQExcXFacWKFZZ2FxcXvfDCC+rTp4+NygMAAEBhXblyRR9+uFYPPfSwqlatZmlPS7ssLy/vfOO9vLyUnp6er/2FF57RqVMn5e5eRpMnv60yZcoUad0AAAAAAJQ0Bb4z6yuvvKIXX3xRP//8s5KTk+Xn56fGjRtbXf4CAAAAjvfVV//WpUsX9eyzva3a8/LyZDAYrvOIPBkM+a9y+8orYyRJn322WWPHjlJk5AR17ty1KEoGAAAAAKBEKnAoI0m+vr5q27atrWoBAABAEfjqq22qWbOW7rqrrlW7j4/PdU/EZGRkqnz5ivnar12uLDQ0TOfPJyoubimhDAAAAAAAdyD/P4EEAABAiWEymbRjx4/q0KFTvr5q1arr9OmTVm1ms1lnz55RjRo1JUm7du3Q9u3f5XtsvXp3KzHxXNEUDQAAAABACUUoAwAAUIIdOXJYWVlZatSoSb6+++4L088//6SkpCRLW3z8dmVkpOu++66eivnss081efKbysj440SNyWTSrl07VLv2XUW/AAAAAAAAShBCGQAAgBLs6NHDkqQaNWrl6+ve/e8qU6aMRo4coq+//kqffrpJkya9rrCw1mrYsLEkqWfP55Wdna1XXx2h7777Rt9997VeeWW4jh8/qsGDh9t1LQAAAAAAFHeEMgAAACXYpUsXJV29F+BfBQQEKDr6Hfn7B2jSpPFasiRGDz74kCZNmmYZU6tWHS1cuEweHp6aOnWiJk4cL6PRoAULlqpZs3vttg4AAAAAAEoCV0cXAAAAgKLTs+cL6tnzhRv216pVR1FRMTedo169+pozZ76tSwMAAAAAoNQhlAEAALADo9Ego9Hg6DKKPbM5T2ZznqPLAAAAAACgQAhlAAAAipjRaFBAoKdcjC6OLqXYyzXnKjkpk2AGAAAAAFAsEcoAAAAUMaPRIBejixbveFtnL590dDnFViXfahoYOkZGo6FEhDJ79vyqxYsXaN++3+Tp6aWWLVtpyJARCgwsK0n65pv/aMWKZfr99+Py9w9Q166P6fnnw+Xm5iZJGjr0Rf388083nP+773baZR0AAAAAgNtHKAMAAGAnZy+f1ImUw44uA05g//59Gj58kFq0CNXUqbN04cJ5LV68QOPGndQ77yzX9u3fKTIyQl27PqaXXhqm338/rnfeWagLFy5ozJhISdIrr4xVenq61bxnzpzS5Mlv6m9/6+6IZQEAAAAAboFQBgAAALCzmJgo3XVXXU2bNlsuLlcva+ft7a2oqNk6c+a0Vq1aobvvvkfjxr0hSQoNbank5GS9++5yDR8+Sp6enqpZs5bVnCaTSfPmzVSdOndpxIhX7b4mAAAAAMCtEcoAAAAAdpSSkqz//W+XIiMnWAIZSWrXroPatesgSYqMnKDcXJPV49zc3GQ2m2UyWbdf8/HHG3Tw4H69885yyyXOAAAAAADOhVAGAAAAsKMjRw4rLy9PgYFlNXHieH333TeS8tS2bXu9/HKE/Pz8VKVKVcv4tLQ07dwZrw8+eE8PP/yIfH19882ZkZGh2Ngl6ty5qxo0aGjH1QAAAAAA7gShDAAAAGBHSUlJkqRp0yYpLKy1pk2bpVOnftc77yzU6dOntGhRrIxGoyTp/PlEde/eVZJUqVIVhYcPvO6cmzd/rLS0y+rdu699FgEAAAAAKBCjowsAAAAAShOT6YokqV69+ho79nW1aHGfnnjiKb366lj99tuv2rEj3jLWw8NTUVGLNGXKTPn7+6tfv946duxovjk3blyv++9/QCEh1e22DgAAAADAnSOUAQAAAOzIy8tLktS6dVur9pYtW0uSDh06YGnz9fXVvfeGql27BzVnzgJJeVq37n2rxx06dFCnTv2uhx9+pGgLBwAAAAAUGqEMAAAAYEdVq4ZIkq5cybFqN5lMkqQyZTy0bdsXOnhwv1W/n5+fKleuqnPnzlm1//DDt/Lw8FDr1m2KsGoAAAAAgC0QygAAAAB2VKNGTVWqVFnbtn1h1f79919Lkpo0aaqYmGgtWjTfqj8hIUEnThxTnTp3WbXv3btHdevWV5kyHkVbOAAAAACg0AhlAAAAADsyGAwaPHi49uz5VW+8MU47dvyoDz9co6ioOWrfvoPq1q2v8PAXtWNHvN5+e7J27IjX559v1ogRg+TvH6Bnn+1lNd/Ro0dUo0ZNB60GAAAAAHAnXB1dAAAAAFDaPPhgR02fXkYrVizVmDGj5Ovrpyee6KEBA16SJHXr9jd5enpp9eqV2rr1n/Lw8FDLlq01aNBQBQaWtZrr0qWL8vX1c8QyAAAAAAB3iFAGAAAAcID772+r++9ve8P+Dh06qkOHjrecZ9u2721ZFgAAAACgCHH5MgAAAAAAAAAAADvgpAwAAABKNaPRIKPR4OgySgSzOU9mc56jywAAAAAAp0UoAwAAgFLLaDQoINBLLkYOkNtCrtms5KQMghkAAAAAuAFCGQAAAJRaRqNBLkajZuzYpJOXLzi6nGKtmm85jQ59QkajgVAGAAAAAG6AUAYAAACl3snLF3QkJcHRZcBJZGVl6eGHH5DZbLZqd3d315df/iBJOn8+UTEx0YqP3y6TyaS7775HQ4YMV9269S3jFy2ar9WrV+abf+DAoerdu0+RrgEAAACAcyKUAQAAAIA/OXz4kMxmsyZMmKLg4MqW9mv3HsrISNeQIQPk5uamiIhxcncvoxUrlmnkyCFauXKtypUrJ0k6dOig7r33Pg0Y8JLV/MHBwfZbDAAAAACnUmxCmU2bNmnJkiU6efKkQkJCNHToUHXp0kWStG/fPk2ZMkV79uxRQECAevfurX79+lkeazabtWDBAq1fv16pqam699579eabb6p69eqOWg4AAAAAJ3X48AG5ubmpffuH5Oqa/1emtWvfV0pKslav3mAJYOrXv1v9+vXW//63U506PfL/8xzUE0/0UMOGjexaPwAAAADnVSzuaPrxxx/rtdde09NPP63Nmzera9euGjVqlP73v/8pKSlJffv2VY0aNbRhwwYNGzZMUVFR2rBhg+XxMTExWrNmjSZPnqy1a9fKYDBowIABysnJceCqAAAAADijQ4cOqkaNmtcNZCTpP//5Uu3bP2QJZCQpKKicNm363BLIXLp0UZcuXdRdd9W1S80AAAAAigenPymTl5enqKgovfDCC3rhhRckSUOGDNFPP/2k//73v/rvf/8rd3d3TZgwQa6urqpdu7ZOnDihpUuXqkePHsrJydHy5csVERGhdu3aSZLmzp2rtm3bauvWrerWrZsjlwcAAADAyRw6dFAGg1EvvzxYe/bslpubux588CENHfqy3N3L6Pjxo+rcuYuWLl2kzZs3KTk5WY0aNdHIkaNVu3YdSdLBgwckSd9++7XmzZulCxfOq1at2nrxxSFq1ep+Ry4PAAAAgAM5fShz9OhRnT59Wo899phVe2xsrCRpwIABCg0NtfpXbGFhYVq8eLEuXryo06dPKz09XWFhYZZ+Pz8/NWjQQDt27ChUKOPqevODRi4uxeIgUrFiq+eUvbE9Wz6n7I9tsTfOjfc158Vrx3mxN86tuD+nZrNZR48eltHooiFDhqtfvwHau3evYmOX6MSJY5o2bZZyc3O1bt37qly5ql577Q3l5ORo6dJ3NGzYQL333lpVqFBBR48ekiSlpCTptdfe0JUrOVq/fo3GjBmpOXOiFRbW2sErBQAAAOAITh/KHD9+XJKUkZGhfv36ae/evapatapeeukldejQQQkJCapb1/qSABUqVJAknTlzRgkJCZKkSpUq5Rtz9uzZAtdlNBoUGOhd4MejYPz8PB1dAm6AvXFe7I1zY3+cF3vjvNgb51bc9yc3N1dLlixRuXLlVLt2bUlShw4PKCSksiIiIvTrr7skSQaDQStWLJe399XfCcLCWujhhx/W5s0b9corr+ipp7qradNGatu2rYzGq0HVI4901OOPP67ly5eoS5dOjllgMZeZmanmzZvLbDZbtbu7u+vXX3/NN37lypWaOnWqtm3bpqpVq153zmnTpmnv3r1atWpVkdQMAAAA/JnThzJpaWmSpDFjxmjo0KF69dVX9a9//UuDBw9WXFycsrKy5O7ubvWYMmXKSJKys7OVmZkpSdcdk5KSUuC6zOY8paZm3HSMi4ux2P9S6mxSUzOVm2u+9cBbYG9sz1Z7I7E/tsbeODfe15wXrx3nxd44N1vuj6PUrdtQkpSUlG5pa9r0PknSoUNHJUnNmt2rnBwpJ+fqGE9Pf9WoUVO7d+9RUlK6vLwC1LhxC6WkZFrN3aLFfdq0aaPV3Lh9e/bsltls1ltvTbP6h3cGgzHfc/r7779r9uw5kqSUlEx5e+d/zt99d4VWrFihZs3uZU8AAABQKH5+nrd15QCnD2Xc3NwkSf369VP37t0lSXfffbf27t2ruLg4eXh4KCcnx+ox2dnZkiQvLy95eHhIknJycix/vjbG07Nwv4CbTMX7l83iKDfXzPPupNgb58XeODf2x3mxN86LvXFuxX1/zp9P1Pbt3yssrLUqVKhoaU9PvxquBAWVV2BgWWVnZ+db55UrJrm7l5HJZNYPP3ynK1dy1K5dB6sxmZlZ8vPzL9bPkSPt379fbm5uatv2QatLWEvWv5/l5uZq0qQ35O/vr8TErHz/vzxz5rTmz5+rH374Vj4+PsrLy2NPCikrK0sPP/zAdU8xffnlD5Kkb775j1asWKbffz8uf/8Ade36mJ5/Ptzye/9fzZ8/RwcO7NeCBUuKvH4AAAB7cfoLPgcHB0tSvkuU1alTR6dOnVJwcLASExOt+q59XbFiRcu/nrremGtzAwAAAIB09R9zzZgxRZ988pFV+5dffiGj0agmTZoqLKy1du78r5KTky39v/9+XCdPnlCTJk0lSdu2faGpUycqNTXVMiYzM1Pbt3+vZs3utcdSSqRDhw6qRo2a+QKZv/rgg1W6dOmievZ84br98+fP0enTJxUVtUh16tS97hjcmcOHD8lsNmvChCl65504y3/XApXt279TZGSE7rqrrqZNm63nnuutNWtWa86cGded7733Vmjt2vftuQQAAAC7cPqTMg0aNJC3t7d++eUXtWjRwtJ+8OBBhYSEqHnz5lqzZo1yc3Pl4uIiSdq+fbtq1qypoKAg+fr6ysfHR/Hx8QoJCZEkpaamau/everVq5dD1gQAAADAOVWpUlWdO3fV6tUr5ebmpnvuaaTdu3/WqlVx6t79KYWE1FCfPv317bf/0ahRQ9SnzwCZTCYtWbJQFSpU1GOPPSFJ6tnzef3nP9sUETFCvXv3UW5urt57b6UyMzPUr99ARy6xWDt06KAMBqNefnmw9uzZLTc3dz344EMaOvRleXldvb/P0aNHtHz5Us2eHa2zZ89cd54BA15SzZq1ZTAY7Fl+iXb48AG5ubmpffuHrhuarVq1QnfffY/GjXtDkhQa2lLJycl6993lGj58lOVKFn89xQQAAFDSOH0o4+Hhof79+2vhwoWqWLGiGjdurC1btuj777/XihUrVKdOHS1btkyRkZHq37+/du/erZUrV2rixImSrh6V7tWrl2bNmqWyZcuqSpUqmjlzpoKDg9WpEzfXBAAAAGBt9OhIVasWos8/36KVK2NVrlx5hYcP1HPP9ZZ0NbhZtGi5Fi2ar7feekMuLka1aNFSw4ePsgQDtWrV0cKFS7VkSYymTp2kK1euqGnTZho37nVVqXL9G87j5sxms44ePSyj0UUvvTRMffr01759exUXt1THjx/TggVLZDabNWXKBD322ONq1uzeG4YytWrVsXP1Jd+tTjFFRk5Qbq7Jqs3NzU1ms1km0x/tV08xnVJU1CItW/ZOkdYMAADgCE4fykjS4MGD5enpqblz5+rcuXOqXbu25s+fr5YtW0qSli1bpilTpqh79+4qX768Ro8ebbn/jCQNHz5cJpNJ48ePV1ZWlkJDQxUbGyt3d3dHLQkAAACAkypTpoz69OmvPn3633BMzZq1NGPG3JvOU79+A82Zs8DW5ZVaeXl5mjFjnoKCyql69RqSpKZNmysoKEiTJr2u+Pjt2rfvN12+nKpBg4Y5tthS6FanmP4cRqalpWnnznh98MF7evjhR+Tr62vp4xQTAAAo6YpFKCNJffv2Vd++fa/b17hxY61du/aGj3VxcVFERIQiIiKKqjwAAAAANmY0GmQ08sGsLZjNeTKb8xxdRqG4uLioefMW+dpbtWojSTp8+KBWrYrTzJlRcnNzk8lkstx03mw2W13yGrZ1O6eYjMart7Q9fz5R3bt3lSRVqlRF4eHWl/PjFBMAACjpik0oAwAAAKD0MBoNCgj0ksv/f5CLwsk1m5WclFGsg5nz5xO1ffv3CgtrrQoVKlras7OzJUkbN67XlStX9PLLg/M99umnn1DTps0tN52Hbd3OKaZWre6XJHl4eCoqapHS0tK0alWc+vXrrUWLYlWzZi0HrgAAAMB+CGUAAAAAOB2j0SAXo1Ez//uVTl5OdnQ5xVo13wBF3PegjEZDsQ5lcnJyNGPGFPXp01/9+w+ytH/55RcyGo164423LDeLv+b7779VXNxSTZ8+RyEhIfYuudS49SmmQ5ZQxtfXV/feGypJatbsXv3jH3/TunXva8yY8fYruBTKysrSww8/YDk9do27u7u+/PIHSdLvvx/X/PlztXv3z3JxcVXbtu00dOhIq8vL/dn8+XN04MB+wk4AAO4QoQwAAAAAp3XycrKOJF90dBlwAlWqVFXnzl21evVKubm56Z57Gmn37p+1alWcund/Ss2a3ZvvMUePHpEk1a5dR5UqVbZ3yaXGrU4xBQQEaNu2L1StWojq1q1v6ffz81PlylV17tw5u9dc2hw+fEhms1kTJkxRcPAfr4Vrl4i8fPmyRowYrHLlyun11yfp0qVLiomJVmLiOc2duzDffO+9t0Jr176vpk2b220NAACUFIQyAAAAAIBiYfToSFWrFqLPP9+ilStjVa5ceYWHD9Rzz/V2dGml2q1OMTVp0lQjRw5VSEh1qw/4ExISdOLEMbVocZ8jyi5VDh8+IDc3N7Vv/5BcXfN/FLRp04e6fDlVy5evVmBgoCSpfPkKiogYoV9++VlNmjSVJJ05c1rz58/VDz98Kx8fH3suAQCAEoNQBgAAAABQLJQpU0Z9+vRXnz79b2t8166PqWvXx246hksvFd6tTjGFhNRQePiLmjZtkt5+e7I6dOikCxfOa8WKZfL3D9Czz/Zy9BJKvEOHDqpGjZrXDWQkKT5+uxo3bmYJZCSpZctW8vLy1o8/fm8JZebPn6PTp08pKmqRli17xx6lAwBQ4hDKAAAAAADumNFosFz6CAVnNucV63v9XHOrU0zduv1Nnp5eWr16pbZu/ac8PDzUsmVrDRo0VIGBZR1cfcl36NBBGQxGvfzyYO3Zs1tubu568MGHNHToy/Ly8taJE8f10EOdrB5jNBpVqVJlnTx5wtI2YMBLqlmztgwGXvsAABQUoQwAAAAA4I4YjQYFBHrJxWh0dCnFXq7ZrOSkjGIfzNzOKaYOHTqqQ4eOtz0np5hsw2w26+jRwzIaXfTSS8PUp09/7du3V3FxS3X8+DEtWLBEaWmX5eXlne+xXl5eSk9Pt3xdq1Yde5YOAECJRCgDAAAAALgjRqNBLkajZsXv0KnLlx1dTrFV1ddXr7YMldFosGkowykm2ykJJ5ny8vI0Y8Y8BQWVU/XqNSRJTZs2V1BQkCZNel3x8duVl5d3g9MveTIYCF8BALAlQhkAAAAAQIGcunxZR5KTHV0G/oRTTLZVEk4yubi4qHnzFvnaW7VqI0k6fPiQfHx8rE7EXJORkany5SsWeY0AAJQmhDIAAAAAAJQQ104xzf3vAZ26nOHocoq1qr5eGnlfPZufZLK38+cTtX379woLa60KFf4IWLKzsyVJAQEBqlatuk6fPmn1OLPZrLNnz6hduwftWi8AACUdoQwAAAAAACXMqcsZOpqc/+QDSp+cnBzNmDFFffr0V//+gyztX375hYxGo5o0aaoLF87r/fffVVJSkgIDAyVJ8fHblZGRrvvuC3NU6QAAlEiEMgAAAAAAACVUlSpV1blzV61evVJubm66555G2r37Z61aFafu3Z9SSEgNde/+d23YsFYjRw5R374DlJqaopiYaIWFtVbDho0dvQQAAEoUQhkAAAAAAIASbPToSFWrFqLPP9+ilStjVa5ceYWHD9Rzz/WWdPUSZtHR7ygqao4mTRovLy9vPfjgQxo69GXHFg4AQAlEKAMAAAAAAFCClSlTRn369FefPv1vOKZWrTqKioq57TkXLFhii9IAACh1CGUAAAAAAADswGg0yGg0OLqMEsFszpPZnOfoMgAAuGOEMgAAAAAAAEXMaDQoINBbLoQyNpFrzlNyUjrBDACg2CGUAQAAAAAAKGJGo0EuRoPe3XFRCZevOLqcYi3Y103PhwbJaDQQygAAih1CGQAAAAAAADtJuHxFp1IIZQAAKK2Mji4AAAAAAAAAAACgNOCkDAAAAAAAAOBgr70WoYMH9+vDDz+1tP30004tX75Ehw8fkru7uxo2bKzBg4eratVqljGHDx/SokXz9dtvu2U0uqh16zYaNGiYypUr54hlAABugZMyAAAAAAAAgAP961+f6ZtvvrJq27Nnt0aOHCJ/f3+9+eZbGjkyQmfOnNbgwf2VnJwsSUpMPKcRIwYpPT1Nb7wxWa++Ok579uzWyy8PlslkcsBKAAC3QigDAAAAAAAAOMiFC+c1b94sVahQ0ap91ao41ahRU2+99bZatWqjBx/sqFmzopWSkqzPPrt6mubjjzcqMzNLM2bMVevWbdShQ0eNHh2p48ePateuHY5YTon12msReuqpx67bZzKZNGDAC4qNXXzTOfbv36d27Vpa9g9A6UQoAwAAAAAAADjI9Olv6b77Wuree0Ot2u+++x79/e/Pymj84+O7cuXKycvLW2fOnJIkPfXUM4qJWSo/P3/LGDc3N0nSlSs5dqi+dLjeSaZrsrOz9Oab47Rv3283nSMnJ0dTpryp3NzcoigRQDHCPWUAAAAAAAAAB/j00006cGC/Vq1ap4UL51n19enTP9/4n37aqcuXU1WzZm1JUmBgoAIDAyVJ2dnZOnTogObMeVtVq4YoNDSsyOsvDW50kkmSfvnlf5oz522dP3/+lvMsW7ZI6enpRVEigGKGkzIAAAAAAACAnSUknNX8+XP1yitjFBAQcMvxSUlJevvtyapQoaK6dHk0X/8LLzyjQYPCdeLECQ0fPkplypQpgqpLnxudZJKkMWNGqWLFSlq+/L2bzrFnz259+OE6jRo1uqjKBFCMcFIGAAAAAAAAsKO8vDxNmzZJrVq1Vvv2D91y/IUL5zVq1FAlJSUpKipGXl5e+ca88soYSdJnn23W2LGjFBk5QZ07d7V57aXJzU4ySdLChUtVu3adm86RnZ2lyZMn6Pnn+6p27buKqFIAxQmhDAAAAAAAAGBHGzeu05Ejh7Ry5RqZTCZJV4Ma6epN441Go+VeMkeOHFZExAhlZmZq9uxo3X33Pded89rlykJDw3T+fKLi4pYSyhTCtZNMr732xg1PMt0qkJGkRYvmy8vLU7169dH584k2rhJAcUQoAwAAAAAAANjRV19tU3Jysh5//JF8fe3bh6lv3wHq12+gdu3aoXHjXpWPj48WLlyiWrWsQ4Bdu3YoJydbrVq1sWqvV+9u7d27p0jXUJLd6UmmG/npp5365JOPtGTJSrm68jEsgKt4NwAAAAAAAADsaPTo15SRkWHVtnz5Uh04sE9vvz1H5cqV18GD+zVmzEhVqlRZc+YsUPnyFfLN89lnn+rHH7/X+vWfyMvLW9LVkza7du3gUlmFcCcnmW4kIyND06ZNUs+eL6hGjZoymUwym82SJLPZLJPJRFADlFK88gEAAAAAAAA7Cgmpka/N399fbm5uql+/gSRp9OiXZTKZFB7+os6dO6dz585ZxgYGBqpKlarq2fN5ff31l3r11RF67rnnJeVp/fq1On78qObOXWin1ZQ8t3uS6Wb279+rs2fPKC5uqeLillr1TZ/+lqZPf0vffbfTpnUDKB4IZQAAAAAAAAAncvr0KR08eECS9PrrY/P1d+nyqCIjJ6hWrTpauHCZFi9eqKlTJ+rKlRw1bNhYCxYsVcOGjexddolxOyeZbqV+/bu1bNm7Vm0XLlzQ2LGj1LfvAN1/f1ub1gyg+CCUAQAAAAAAABwsMnKC5c9VqlS97VMU9erV15w584uoqtLpdk4y3YqXl3e+sWfPnpEkVapU+bbnAVDyEMoAAAAAAACg1DMaDTIaDY4uo9gzm/NkNuc5ugwAcFqEMgAAAAAAACjVjEaDAgO9CWVswGzOU1JSeokLZv58kul6budkU6VKlbmPDIDiFcocO3ZMTz75pF5//XU9+eSTkqR9+/ZpypQp2rNnjwICAtS7d2/169fP8hiz2awFCxZo/fr1Sk1N1b333qs333xT1atXd9QyAAAAAAAA4ESunZLZEX9Zly+bHF1OseXr66rQlr4yGg02DWU4xWQ7nGQCHK/YhDJXrlzRq6++anWTraSkJPXt21cdO3bUxIkT9fPPP2vixIkKCAhQjx49JEkxMTFas2aNpk2bpooVK2rmzJkaMGCANm/eLHd3d0ctBwAAAAAAAE7m8mWTkpNzHV0G/oRTTLZVUk8yAcVJsQll5s+fL29vb6u2devWyd3dXRMmTJCrq6tq166tEydOaOnSperRo4dycnK0fPlyRUREqF27dpKkuXPnqm3bttq6dau6devmiKUAAAAAAAAAuA3XTskc/ypFWQRmheIR4KIaD/rb/CSTI+Tm5ur991dp8+ZNOn/+vKpVC9Fzz/VW585drzt+3boPFB09W+vXf6JKlSpb2o8fP6aYmGj973+75OrqqmbN7tWgQUMUElLDTitBaVQsQpkdO3Zo7dq12rRpk9q3b29p37lzp0JDQ+Xq+scywsLCtHjxYl28eFGnT59Wenq6wsLCLP1+fn5q0KCBduzYQSgDAAAAAAAAFANZybnKvMil5XDV4sULtW7d++rff5Dq12+g7du/11tvvSGDwaiHH37EauzJk79r8eIF+eY4c+a0Xnqpn3x8fDVq1GgFBpbVli2faODAcMXGrlLlylXstRyUMk4fyqSmpmr06NEaP368KlWqZNWXkJCgunXrWrVVqFBBknTmzBklJCRIUr7HVahQQWfPni10ba6uxpv2u7jcvB93zlbPKXtje7Z8Ttkf22JvnBvva86L147zYm+cG+9rzovXjvNib5wb72vOi9eO82JvnFtxf04zMjK0YcM6PfNMT/XpEy7p6j/UP3hwvzZuXKuuXf84LZObm6upUyfI3z9AiYnn5OJitHym++GHa5SdnaUVK95TlSpVJUn333+/+vd/QcuWvaNJk6bYf3EoFZw+lJkwYYKaNm2qxx57LF9fVlZWvvvClClTRpKUnZ2tzMxMSbrumJSUlELVde16lrAvPz9PR5eAG2BvnBd749zYH+fF3jgv9sa5sT/Oi71xXuyNc2N/nBd747zYG+dW3PfH17eM1q1bq3Llyll9Puvl5aGkpEyrtiVLlig5OUmDBg3UpEmT5O/vaek/ffp33XXXXWrYsJ7V/C1b3qf169fz2S+KjFOHMps2bdLOnTv16aefXrffw8NDOTk5Vm3Z2dmSJC8vL3l4eEiScnJyLH++NsbTs3BvPmZznlJTM246xsXFWOzf5JxNamqmcnPNhZ6HvbE9W+2NxP7YGnvj3Hhfc168dpwXe+PceF9zXrx2nBd749x4X3NevHacF3vj3Gy5P45SsWI1SdKlS2m6dOmiNm/+RD/88IPGjXtdSUnpkqSjR49owYIFmjt3gc6cOS1JSknJlLf31X5vbz/t339A588ny9XVzTL3kSPHdPnyZR0/fkb+/v52XhmKMz8/z9s6iebUocyGDRt08eJFq/vISNKbb76p2NhYVa5cWYmJiVZ9176uWLGiTCaTpS0kJMRqTP369Qtdn8lUvN+8iqPcXDPPu5Nib5wXe+Pc2B/nxd44L/bGubE/zou9cV7sjXNjf5wXe+O82BvnVpL254svPtekSa9Lklq1ul/t23eUyWSWyWTSxIlv6NFHH1fjxs106tQpSdZr79Klm7744nO9+ebrevHFwfLx8dG//vWZfvzxB0lSWlq6vL19HbMwlGhOHcrMmjVLWVlZVm0PP/ywhg8frq5du2rLli1as2aNcnNz5eLiIknavn27atasqaCgIPn6+srHx0fx8fGWUCY1NVV79+5Vr1697L4eAAAAAAAAAIBtNGjQUAsWLNHvv59QbOw7eumlcC1ZslKrV6/U5cupGjRo2A0fGxoapjfeeEvR0bP19NNfSJLuvfc+9e7dV8uWvVPoKy0BN+LUoUzFihWv2x4UFKQqVaqoR48eWrZsmSIjI9W/f3/t3r1bK1eu1MSJEyVdvZdMr169NGvWLJUtW1ZVqlTRzJkzFRwcrE6dOtlzKQAAAAAAAAAAG6patZqqVq2mpk2bq0qVqhox4iWtWfOeVq2K08yZUXJzc5PJZJLZfPV0jNlstvoH/g8/3EUdO3bW6dOn5OHhofLlKyg2drGMRqO8vX0cuTSUYE4dytxKUFCQli1bpilTpqh79+4qX768Ro8ere7du1vGDB8+XCaTSePHj1dWVpZCQ0MVGxsrd3d3B1YOAAAAAAAAALhTSUmX9OOPPygsrLUCA8ta2u++u4EkaeXKWF25ckUvvzw432OffvoJNW3aXAsWLNHx48e0b99v6tLlUVWr9setLw4c2Kc6de6yBDeArRW7UObAgQNWXzdu3Fhr16694XgXFxdFREQoIiKiqEsDAAAAAAAAABShjIwMTZkyQS++OFjPPx9uaf/xx+2SpLFjX1dISHWrx3z//beKi1uq6dPnWG5zcezYEU2ZMkENGjRU9eo1/r/tqOLjt6tv3wH2WQxKpWIXygAAAAAAAAAASqcqVarqkUe6acWKZTIajbr77nu0f/9erVy5XPfd10qdOj0ig8Fg9ZijR49IkmrXrqNKlSpLklq1aqMqVapq4sRI9e//kjIy0hUTE63KlavoH/941u7rQulBKAMAAAAAAAAAKDZGj45UtWoh2rLlEy1fvkRBQeX0978/oxde6JcvkLkRDw8PzZ49X9HRszVp0ni5u5dRWFhrDRw4RF5e3kW8ApRmhDIAAAAAAAAAgGLD3d1dL7zQTy+80O+2xnft+pi6dn0sX3vVqtU0Y8Y8G1cH3ByhDAAAAAAAAADgjhmNBhmNt3cyBTdnNufJbM5zdBmwA0IZAAAAAAAAAMAdMRoNKhvoLQOhjE3kmfN0KSmdYKYUIJQBAAAAAAAAANwRo9Egg9Gg5C0JMl284uhyijXXIDcFdAuW0Wgo9qFMXl6ePvnkI23cuE5nzpxWQEBZtWnzgPr3Hyhvbx9J0i+//E+LFy/U4cOH5OPjowceeFAvvviS1b18Dh8+pEWL5uu333bLaHRR69ZtNGjQMJUrV85RS7MZQhkAAAAAAAAAQIGYLl6RKTHb0WXASbz//rtasiRGzz7bW/feG6rTp09q2bJ3dPToEc2bt1DHjh3RyJFD1KhRU02aNE3nzycqJiZaZ86c1owZcyVJiYnnNGLEIFWrVl1vvDFZWVlZWrJkoV5+ebBWrHhfrq7FO9Yo3tUDAAAAAAAAAACHM5vNWrVqhf72tyc1aNBQSVJoaEv5+QXojTfG6sCBffr6668kGTRt2ix5eXlJknJzTZo1a7oSEs4qOLiSPv54ozIzszRjxlz5+flLkgICAjR8+CDt2rVDLVu2ctQSbYJQBgAAAAAAAAAAFEp6ero6d+6ihx562Kq9WrUQSdLp06eUk5MjV1dXeXh4WPr9/QMkSSkpKQoOrqSnnnpGbdu2swQykuTm5iZJunIlp4hXUfSMji4AAAAAAAAAAAAUb76+vho5crQaN25q1f7NN19JkmrWrK1HH31cBoM0f/5cpaQk6+jRI4qLW6rateuoTp27JEmBgYGqX7+BJCk7O1t79uzWnDlvq2rVEIWGhtl1TUWBkzIAAAAAAAAAAMDmfv31F61evVJt27ZXrVq1JUmDBg3T3LkztH79B5Kk4OBKWrhwqVxcXPI9/oUXntGpUyfl7l5Gkye/rTJlyti1/qLASRkAAAAAAAAAAGBTv/zyP0VEjFDlylU1btzrkqRVq+I0e/Z0PfFED0VFLdLEiVPl6empESMG69Kli/nmeOWVMZo7d4HatXtQY8eO0r/+9Zm9l2FznJQBAAAAAAAAAAA28+9//0tTp05UtWrVNWfOfPn5+ctkMmnlylg9/HAXjRo1xjK2WbN79fTTT+j991dp6NCXrea5drmy0NAwnT+fqLi4percuas9l2JznJQBAAAAAAAAAAA28f7772rixPFq0KChFi5cqqCgcpKk5ORkZWVlqVGjJlbjy5YNUkhIDR07dlSStGvXDm3f/l2+eevVu1uJieeKfgFFjFAGAAAAAAAAAAAU2qZNGxQTE60HH+youXMXysfHx9IXGBgoPz9//fLL/6wek5ycrJMnf1elSpUlSZ999qkmT35TGRnpljEmk0m7du1Q7dp32WchRYjLlwEAAAAAAAAAgEK5ePGC5s+fo+DgSurR42kdOLDfqr9Klarq1+9FzZ07U97e3nrwwY5KSUnWqlUrZDQa9eyzvSRJPXs+r6+//lKvvjpCzz33vKQ8rV+/VsePH9XcuQsdsDLbIpQBAAAAAAAAAACFsn3798rOzlZCwlkNGdI/X/9rr72pHj2elo+Pr9aseU+fffap/P0D1KRJU02bNstyUqZWrTpauHCZFi9eqKlTJ+rKlRw1bNhYCxYsVcOGjey9LJsjlAEAAAAAAAAAAIXy6KOP69FHH7/luM6du6pz5643HVOvXn3NmTPfVqU5Fe4pAwAAAAAAAAAAYAeclAEAAAAAAAAAoIQxGg0yGg2OLqPYM5vzZDbn2Ww+QhkAAAAAAAAAAEoQo9GgsoFeMhi5WFZh5ZnNupSUYbNghlAGAAAAAAAAAIASxGg0yGA0KuVfe2W6lOHocoot17Je8u/cQEajgVAGAAAAAAAAAADcmOlShkzn0xxdBv6Es0sAAAAAAAAAAAB2QCgDAAAAAAAAAABgB4QyAAAAAAAAAAAAdkAoAwAAAAAAAAAAYAeEMgAAAAAAAAAAAHZAKAMAAAAAAAAAAGAHhDIAAAAAAAAAAAB2QCgDAAAAAAAAAABgB4QyAAAAAAAAAAAAdkAoAwAAAAAAAAAAYAeEMgAAAAAAAAAAAHbg9KFMcnKy3njjDT3wwANq3ry5nn32We3cudPSv2/fPvXq1UtNmzZV+/btFRsba/V4s9ms6OhotW3bVk2aNFF4eLhOnDhh72UAAAAAAAAAAIBSzulDmVGjRumXX37RnDlz9OGHH+qee+5Rv379dOTIESUlJalv376qUaOGNmzYoGHDhikqKkobNmywPD4mJkZr1qzR5MmTtXbtWhkMBg0YMEA5OTkOXBUAAAAAAAAAAChtXB1dwM2cOHFC33//vT744AM1b95ckhQZGalvvvlGmzdvloeHh9zd3TVhwgS5urqqdu3aOnHihJYuXaoePXooJydHy5cvV0REhNq1aydJmjt3rtq2bautW7eqW7dujlweAAAAAAAAAAAoRZz6pExgYKCWLFmihg0bWtoMBoPy8vKUkpKinTt3KjQ0VK6uf2RLYWFhOnbsmC5evKj9+/crPT1dYWFhln4/Pz81aNBAO3bssOtaAAAAAAAAAABA6ebUJ2X8/PwsJ1yu+fzzz/X777+rTZs2mjt3rurWrWvVX6FCBUnSmTNnlJCQIEmqVKlSvjFnz54tdH2urjfPtFxcnDrzKpZs9ZyyN7Zny+eU/bEt9sa58b7mvHjtOC/2xrnxvua8eO04L/bGufG+5rx47Tgv9sa58b7mvHjtOC9bPp9OHcr81a5du/Taa6/poYceUocOHTRt2jS5u7tbjSlTpowkKTs7W5mZmZJ03TEpKSmFqsVoNCgw0LtQc+DO+fl5OroE3AB747zYG+fG/jgv9sZ5sTfOjf1xXuyN82JvnBv747zYG+fF3jg39sd5sTfOy5Z7U2xCmX//+9969dVX1aRJE82ZM0eS5OHhoZycHKtx2dnZkiQvLy95eHhIknJycix/vjbG07NwT6LZnKfU1IybjnFxMfJCsrHU1Ezl5poLPQ97Y3u22huJ/bE19sa58b7mvHjtOC/2xrnxvua8eO04L/bGufG+5rx47Tgv9sa58b7mvHjtOK/b2Rs/P8/bOlFTLEKZ9957T1OmTFGnTp00a9Ysy8mX4OBgJSYmWo299nXFihVlMpksbSEhIVZj6tevX+i6TCbbvEBw+3JzzTzvToq9cV7sjXNjf5wXe+O82Bvnxv44L/bGebE3zo39cV7sjfNib5wb++O82BvnZcu9cfoLy73//vt666231LNnT82bN8/qUmShoaHatWuXcnNzLW3bt29XzZo1FRQUpPr168vHx0fx8fGW/tTUVO3du1ctWrSw6zoAAAAAAAAAAEDp5tShzLFjxzR16lR16tRJAwcO1MWLF3X+/HmdP39ely9fVo8ePZSWlqbIyEgdPnxYGzdu1MqVKzVw4EBJV+8l06tXL82aNUvbtm3T/v37NXLkSAUHB6tTp04OXh0AAAAAAAAAAChNnPryZf/617905coVbd26VVu3brXq6969u6ZPn65ly5ZpypQp6t69u8qXL6/Ro0ere/fulnHDhw+XyWTS+PHjlZWVpdDQUMXGxlqduAEAAAAAAAAAAChqTh3KDBo0SIMGDbrpmMaNG2vt2rU37HdxcVFERIQiIiJsXR4AAAAAAAAAAMBtc+rLlwEAAAAAAAAAAJQUhDIAAAAAAAAAAAB2QCgDAAAAAAAAAABgB4QyAAAAAAAAAAAAdkAoAwAAAAAAAAAAYAeEMgAAAAAAAAAAAHZAKAMAAAAAAAAAAGAHhDIAAAAAAAAAAAB2QCgDAAAAAAAAAABgB4QyAAAAAAAAAAAAdkAoAwAAAAAAAAAAYAeEMgAAAAAAAAAAAHZAKAMAAAAAAAAAAGAHhDIAAAAAAAAAAAB2QCgDAAAAAAAAAABgB4QyAAAAAAAAAAAAdkAoAwAAAAAAAAAAYAeEMgAAAAAAAAAAAHZAKAMAAAAAAAAAAGAHhDIAAAAAAAAAAAB2QCgDAAAAAAAAAABgB4QyAAAAAAAAAAAAdkAoAwAAAAAAAAAAYAeEMgAAAAAAAAAAAHZAKAMAAAAAAAAAAGAHhDIAAAAAAAAAAAB2QCgDAAAAAAAAAABgB4QyAAAAAAAAAAAAdkAoAwAAAAAAAAAAYAeEMgAAAAAAAAAAAHZAKAMAAAAAAAAAAGAHhDIAAAAAAAAAAAB2QCgDAAAAAAAAAABgB4QyAAAAAAAAAAAAdlBqQhmz2azo6Gi1bdtWTZo0UXh4uE6cOOHosgAAAAAAAAAAQClRakKZmJgYrVmzRpMnT9batWtlMBg0YMAA5eTkOLo0AAAAAAAAAABQCpSKUCYnJ0fLly/XsGHD1K5dO9WvX19z587VuXPntHXrVkeXBwAAAAAAAAAASoFSEcrs379f6enpCgsLs7T5+fmpQYMG2rFjhwMrAwAAAAAAAAAApYUhLy8vz9FFFLUvvvhCw4YN0y+//CIPDw9L+4gRI5SVlaXFixff8Zx5eXkym2/+1BkMktFolDkjS3lm8x1/D/zBYDTK6OUhs9ksW/w/9tre5GZclsy5hZ+wNDO6yMXL12Z7I/2xP1cyk2U2m2wzaSlkNLrKzTOgSPYmi70pNKPRVR423J9re5ORxd4UltHoKi+PonntpGYlKzeP/SkoF4Or/Ipob5Kz0mXK42eCwnA1uCjAw9vm72vJWZky5fGzdGG4GowK8PAsotdOlky3+L0IN+ZqNCjAw3a/50h/7E1KVo5MJf/X/SLlajDI38Pd5u9rl7NylcveFIqLwSBfD5ei+V0ny6w83tcKzGA0yMPDWESfEbA3hWUwGuTmabv9+ePzNZPEj9KF4yK5eLkWyWvHnJHDa6cQDEaDjF639/OA0WiQwWC45ZyuNqrNqWVmZkqS3N3drdrLlCmjlJSUAs1pMBjk4nLrJ1iSjF4etx6E22I02vZwl4uXr03nK81svTeS5OYZYPM5S6Oi2BsP9sZmbL0/Xh4BNp2vNCuK144f+2MTRbE3AR7eNp+ztLL1/gR4eNp0vtKsaF47/K5jC0WxN/4e7rcehNti6/3x9XCx6XylWZH8ruNRKi4qU+SK5jMC9sZWbP/5Wqn4iNkuiuK1Y/TiZwJbsOXelIp3s2unY3Jycqzas7Oz5enJL3kAAAAAAAAAAKDolYpQplKlSpKkxMREq/bExEQFBwc7oiQAAAAAAAAAAFDKlIpQpn79+vLx8VF8fLylLTU1VXv37lWLFi0cWBkAAAAAAAAAACgtSsUF/9zd3dWrVy/NmjVLZcuWVZUqVTRz5kwFBwerU6dOji4PAAAAAAAAAACUAqUilJGk4cOHy2Qyafz48crKylJoaKhiY2Pl7s6NjgAAAAAAAAAAQNEz5OXl5Tm6CAAAAAAAAAAAgJKuVNxTBgAAAAAAAAAAwNEIZQAAAAAAAAAAAOyAUAYAAAAAAAAAAMAOCGUAAAAAAAAAAADsgFAGAAAAAAAAAADADghlAAAAAAAAAAAA7IBQBgAAAAAAAAAAwA4IZZzM2LFj1bt3b0nS/PnzVa9evZv+d+rUqevOc+zYMY0aNUqtWrVSw4YN1aFDB02cOFEXLlywab1fffWVDh8+bNM5Ha13794aO3bsdfv+vD+2UK9ePW3cuFGSlJeXp48++kgXL16UJG3cuFH16tWz2fcqrdLS0tSkSRO1bt1aOTk5t/24O33+2b/b06FDB6v3sIYNG6pz585atmyZTb/PzV7Hp06dUr169RQfH2/T71ka5eXlaePGjerdu7fCwsLUsGFDdezYUZMmTdK5c+ds9n3OnDmjLVu22Gw+Z2TPv3tuJj4+3urni6SkJK1fv77Aj//rukrizw03c+09Ly4u7rr9b7zxhurVq6f58+cX6ntce3xp+7unoH/HX3Po0CH95z//sX1hd+Cvr5nr+fPrqLjvsclk0sqVK/Xkk0+qWbNmatmypfr27avt27dbxvz552N7uZ19uBM3e08vbv78HgPn17t373yfGVz7PGDKlCnKysq67bnu9O9sZ3qvut7zcO2/KVOmFPn3vtnr/6+/D919991q0aKFevfurZ07dxZpbUXNHj+z3s7fI/awa9euIt+vjIwMrV692vK1PX8ncAZjx4695WeihfHn97i//hxQ2v7uK+zP1H9l689c5s+frw4dOli+LimfD7g6ugDcWHh4uJ555hnL10899ZS6du2q8PBwS1vZsmXzPe7ChQt69tln9cADD2jp0qUKDAzUsWPHNHPmTPXu3Vsff/yx3N3dC13f6dOnNWjQIL377ruqU6dOoecr7Xbs2KGxY8dq27Ztji6lRNmyZYuCgoJ04cIFbd26Vd26dbutx3Xt2lVt27a97e/z1/2708eXJuHh4Zb3saysLP3yyy8aP368PD091bNnzyL//pUqVdJ3330nf3//Iv9eJVlubq6GDBmin376SYMGDdIbb7whb29vHTp0SDExMerRo4c2bdqkcuXKFfp7jRkzRlWqVLnt1y8KrlmzZvruu+8sP1/MmDFDp06d0t///vcCzTd//ny5uLhIKr0/N7i5uemf//yn+vbta9VuMpn0xRdfyGAw2Ox7lba/ewr6d/w1AwcOVPfu3dW+ffuiKdBG/vw6Ks5ycnLUt29fnT17VsOGDVOzZs2UlZWlDRs2KDw8XNOmTdMTTzzhkNr++t4HFGddunRRZGSk5euMjAx99913mjZtmnJzc/XGG2/cco6C/J3tbO9Vf30ervH09HRANdb+/PtQXl6ekpOTNWfOHPXv31///Oc/FRwc7OAKCyYyMlK5ublFNr8z/T3y3HPPadq0aWrRokWRfY/ly5dr48aNlt+Ri/r5dTaRkZF65ZVXLF+3adNGr732mrp27Vrouf/6Hlfafw4o7M/Uf1XUn7mUlM8HCGWcmLe3t7y9vS1fu7i4yMvLS+XLl7/p4/75z3/KZDLp7bfftvyiX6VKFVWuXFldunTRt99+q4ceeqjQ9eXl5RV6DvyB57NobNiwQW3atNG5c+e0Zs2a237T9vDwkIeHx21/n7/u350+vjT56/tYtWrVFB8frw0bNtgllHFxcbnl+yhuLS4uTt9++63WrVune+65x9JeuXJl3XffferatauWL1+u0aNHO7BK3Cl3d3er10dh/24KCAiw2VzFVatWrfTtt9/q7NmzqlSpkqX9xx9/lJeXl00/HCptf/cU9O/44ubPr6PiLDo6Wvv379eWLVusPnCMjIxURkaGpk6dqk6dOjmktr++9wHFmYeHR77/P1evXl179uzRli1bbiuUKcjf2c72XnW958FZ/PX3oQoVKmjixIl64IEH9MUXX+j55593YHUF5+vrW6Tz3+7fI3/+HK04++vrsKifX2fj6+ubb82+vr42eV3/9bkt7T8H2Ppnaj5zuT1cvqwEMhgMSk9Pz3dMrFatWtqyZYvCwsIsbV999ZWefPJJNW7cWJ06ddK8efOsjqplZGRo8uTJatOmjZo1a6aePXtq9+7dOnXqlCXY+b/27jyupvx/4PjLVso2diORZcSICoOEJMuEsSRDkVG2vvaMrbEVSpuJauwZS7IWSvZswyB7Q5ZRiZixjDVLUX5/eHR+3dbLTIj38/HweOiee8/93LN89s/7DBgwQFnWFxsbi6OjI82bN6dJkyaMHj2aW7duKfuzs7Pjp59+onfv3jRt2pQtW7bk45HIX0+ePGHatGm0aNGCJk2aMGDAAP744w9l++vXr1m2bBmWlpYYGBjQpEkThg0bxo0bN7Ls6/jx40rFy8LCQiVkQ2hoKB06dKBhw4ZYWVlx7tw5ZdurV6+UZXyGhoZYWVlx6NAhZfu+ffvo27cvxsbGNGzYEGtra37//Xdle07nIyQkBEtLSxo1aoSlpSUrV64kLS3tPzt270tsbCznzp3D1NSUb7/9lqioKGJjY4E3S2Ezz/z++++/qV+/PkePHs2y5P7PP/9k+PDhNG/eHAMDAzp06MDKlSuB7M9f5s8/fPgQV1dXzMzMaNSoETY2NirLnf39/bGzs2Pp0qW0adOGhg0bMmDAAOLi4vLt+HxMMnZKPn78mBkzZmBmZkaDBg0wNTVlxowZSriF9KXFBw8epGvXrhgYGNClSxf279+f7b5TU1MZO3YsZmZmXLt2LctSWjs7Ozw9Pfnpp59o2rQpjRs3ZtKkSTx9+lTZx/nz5+nXrx+GhoZYWFgQFhbG119//dmGQHv9+jVr1qyhW7duKgMy6bS0tAgKCmLs2LEA/PXXX4wfPx5TU1OMjIwYNGgQly9fBv7/fKbfm+ns7e2ZMGECdnZ2REVFsXnzZpUly5+rvPKS58+fM2XKFExNTWnYsCE9evRg9+7dynY7Ozvc3d2ZOHEiRkZGtGnThiVLligNk4xL9ydPnszmzZuJiopS8rO87s/M0kN4ZFdv6NGjB87OzirvP3ToEAYGBty/f/8/PW4fUqNGjahatSo7d+5UeX379u1YWlqqrJQ5ffo0/fr1o1GjRrRt2xZXV1eSkpKU7U+ePGHSpEk0bdoUExMTVqxYobLPtym7CrrcynjIPnxMxrAf7dq14+bNmwQEBCivqVNWDxw4kFWrVtGqVSuMjIwYN24cd+/eZeLEiRgbG2NmZsbmzZuVz6SmprJixQo6depEw4YN6dSpExs2bMjye/bv30/Hjh1p1KgR9vb2KvXF3ELhpKSk4O3tTevWrTE2Nub777/n8OHD73BE89fLly/ZuHEj1tbW2c4AHzNmDMuWLVMGFePj47G3t6dRo0a0atWKxYsXq7w/rzaMvr4+27ZtY8CAAcp79u3bx759++jUqRNGRkYMHjxYyWsyhy35t3Xsz0VKSgpz586lffv2GBgY0Lx5c8aNG8eDBw9IS0vDxMREJXzjypUr0dfX5+zZs8prY8aMYcKECQwfPjxLR3RcXBz6+vpcunQJUO+8+/r6Ym5ujqmp6WdTj1aXpqYmhQu/6QLKLe/Iqa2vTtuyIIXtUyd/zqt/IyUlBXd3d0xMTGjatClz585953Zz0aJv5kynRzVp164d7u7udO7cmebNm3Ps2DG10nzjxg1GjBhBkyZNaN68OU5OTiph7PNq62/ZsoUuXbrQsGFDWrdujZubm3Kfpaam4u3tjZmZGQYGBnz77besXbtW+WzGcladNltqaiq+vr60atUKQ0NDRo0ahZubW7Yhut6mHPkv2t4HDx7EysoKQ0NDTExMmDx5Mo8ePQJQ6lrOzs5KPVdfX58FCxZgampKu3btePz4cbbhODOHxTp//jz29vYYGxvTsmVLpk+fzrNnz/D39ycgIICbN2+q1M0zHht1+t/yaucWZLmVCbt27UJfX59du3Yp758wYQLm5ubExMRkyePyCmOaVx29IMurTp3XfZp+7JYuXUrz5s3p2bMn169fzxK+bPXq1XTq1IlGjRrRuXNntm7dCuQc6iyncLbZ9Q9kF24uY5mUXRpTU1O5ffs2Tk5ONG3alObNm+Po6Mi1a9f+/UFVkwzKfIK6dOlC1apV+eGHH+jevTtz5sxh7969JCUlUadOHWXWwKFDhxgzZgy9e/dm27ZtzJgxgx07djBhwgRlX05OTuzfvx93d3e2bNlCzZo1GTRoEMWLF1dizPv7++Pg4MDNmzfp06cPGhoarFy5kl9//ZV//vmH/v37q2RWoaGhDBgwgLVr12JmZvZ+D85/5PXr1wwZMoRr166xePFiNmzYgJGRETY2NsTExABvGh2LFy9mwoQJ7Nq1iwULFhAfH4+Hh0eW/RkbGysZyMaNG1WWY65bt465c+cSEhKChoaG0skJ4O7uzpo1axg/fjzh4eGYmZkxfPhwrl69yvnz5xkxYgQdO3YkLCyMjRs3Ur58ecaPH6/SeMl8PtavX4+npycjRowgIiKCsWPHsnTpUnx8fPLpaOafTZs2oa2tTZs2bWjfvj0aGhpKpbFnz55ER0eTkJCgvD8sLIzKlSvTvHlzlf08f/4ce3t7tLW1CQ4OJiIiAktLS9zd3bl48WKu5w/eFGIODg6cPHkST09PNm/eTL169Rg4cKDKQN6ZM2c4ceIES5YsYcWKFdy6dQtXV9f8OjwfjejoaMLDw+nTpw/wZilqdHQ0fn5+7Nq1C2dnZ0JDQ1m/fr3K57y9vZkyZQqhoaHo6uoyfvz4LBXMtLQ0Jk6cyLlz5wgKCkJPTy/bNKxevZoKFSqwceNGZs+ezfbt25XOztu3b/PDDz+go6NDSEgI06dPx9fX97NaOp5ZYmIit27domXLljm+R0dHBw0NDZKSkrCxseH27dssXLiQdevWoa2tTf/+/bl16xbNmjWjWrVqhIWFKZ+9c+cOx44do2fPnvj7+2NsbIylpSWbNm16Hz/vo6VOXjJ//nwuX77MkiVL2L59O23atMHJyUmlgREcHIyWlhYhISE4OTnxyy+/sHTp0izfN2XKFCwtLZXl/KD+/ZnZl19+maXeYGVlxa5du1QGdLZu3Yq5ufknFzrA0tJSZVAmJSWFvXv3qsxCu3TpEgMHDsTU1JSwsDB8fHy4cOECDg4OyqDZ2LFjiY6OZtGiRSxfvpz9+/dz8+bNbL8zr7KroMutjFf381WqVMHBwQF/f3+1y+qTJ09y8uRJVq5cybx589i1axddu3alfv36hISE0KZNG6ZPn86DBw8A8PDwYMGCBYwcOZLw8HAGDBjAzJkzWb16tUp6AgMDmTZtGps2bUJTUxMbGxueP3+e5+9wdnbmt99+w9vbm82bN2NpaYmjo+MHf1ZOZjdu3ODhw4cYGRllu71SpUo0atRICX0UFBRE9+7diYiIwNbWlp9//ll5XoA6bRiA2bNn069fP7Zt20adOnX48ccfWbhwId7e3ixatIjo6Ohs8z74b+rYnwMvLy+2bduGm5sbu3btwtPTkyNHjrBw4UIKFy6MmZkZR44cUd5/9OhRChUqxLFjx4A3g19HjhzBwsKCXr16ERUVpdKhuGXLFho0aEC9evXUPu/r16/Hz8+PX375hVq1ar2fA/GRe/XqFQcOHGDr1q10794dyD3vyK7M/hSv+7zyZ3X6N9LbDR4eHqxdu5Zbt26903NGbt++zcyZM5VyLd3atWuZOnUqy5Yto3Hjxnmm+cmTJ9ja2vLs2TNWrFjBihUruHnzJqNGjQLIs61/6dIlpk6dyqhRo9i1axfu7u5s3bpVef5ncHAwO3fuxNfXl127dtG/f39cXFxy/c25tdl8fHxYv34906dPJzQ0lEqVKmUpH9OpW44A/7rtff/+fUaOHEmvXr3Yvn07AQEBnDhxAi8vLwClXvzTTz+phMgLCwtj5cqVzJ8/n9KlS+d4TNIlJiZiZ2dHuXLlWL9+PQEBARw/fpzp06crIe6qVKnC4cOHVVZbg3rXJ+Tezi3I8ioTOnXqRPfu3Zk1axaPHj0iIiKCiIgIvL290dfXz5LH5UadOnpBlledWt379MCBA6xfvx53d3dlAkC6wMBAfHx8GDRoENu2baNfv344Ozur1BHU9W/6BzKmMTk5GTs7O1JTUwkKCmL16tWULVuW77///j99Pm5uJHzZJ+iLL74gNDSUVatWsXv3bqUwLl68OEOHDmXEiBEALFq0CGtra2xsbACoXr06rq6u/PDDDyQmJvLy5UsOHDjAsmXLlPjk6c8MePz4sdJhUqZMGUqUKMGCBQvQ1tbGx8dHmd3h5+dHu3btCAsLw9bWFoD69evz3Xffve/D8lbCw8NVRtTTpaSk0LhxY44dO8aZM2c4evSochzGjRvH6dOnWbVqFR4eHlSvXh0PDw9l5FZHRwdLS8tsH0aloaGhxFosV66cSugRd3d3JY7voEGDGDlyJP/88w+ampps2LCBqVOnKoMAY8aMIS0tjadPn6KhocHUqVNVwkENGDAABwcH/vnnH6VQz3w+FixYwLBhw+jatSvwJrRUUlISrq6ujBkzBk1NzXc/sO/Rq1evCA8Px9zcXFmFYWZmxtatW/nxxx9p1qwZurq6hIeHM3LkSODNee/evXuWAuT58+cMGDAAW1tbSpYsCcDIkSNZvHgxly9fpn79+jmeP3hTabtw4QLh4eHUrVsXeHMvnTt3jsDAQObNm6ek2cvLS1n6b2dnh7e3d74cnw9p8eLFLF++HHgz4+nly5cYGhoq17GpqSlNmzalXr16AFSrVo2goCBlZUW6sWPHYmJiovy/e/fuXLlyBWNjY+DNgIyzszNnz54lKCgIHR2dHNNUu3Ztxo0bB0DNmjWJiIjg9OnTwJvGS+nSpXFzc6NYsWLUqVOHadOm8b///e8/PCoFS/psu8wd546OjiozXKpWrUq/fv148OABoaGhyvt9fHxo3749a9asYcKECfTs2ZPNmzfj5OQEwLZt26hcuTItWrSgcOHCFCtWjOLFi39yHfWZ5VX2qJOXXL9+nZIlS1K9enVKlSrFmDFjaNq0qUo831q1auHi4kKhQoWoXbs2sbGxrFq1iiFDhqh8b6lSpShevDjFihVTlp+re39mVqRIkSz1hm7duuHt7c3evXvp2rUrSUlJ7N27V8kTPyWWlpYEBgYqIcyOHDlC2bJl+frrr5X3BAYGYmJiwvDhwwHQ09NTZqBHRUVRsWJFDh8+zIoVK5T45XPnzsXc3Dzb71Sn7Cqo8irj1QkJV65cOSU08BdffMHBgwfVKqvT0tKYPXs2pUuXpnbt2tSvX59ixYopzwwaOHAgGzZsICEhgWLFirF27VomT56s1LX09PS4ceMGixYton///kp6pk6dqtS3vby8MDMzY9u2bbk+zykhIYFt27axadMmGjZsCLxZZXjp0iUCAwM/qmflpM8uVje2uI2NjfJcgOHDh7N8+XLOnz+PiYlJnm2YatWqAW8m4HTq1AmAvn37sm/fPpycnJROO1NTU65cuZLlu5OSkv6TOvbnoGHDhnTs2JFmzZoBb9o7rVq1UsoEc3NzJk2aREpKCoULFyYqKooOHTpw/PhxHB0dOXXqFMnJybRu3RpNTU0qVKhAeHg4w4YNIy0tjbCwMAYPHgzk3XZNP+/du3dX7ofPVeb6xIsXL6hatSqDBg3C0dFRrbwjc5ldpEiRAnfdZ1evMjY2Zvny5SQlJeWZPwcHB+fav9GtWzdCQ0OVFcTwpu2uzkr6jO2hV69ekZKSQu3atZk3bx5Vq1ZV3mdmZqZMglInzdu3b+fJkyf4+voqbUo3Nze2bt1KcnJynm39xMREChUqRLVq1ahatSpVq1YlMDBQqUdcv34dbW1tdHV1qVixIv3796dWrVrUrFkzx9+aU5utXr16BAcH4+zsTMeOHQGYNm0aZ86cyXY/6pYj/0Xb+/bt26SkpFC1alV0dHTQ0dFh0aJFyqS89Hpxenit9LTZ2tq+1XMTN2zYQJkyZfDw8KBYsWLAm4G+qKgoSpQogba2do5hoPK6PtP733Jr5xZk6pQJ06dPp1u3bkyZMoXjx48zfPhwpQ6dOY/LTV519MyTeguSvOrUgNr3qYODgzIJNvOKoxUrVjBgwAC+//57APr168eLFy/eaaLrF1988c79AxnTuHHjRh48eMDcuXOV+8/NzY3jx4+zYcMGZTA7P8mgzCeqTJkyjBo1ilGjRvHPP/9w7NgxZdZQ2bJlsbW1JSYmhujoaJUQC+mjvLGxscrsvIwzETQ0NJRQI5lvsitXrmBgYKAUCADly5enZs2aKp01NWrU+M9/73+tXbt2jB8/PsvrPj4+PHz4kAsXLgBkeTZPSkoKycnJyj7OnTuHn58fCQkJxMbG8ueff1K5cuW3SkvGCk76bIsXL15w69YtXr58mWWmSHqnJry5DpYuXUp8fDzXrl1TZsZmzPgyno/79+/z999/M3/+fAICApTX09LSSE5OJjExkdq1a79V+j+UgwcPcvfuXZVVK507d2bPnj1ERERgbW1Njx49lEGZixcvcuXKFfz8/LLsq1y5ctja2rJ9+3YuXbpEQkKCcizVWZ5+5coVSpUqpVQK4U2YwaZNm/Lbb78pr1WoUEElFnOpUqV4+fLlu/z8j1rfvn2Vpa6vXr3i2rVr+Pr6YmtrS0hICLa2tuzbt4+tW7dy/fp1rly5wo0bN7Kscsk4+zG9oZDxeO3YsYOXL19Sq1atPOOZZr6uS5UqxePHjwGIiYmhQYMGSkEN5OsDHQuCsmXLAm9C/WTk6uqqrHpYvXo1+/bt48qVK+jp6alUmDQ1NWnUqJFSNvTo0YOAgABOnz5N48aNlZmcmQdIP3V5lT3q5CVDhgzB0dERExMTjI2NMTU1pUuXLirxmJs1a6YSMsvIyIilS5cqM/tzo+79qY4vvviCdu3asWXLFrp27cqOHTsoVarUJ/mgegMDA3R1ddm5cyf29vZs375d6RBJFxMTQ0JCgjKwnFFsbKxyfjJ2NlaoUAFdXd1sv/Pfll0fM3XK+Lelblldvnx5ldmvWlpaKp2R6ZNXkpOTiYuL4+XLlzRp0kTlu5o2barMaM34WrrSpUujp6eX7YBBRumrszOHfHr58qVaM3Tfp/QyIHO5kZPMHXylS5dW6th5tWHSO+cz7iN9wkzG+0VTUzPbGf7x8fH/SR37c9C9e3eOHj3Kzz//zLVr14iNjSUuLk65nlu1akVqaiqnTp1CQ0OD4sWL07dvX4YPH05KSgoHDhzAxMRE6RDr1q0bW7duZdiwYRw7dox79+4peaW6570gtDXzW3p9Ii0tjXPnzjFnzhxatmyJo6MjRYsWfae8I30SWkG67rOrV6XnBerkz3n1b6TnFRnLZU1NTbUmPWRsDxUuXJgvvvgi2+eFZLye1Unz5cuX0dPTU2lTfvXVV4wfP16ttn56OLtevXqhp6dHy5YtsbCwwMDAAHjTkbp3717atGmDgYEBpqamWFpaUr58+Rx/a05tttjYWF68eJElr23SpIkSsjAjdcuR/6LtXb9+fbp27YqjoyNffvklLVu2pG3btnmGUn7b/Ofy5ctZ2pnffPMN33zzTZ6fVbf/Lbd2bkGmTplQsmRJ3NzcGDhwIA0aNHjnSZV51dEL8qBMXnXqevXqqX2f5tQevH//Pnfu3MHQ0FDl9UGDBgFZ+5bzU8Y0xsTEkJSUpEwsSZecnJwltHp+kUGZT9DSpUupVq0alpaWwJuMuUuXLnTu3Jk+ffpw8OBBbG1tSUtLY/DgwfTs2TPLPipWrKjEh83YcZOb169fZ/ve1NRUlUKmIDyAtkSJEtkWqCVKlODhw4ekpaVRsmTJbOMbpheKS5cuxd/fHysrK5o1a4adnR2RkZHZrpTJTXoYh4xev36tckyzc+LECRwcHDAzM6Np06Z06dKF58+fKyul0mU8H+mdNM7OztmGJfoYZ0DlJP3cjB49Osu2devWYW1tTc+ePQkICCA6OpodO3ZgbGyc7Syfe/fu8f3331O2bFksLCwwMTGhYcOGaoffy+neSEtLU2IHAyoVqk9ZmTJlVO6v2rVrU6ZMGfr168fvv/9OcHAwly9f5rvvvqNTp06MGzeOadOmZdlPdscr4/LhSpUq8fPPPzNo0CD8/Pyy7ezObV/pihQpUuA7MP9r6bPjoqKiVMIvZRx0Tp/FllvZkH79V6tWjWbNmhEeHk6pUqW4dOnSJ7laIi95lT3q5CXGxsYcPHiQI0eOcPToUTZt2oS/vz/Lli1TZilmzHfg/++b7MqbzO9zdHRU6/5UV69evXB0dOTevXvKrNPM6ftUpIcws7W1JTIyUgmbkC4tLY3vvvsOR0fHLJ8tV66csrw/c36U0/H6t2XXx0ydMh6yPsQ1t4kO6pbV2dW/chpATv/+zPtNP4cZ95v5/ktNTc2zXpC+/zVr1mSZ5fmxDWrr6upSoUIFzpw5kyXMK8C1a9eYOXMmkyZNAnKu/wJ5tmHSZXdvqNOu+a/q2J8DFxcXtm/fTo8ePWjbti3/+9//CAwMVMJ+lChRgmbNmnHkyBGKFy9OixYtaNq0Ka9fv+bcuXMcPHiQgQMHKvvr1asXgYGBnD9/nrCwMCwsLJROU3XPe0Foa+a3jPWJmjVrUqVKFezt7SlSpAguLi7vlHcUxOs+p3oVqJc/q9u/kZk69ZjM7aGcZLye1Ulz0aJFc8zn1Gnra2hosGrVKmJiYjh8+DCHDx9m3bp19OjRgzlz5qCnp8fu3buJioriyJEjREZGsmjRIubMmZPtvQk5t9nSj5O64Z/ULUcaN278n7S9586dy4gRIzh06BC///4748aNo3HjxqxatSrHz2SX/+RWF8ntfOVF3evzU+1jULdMuHDhAkWLFiU+Pp6bN29SvXr1d/qu3OroBVlederZs2cD6t2nOUXVSb8G87rWM37Hu0xOVqfenzGNaWlp1KxZk4ULF2Z5n7a29lt//7v4uGrr4j9x7tw5FixYwKtXr1ReL1SoECVKlFBmMXz11VfExcVRo0YN5d/t27fx8vLi6dOnyoh6xribr169om3btkRERGS5oerWrUt0dLTKjLN79+6RkJBQYFZXqKtu3bokJSWRkpKicvyWLl1KZGQkAAsXLmTkyJG4uLjQp08fjIyMuHbtWo6Z2dsWxjVq1KBYsWIq5wfA2tqaZcuWERgYSPPmzQkICFDiX/71119Azhlq+fLlKV++PNevX1f5XRcuXChQHaT3799XHsy3ZcsWlX/W1tb88ccfXLhwAR0dHZo1a8bOnTvZvn17jhXJ8PBwHj58yLp16xg+fDgdOnRQlijnVDnOSF9fn8ePH2eZ8Xrq1Km3Wt78OTh//jwHDx5UBlG6detG9erVuX79+lvHa/3mm28wNDRk/PjxLF++nOjo6HdKU7169bhw4YJKoX7u3Ll32tenokiRIgwYMIAtW7ZkO5MNUPKbunXrEh8frzIjPDk5mfPnz6tc/1ZWVuzZs4fw8PAcB0g/d+rkJX5+fpw6dQoLCwumTp3Krl270NXVVQnfkbncOH36NNWqVcs2HETGvC0mJuZf3Z/Z5ZOtWrWiYsWKbNy4kVOnTmFlZZXnfgoqS0tLzp07x6ZNm9DV1c1SN/rqq6/4888/Vcrf1NRU5syZw19//aWEOssYcuLx48dcv3492+9Tp+wqiNQt44sVK8aTJ09UPpvTsYL8Katr1apF0aJFs8TaP3nyJBUrVlS5586fP6/yG69du8ZXX32V6/7Tt9+5c0flugkNDSUkJOSd0pxfChcujLW1NaGhodnG6V62bBlnz57NNdRourzaMP9WftSxP0UPHjxg7dq1uLi48NNPP2FlZUX9+vWJi4tTOQ7t2rXjyJEjHDt2jBYtWqCpqYmxsTGbNm0iLi5OJQRj7dq1MTY2Zvv27coDnNPl93n/lLVo0QJ7e3vWrl3LoUOH1Mo7MpfZn9p1r07+nFf/Ru3atdHU1OTUqVPK9levXuVYN34faa5Tpw7Xrl1TKf9iYmJo3rw5ycnJebb1Dx48SEBAAF9//TVDhw5l1apVjB49mu3btwMoIfJNTU2ZOHEi4eHhmJiYKNvfRo0aNShevDhnz55VeT2nNpu65Ui9evX+dXl+9uxZ3N3dqVWrFgMHDmTJkiVKaLqMbZq8ZK6LJCUlcf/+feXvOnXqEBMTo7LabM+ePbRp04bnz5/n2sfwOfW/ZUedMuHy5cvMnz+fGTNmYGBgwMSJE5Vj/Tb9b3nV0QsqderUz549e6v7NDslS5akUqVKWepVo0ePZvbs2cogYsZnIeVWZ89O5nstLS0tzxU4devW5datW5QqVUo5rzo6OsydO5cTJ0681fe/KxmU+QSNGDGCxMREBg0axOHDh7l58yZnzpzBw8ODs2fPKjGvhwwZwu7du/H39yc+Pp6jR4/i7OzM48ePqVixIjVr1qRjx464urpy9OhR4uPjmT59OikpKZiYmCgjh1euXOHJkyfY2NiQlJTE+PHjuXTpEtHR0YwZM4ayZcuqzKT+FLRu3Zr69eszduxYjh49SkJCAp6enoSEhCgFYHrM+KtXrxIXF4evry+7d+/O8UGI6cfz0qVLajUstLS06N+/P/PnzycyMpLr16/j6+vL1atXMTc358svv+Ty5cucPHmSxMREQkJCmD9/PkCOaShUqBCDBw9m9erVrF69muvXr7N3715cXV3R0NAoMLMstm7dyqtXrxg8eDB169ZV+efo6EiRIkWUB5dZWVmxbt06Hjx4kO2MG4AqVarw/PlzduzYwa1btzh8+LASlzX9WOZ2/kxNTdHX1+fHH3/k+PHjxMbG4urqypUrV/jhhx/y6zB8tJ49e8bdu3e5e/cud+7c4eTJk7i7u1OpUiV69+5N0aJF2bFjBzdu3OCPP/5g7Nix3L17950fItqnTx8aN26Ms7PzO+3D1taWJ0+eMG3aNGJjYzl69CgzZ84E3n4w9VMyePBgzM3NsbW1ZdGiRVy6dInExET27duHg4MDISEhtGjRgu+++47SpUsrDyi/dOkSEyZM4NmzZ/Tp00fZX6dOnXj27BlBQUFZBkhLlCjBzZs3+fvvv9/3z/yoqJOXJCQkMGPGDI4ePcrNmzfZuXMnt27dUlluf/LkSfz8/IiPj2fTpk2sWbNGidmfmba2Nnfu3OHGjRtUqFDhX92fmesN8KaB3aNHDxYtWoSBgcEnPVBdv359atSowc8//5xtvcjBwYGLFy8yffp0rl69yrlz5xg/fjzx8fHo6elRvXp1vv32W2bOnMnvv//OlStXmDhxYo7HXp2yqyBSt4xv3Lgxv//+O/v27ePGjRv4+fll6aApUaIE165d4969e/lSVpcqVYrvv/8ePz8/wsPDSUhIYM2aNQQHB+Pg4KBShkyfPp2jR49y8eJFnJyc+PLLL3Osl6T76quvMDc3Z8aMGURGRnLjxg0CAwNZvHhxjmHtPiRHR0dq1KhB37592bJlC9evX+ePP/5gypQphISEMGvWLCW0TW7yasP8W/lRxy7oEhISOHTokMq/ixcvUqpUKSIjI0lISODy5ctMmzaNCxcuqByHdu3acenSJc6ePUuLFi0AMDExISwsDCMjoyznrFevXqxZswYNDQ1atWqlvJ7f5/1TN2bMGPT09JgxYwZVq1bNM+/IXGZ/ate9OvlzXv0b2tra9O/fHz8/P3bv3k1sbCwzZszItwdEq5Pm7777jjJlyjBhwgQuXbrE+fPncXFxoW7duujo6OTZ1i9atCi//PILK1asUOp6+/fvV+qR//zzDzNnziQyMpKbN29y6NAhYmJisg3rlBctLS3s7Ozw8/Nj7969xMfH4+Pjk6XzNyN1ypG2bdv+6/K8ZMmSBAcH4+3treRvERER6OnpKWGctbW1VcLLZsfY2Jj169dz4cIFpd6WcbWOra0tDx48YMaMGcTGxnLy5El8fHwwNTVFS0sLbW1tHj16pITKy+hz6n/LTl5lQkpKChMmTKBZs2Z8//33zJ49m4sXL7JkyRIg+3ZJTvKqoxdU6tSpQ0ND3/o+zc7QoUNZuXKlct+uWbOGyMhI2rdvT6VKldDV1eXXX3/l6tWr/PHHH0ybNi3X/sfM/QONGzdm+/btnDhxgvj4eFxcXPI8r926daNMmTKMHDmSs2fPEhsbi7OzMwcPHsxzYtR/5dOMD/GZq1+/Phs3bmTBggU4Ozvz4MEDSpQowTfffMO6deuUi+vbb7/F19eXxYsXs3jxYsqUKYO5uTkTJkxQ9jVnzhy8vLxwcnIiOTkZQ0NDli9frizR69WrF15eXiQkJDB16lRWr16Nj48Pffr0QUNDA1NTU7y9vT+6uNb/VpEiRVi+fDne3t44OTnx/Plzateujb+/vxIexsvLi5kzZ9KrVy9KlCiBoaEhrq6uuLi4qDyMMl3dunUxMzNj7NixjBs3TiW+aU7GjRtH0aJFcXFx4fHjx+jr67NkyRJq167N6NGjuXfvnrLEsk6dOri7uzNhwgSio6NznD3h4OCApqYmq1evxtPTk/Lly2NlZaUSR/tjFxoaSsuWLbP9jbq6unTo0IGIiAgmT55Mp06dmDlzJu3bt882ji+8uVcuXLiAp6cnSUlJ6Ojo0Lt3byIjI4mOjsbGxibX81e0aFF+/fVXPD09GTVqFCkpKTRo0IAVK1Zkic35OVi+fLnyYMvChQtTtmxZmjRpgo+PD5UrV8bDwwN/f3/WrFlDxYoVadu2LQMHDiQyMvKdZuIVKlSIWbNm0b17dwICApSHy6mrfPnyLFu2DHd3d7p3706VKlWwsbHBy8srzxAnn7LChQszb948duzYQUhICKtWreLx48dUqFCBpk2bEhQUpMRDDgoKwtPTUwlR0qRJE9auXavSaailpYWlpSXh4eFK+M10ffv2ZdKkSXTr1o2jR4/mGWbrU6VOXuLq6oqnpycTJkzg4cOH6OjoMH78eLp3767sx8LCgj///JPu3btTqVIlJk+erDwkM7MePXqwZ88eunbtyp49e/7V/Vm2bNks9QZ4Mzi+aNGiT3qVTDpLS0sWLlyYbWe7kZERy5YtY/78+VhZWaGlpUWLFi2YNGmS0ijx9PRU6mVpaWn06dNHZcZlRuqUXQWRumX8oUOHuHHjBhMmTKBQoUJ07tyZgQMHqqw0srOzw9PTkz///JOwsLB8KaunTJlC2bJlmTt3Lvfu3aNGjRpMnz49S1k0fPhwnJ2duX//Ps2bN2fZsmVqTYbx9fXF19eXGTNm8OjRI3R1dZk1axa9evV65zTnFy0tLYKCgli+fDlLly7l1q1baGpq0qBBA1auXJklpndO1GnD/Fv5UccuyMLDwwkPD1d5rXLlysyfPx8PDw+lI7h58+aMGzeORYsW8ezZM7S1tfnyyy/R19fn0aNHStiYli1b4uvrm+3zGSwtLZk9ezY9evRQKe/fx3n/lGlqajJr1iwGDBig5Bu55R2Zy+xP8brPK3/W1dXNs3/jxx9/RFNTk5kzZ/L06VMsLS3zfO5IfqZZS0uLwMBAPDw8sLGxQUNDg3bt2jFx4kQg77a+qakpbm5uLF++HF9fX4oXL46ZmRmTJ08GYOTIkbx69YpZs2Zx7949KlasiK2tLcOGDXun3zNmzBhevnzJ1KlTef78Oebm5lhYWCjPEMtM3XLk35bnderUwd/fn4CAAIKDgylcuDAtWrRg6dKlSog/BwcHli1bRlxcHFOmTMl2Py4uLri6utK3b1/KlSuHvb09z549U7ZXrlyZ5cuX4+PjQ8+ePSldujSdO3dWJtF07NiRDRs20K1bN4KCglT2rc71+SnLq0zw9fUlMTGRRYsWAW9WZo0ePRpfX19at26NgYGBSh7XoUOHHL9LnTp6QfQ2deq3uU+z079/f5KTk/Hz8+Pu3bvo6enh6+urTNbw9vbGzc2NHj16ULVqVUaPHq0M/Gcnc/+Ak5MTjx49YsiQIWhpadG7d286d+6ca/u0VKlSBAUF4eXlxeDBg0lNTaV+/foEBga+t0GZQq8L4lpTIYQQ4j25evUqjx49Unmo5unTp7GxseHAgQMF6llLQtjZ2aGjo4OHh8eHTorixIkTDBkyhN9++y3HwXEhhBCfh8TERDp06MCOHTsK9AxkIUTe9uzZQ5MmTVSey+Hg4ECVKlVwd3f/gCkTQqST+zT/yEoZIYQQIhe3b99m6NChuLm58c0333Dnzh3mzJlDs2bNZEBGiH8hNjaWK1eusGjRInr27CkDMkII8Rn766+/iI6OJjg4mNatW8uAjBCfgcDAQIKDg5k4cSIlS5YkMjKSY8eOKVEVhBAfntyn+UdWygghhBB5CA4OZvXq1SQmJlKqVCnatWvH+PHj1QozKMTH5GNaKRMZGcmPP/5Io0aNCAgI+CxCLQghhMheTEwM/fr1Q09Pj4CAAHR0dD50koQQ+SwxMREPDw9OnDjBixcvqFOnDo6OjrmGkhJCvF9yn+YfGZQRQgghhBBCCCGEEEIIIYR4Dwp/6AQIIYQQQgghhBBCCCGEEEJ8DmRQRgghhBBCCCGEEEIIIYQQ4j2QQRkhhBBCCCGEEEIIIYQQQoj3QAZlhBBCCCGEEEIIIYQQQggh3gMZlBFCCCGEEEKID+j169cfOglCCCGEEEKI90QGZYQQQgghhBAfvVOnTjFq1ChMTU1p2LAhFhYWTJ06ldjY2A+dtH/l6tWr2NjYqLymr6+Pv7//B0qREEIIIYQQIj/JoIwQQgghhBDio7ZkyRL69evHs2fPcHZ2JjAwEEdHR2JiYujZsycREREfOonvbMeOHZw5c0bltfXr19O7d+8PlCIhhBBCCCFEfir6oRMghBBCCCGEEDnZv38/c+fOZfjw4YwZM0Z5vVmzZvTo0YMff/yRyZMnU7duXb766qsPmNL/jpGR0YdOghBCCCGEECKfyEoZIYQQQgghxEcrICCAmjVrMnr06CzbihUrhqurK0WKFGHp0qUAvHz5kl9++YX27dvTqFEjunTpQkhIiMrnIiIisLKywtDQkLZt2+Lt7U1KSgoA/v7+6OvrZ/mujCHFEhMT0dfXJyIiAkdHRwwNDTEzM8Pf35+0tDTlMy9evGDu3Ll07NgRAwMDGjdujL29PRcvXlS+KyAgIMv+M4cvu3PnDs7OzpiZmdGoUSOsra2JjIzMkr41a9YwZcoUmjVrhrGxMaNHj+bevXtvd8CFEEIIIYQQ+UoGZYQQQgghhBAfpfv373P+/HnMzc0pVKhQtu8pW7YsLVu2VAYpJk2axJIlS7C2tmbx4sWYmZnx008/sWXLFgDWrVvHuHHjqF+/PgEBAQwbNozg4GBcXFzeOn0uLi6ULFkSf39/evTowYIFC/Dy8lK2T5w4kU2bNjF06FCWL1/O5MmTuXLlCk5OTrx+/ZrevXtjbW0N5Byy7N69e1hbWxMVFYWTkxP+/v7o6OgwYsQIwsLCVN7r6+tLWloaP//8MxMnTuTAgQO4u7u/9e8SQgghhBBC5B8JXyaEEEIIIYT4KN28eROAatWq5fq+GjVqEBkZyalTp4iIiGDKlCkMGDAAABMTE27dusXx48fp1q0b/v7+dOjQATc3N+XzycnJbN68WVkto66vv/4aHx8fANq0acOzZ88ICgpi+PDhFC9enKdPnzJt2jQ6d+4MvAm59vTpUzw8PLh79y5VqlShSpUqQM4hy3799Vfu37/Pjh070NXVBcDMzIyBAwfi5eVF165dKVz4zVy7unXrMmfOHOWz0dHR7Ny5861+kxBCCCGEECJ/yUoZIYQQQgghxEfp9evXwJswZbkpUqQIACdOnACgQ4cOKtvnzZvHnDlziI+P5969e7Rv315l+8CBA9m6dSsaGhpvlb5u3bqp/N2pUydevnzJ2bNn0dDQIDAwkM6dO3Pnzh1OnDjB+vXr2b9/P/AmzJo6oqKiMDY2VgZkMn733bt3iYuLU17LPLBTpUoVnj9//la/SQghhBBCCJG/ZKWMEEIIIYQQ4qOko6MD/P+KmZzcuHEDbW1tZRCnfPny2b7v4cOHuW5/W5UqVVL5u1y5cgA8fvwYgN9++w13d3fi4uIoUaIE+vr6lChRAvj/Aae8PHr0KNuVQhUqVFD5LgAtLS2V9xQuXFjt7xFCCCGEEEK8H7JSRgghhBBCCPFRKl++PEZGRuzevTvHwYWkpCSOHDmChYUFpUuXBt48iyajuLg4Tp48meP2hw8fcuTIEZ4+fao8uyY1NVXZ/vTp02y/O32QJ90///yjpPv69euMGDGCevXqsWfPHk6fPs3atWsxNzdX89e/UaZMGe7du5fl9bt37wJvnqkjhBBCCCGEKDhkUEYIIYQQQgjx0Ro5ciRxcXHMmzcvy7bU1FRmzJjBixcvGDx4ME2aNAFg7969Ku/z9fVl1qxZ1KpVi7JlyxIZGamyPTw8nCFDhpCcnEzJkiUB+Ouvv5Ttp0+fzjZt+/btU/l7165daGlpYWhoyPnz50lOTmbYsGFUr15dec9vv/0G/P9KmfTnweTkm2++4cyZM9y4cUPl9bCwMCpWrEiNGjVy/bwQQgghhBDi4yLhy4QQQgghhBAfrdatWzN58mS8vLyIiYmhZ8+eVKpUicTERNauXcvFixdxc3OjXr16AHz77bf4+Pjw4sULGjRowOHDh9mzZw/z5s2jSJEijBo1ipkzZ+Li4kKHDh24du0a8+bNw8bGhnLlymFmZsacOXOYNm0aQ4YM4e+//yYgIEAJO5bRzp07qVChAmZmZkRFRbFmzRqcnJzQ1tamQYMGFC1aFG9vbxwcHEhJSSE0NJQDBw4A8OzZMwBl9c62bdswNDTM8uwYe3t7wsLCsLe3Z+TIkZQtW5YtW7Zw7Ngx3N3d8xzUEUIIIYQQQnxcZFBGCCGEEEII8VGzt7fH2NiYlStX4unpyf3796lYsSKmpqa4ublRp04d5b3e3t4EBASwevVqHjx4QM2aNZk3bx7ffvstAP369UNbW5vAwEA2bdpE5cqVcXBwYOjQoQDUrFkTT09PFi5cyNChQ6lduzazZs1i1qxZWdI1ZswYoqKiWL9+PV9++SXTp0/HxsYGgBo1ajB37lwCAgL43//+R5kyZTAyMmL16tXY2dlx8uRJ9PX16dixI1u3bmXy5MlYW1vj4uKi8h0VK1Zk7dq1zJ07Fzc3N16+fEm9evVYsGABFhYW+XTEhRBCCCGEEPml0Gt58qMQQgghhBBCqC0xMRELCwvmzJmDlZXVh06OEEIIIYQQogCRte5CCCGEEEIIIYQQQgghhBDvgQzKCCGEEEIIIYQQQgghhBBCvAcSvkwIIYQQQgghhBBCCCGEEOI9kJUyQgghhBBCCCGEEEIIIYQQ74EMygghhBBCCCGEEEIIIYQQQrwHMigjhBBCCCGEEEIIIYQQQgjxHsigjBBCCCGEEEIIIYQQQgghxHsggzJCCCGEEEIIIYQQQgghhBDvgQzKCCGEEEIIIYQQQgghhBBCvAcyKCOEEEIIIYQQQgghhBBCCPEeyKCMEEIIIYQQQgghhBBCCCHEe/B/ZcTDwhk0rvIAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sort by count\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "OA = sns.countplot(data = df, x = 'Occupation', order=df['Occupation'].value_counts().index)\n",
    "for bars in OA.containers:\n",
    "    OA.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "317d8412-8f1c-4337-bbff-64d23e64b8b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#occupation va amount\n",
    "OA = df.groupby(['Occupation'], as_index=False)['Amount'].sum().sort_values(by=['Amount'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3e2acc15-b876-4b97-9ead-fb3ab9252bf2",
   "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>Occupation</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>IT Sector</td>\n",
       "      <td>14755079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Healthcare</td>\n",
       "      <td>13034586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Aviation</td>\n",
       "      <td>12602298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Banking</td>\n",
       "      <td>10770610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Govt</td>\n",
       "      <td>8517212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Hospitality</td>\n",
       "      <td>6376405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Media</td>\n",
       "      <td>6295832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Automobile</td>\n",
       "      <td>5368596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Chemical</td>\n",
       "      <td>5297436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Lawyer</td>\n",
       "      <td>4981665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Retail</td>\n",
       "      <td>4783170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Food Processing</td>\n",
       "      <td>4070670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Construction</td>\n",
       "      <td>3597511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Textile</td>\n",
       "      <td>3204972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Agriculture</td>\n",
       "      <td>2593087</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Occupation    Amount\n",
       "10        IT Sector  14755079\n",
       "8        Healthcare  13034586\n",
       "2          Aviation  12602298\n",
       "3           Banking  10770610\n",
       "7              Govt   8517212\n",
       "9       Hospitality   6376405\n",
       "12            Media   6295832\n",
       "1        Automobile   5368596\n",
       "4          Chemical   5297436\n",
       "11           Lawyer   4981665\n",
       "13           Retail   4783170\n",
       "6   Food Processing   4070670\n",
       "5      Construction   3597511\n",
       "14          Textile   3204972\n",
       "0       Agriculture   2593087"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "56268db0-36e8-436f-ac89-25ca8b048296",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#occupation va amount\n",
    "OA = df.groupby(['Occupation'], as_index=False)['Amount'].sum().sort_values(by=['Amount'], ascending=False).head(10)\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "OA = sns.barplot(data = OA, x = 'Occupation', y = 'Amount')\n",
    "\n",
    "for bars in OA.containers:\n",
    "    OA.bar_label(bars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ca6e0b3-57aa-4186-8905-ce9f0d736f25",
   "metadata": {},
   "source": [
    "***from above graph we can see that most of the buyers are from IT Sector, Healthcare and Aviation***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99cedd7d-07cd-4a50-8028-89ee6b99ec88",
   "metadata": {},
   "source": [
    "***Product Category***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fa5185d1-d068-4712-836e-eabd09776e6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 3000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(30,5)})\n",
    "PCA = sns.countplot(data = df, x = 'Product_Category',order=df['Product_Category'].value_counts().head(10).index)\n",
    "for bars in PCA.containers:\n",
    "    PCA.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "20e95e4c-78ba-4265-8040-0da0f38281bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#product and Amount\n",
    "PCA = df.groupby(['Product_Category'], as_index=False)['Amount'].sum().sort_values(by= ['Amount'], ascending=False).head(10)\n",
    "sns.set(rc={'figure.figsize':(25,5)})\n",
    "PCA = sns.barplot(data = PCA, x = 'Product_Category', y = 'Amount')\n",
    "for bars in PCA.containers:\n",
    "    PCA.bar_label(bars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec774ef3-5c11-476d-a944-e16ae90f24e0",
   "metadata": {},
   "source": [
    "***from the above graph we can see that most sold product are food, clothing and electronic category***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1201b923-aae6-4f68-bddf-18753d6cc9bc",
   "metadata": {},
   "source": [
    "***ProductID and order***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5ecb00b3-9edd-4189-8cc1-504d460d10d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ProductID and order\n",
    "PDO = df.groupby(['Product_ID'], as_index=False)['Orders'].sum().sort_values(by= ['Orders'], ascending=False).head(10)\n",
    "sns.set(rc={'figure.figsize':(25,5)})\n",
    "PDO = sns.barplot(data = PDO, x = 'Product_ID', y = 'Orders')\n",
    "\n",
    "for bars in PDO.containers:\n",
    "    PDO.bar_label(bars)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f0278e4-695e-46b6-9ff5-c9a800cbdcc0",
   "metadata": {},
   "source": [
    "***Conclusion***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78f93f46-1ec0-4ec0-a97a-92e5b45f6a9f",
   "metadata": {},
   "source": [
    "***Married women age group 26-35 yrs from UP, Maharastra and Karnataka working in IT, healthcare, and Aviation are more likely buy products from food ,clothing and electronics category***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f98529e6-224c-4cf5-80e2-0fd8e69e183b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bb2169e-2bdd-4abf-bcc3-bb17ab00b209",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}