mainpython_project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a5a39d6a-d694-48c7-848d-43f84d220ac2",
   "metadata": {},
   "source": [
    "# Diwali Sales Analysis Using Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "055b32c7-b05f-4b9b-a8f2-d659d82615c5",
   "metadata": {},
   "source": [
    " ### Import python Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "89fb90c3-6ea6-41bc-80b9-47ad1f49ec63",
   "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": "markdown",
   "id": "9f181a1a-bcfc-42ed-87bf-ce6ec29721ad",
   "metadata": {},
   "source": [
    "### Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "123af03f-a5d3-4d49-8e7e-8abdb377872f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv(r'C:\\Users\\HP\\Diwali Sales Data.csv',encoding = 'unicode escape')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "619d4843-ea99-49c4-adf5-6f22780434bd",
   "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": "67d257a0-4ec7-4f50-a78e-674c823ed771",
   "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>23952.00</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>23934.00</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>23924.00</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>23912.00</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>23877.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</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>Himachal Pradesh</td>\n",
       "      <td>Northern</td>\n",
       "      <td>Food Processing</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>23877.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1001132</td>\n",
       "      <td>Balk</td>\n",
       "      <td>P00018042</td>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Lawyer</td>\n",
       "      <td>Auto</td>\n",
       "      <td>4</td>\n",
       "      <td>23841.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1002092</td>\n",
       "      <td>Shivangi</td>\n",
       "      <td>P00273442</td>\n",
       "      <td>F</td>\n",
       "      <td>55+</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>IT Sector</td>\n",
       "      <td>Auto</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1003224</td>\n",
       "      <td>Kushal</td>\n",
       "      <td>P00205642</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Govt</td>\n",
       "      <td>Auto</td>\n",
       "      <td>2</td>\n",
       "      <td>23809.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1003650</td>\n",
       "      <td>Ginny</td>\n",
       "      <td>P00031142</td>\n",
       "      <td>F</td>\n",
       "      <td>26-35</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>Andhra Pradesh</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Media</td>\n",
       "      <td>Auto</td>\n",
       "      <td>4</td>\n",
       "      <td>23799.99</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",
       "5  1000588       Joni  P00057942      M     26-35   28               1   \n",
       "6  1001132       Balk  P00018042      F     18-25   25               1   \n",
       "7  1002092   Shivangi  P00273442      F       55+   61               0   \n",
       "8  1003224     Kushal  P00205642      M     26-35   35               0   \n",
       "9  1003650      Ginny  P00031142      F     26-35   26               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",
       "5  Himachal Pradesh  Northern  Food Processing             Auto       1   \n",
       "6     Uttar Pradesh   Central           Lawyer             Auto       4   \n",
       "7       Maharashtra   Western        IT Sector             Auto       1   \n",
       "8     Uttar Pradesh   Central             Govt             Auto       2   \n",
       "9    Andhra Pradesh  Southern            Media             Auto       4   \n",
       "\n",
       "     Amount  Status  unnamed1  \n",
       "0  23952.00     NaN       NaN  \n",
       "1  23934.00     NaN       NaN  \n",
       "2  23924.00     NaN       NaN  \n",
       "3  23912.00     NaN       NaN  \n",
       "4  23877.00     NaN       NaN  \n",
       "5  23877.00     NaN       NaN  \n",
       "6  23841.00     NaN       NaN  \n",
       "7       NaN     NaN       NaN  \n",
       "8  23809.00     NaN       NaN  \n",
       "9  23799.99     NaN       NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "284334b7-f792-4cae-8a4c-293b7ca6a4ba",
   "metadata": {},
   "source": [
    "## To Check Datatypes Of All Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b19aa0ee-de1d-4931-9453-0ba7501e9e85",
   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9208911a-b420-42dc-8456-1255581766f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop unrelated/blank columns\n",
    "\n",
    "df.drop(['Status', 'unnamed1'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b26a085c-b1cd-444a-93e5-9fb971ce09e5",
   "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": 8,
   "id": "d33c9f4a-d960-4563-b204-ee5cba64a414",
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "861a4899-f7b4-4718-9da9-931584263d0c",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for null values\n",
    "\n",
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "727ce2cb-a8f6-4138-9179-eb013d29f251",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11251, 13)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4f0bc8d9-0948-45d0-80b0-7e8b8e4e84f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop null values \n",
    "\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3459b052-a63c-4f74-bfa8-9014ee1a5026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11239, 13)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9061e790-522c-41c4-b818-5e942ae4da9b",
   "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              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "85df26bf-a955-48eb-ba8d-e4b1078fffa3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change data type\n",
    "\n",
    "df['Amount']=df['Amount'].astype('int')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8e555d45-bf76-458b-a466-c03de578f1f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Amount'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5f5e3c82-4770-4de8-8287-75333f0ce8ea",
   "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": "72f386e3-2171-41f9-afca-1ea098dca368",
   "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>Shaadi</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>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</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</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</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</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</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>1000695</td>\n",
       "      <td>Manning</td>\n",
       "      <td>P00296942</td>\n",
       "      <td>M</td>\n",
       "      <td>18-25</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Chemical</td>\n",
       "      <td>Office</td>\n",
       "      <td>4</td>\n",
       "      <td>370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11247</th>\n",
       "      <td>1004089</td>\n",
       "      <td>Reichenbach</td>\n",
       "      <td>P00171342</td>\n",
       "      <td>M</td>\n",
       "      <td>26-35</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>Haryana</td>\n",
       "      <td>Northern</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Veterinary</td>\n",
       "      <td>3</td>\n",
       "      <td>367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11248</th>\n",
       "      <td>1001209</td>\n",
       "      <td>Oshin</td>\n",
       "      <td>P00201342</td>\n",
       "      <td>F</td>\n",
       "      <td>36-45</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>Madhya Pradesh</td>\n",
       "      <td>Central</td>\n",
       "      <td>Textile</td>\n",
       "      <td>Office</td>\n",
       "      <td>4</td>\n",
       "      <td>213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11249</th>\n",
       "      <td>1004023</td>\n",
       "      <td>Noonan</td>\n",
       "      <td>P00059442</td>\n",
       "      <td>M</td>\n",
       "      <td>36-45</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>Karnataka</td>\n",
       "      <td>Southern</td>\n",
       "      <td>Agriculture</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11250</th>\n",
       "      <td>1002744</td>\n",
       "      <td>Brumley</td>\n",
       "      <td>P00281742</td>\n",
       "      <td>F</td>\n",
       "      <td>18-25</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>Maharashtra</td>\n",
       "      <td>Western</td>\n",
       "      <td>Healthcare</td>\n",
       "      <td>Office</td>\n",
       "      <td>3</td>\n",
       "      <td>188</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11239 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       User_ID    Cust_name Product_ID Gender Age Group  Age  Shaadi  \\\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  \n",
       "0       23952  \n",
       "1       23934  \n",
       "2       23924  \n",
       "3       23912  \n",
       "4       23877  \n",
       "...       ...  \n",
       "11246     370  \n",
       "11247     367  \n",
       "11248     213  \n",
       "11249     206  \n",
       "11250     188  \n",
       "\n",
       "[11239 rows x 13 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename(columns = {'Marital_Status':'Shaadi'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f5abd131-74c9-465b-b84d-91f281f0b1af",
   "metadata": {
    "scrolled": true
   },
   "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# describe() method returns description of the data in the DataFrame (i.e. count, mean)\n",
    "\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5e429d8c-d7b7-418f-8b44-21bb333f2688",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use describe() for specific columns\n",
    "\n",
    "df[['Age','Orders','Amount']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5915f3f9-5cf4-41b5-8ee7-c869fd8e8116",
   "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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3ebb01e9-b063-4acd-8baa-14bea9124e23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x= 'Gender',data= df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a48fc3b2-d6c2-452c-a7ee-5489501f1174",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "124371b2-56c2-4fcf-980a-4f104a48b2f6",
   "metadata": {},
   "source": [
    "### Gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1ae15088-dd51-42e2-8570-742d293a9789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a bar chart for Gender and it's count\n",
    "\n",
    "ax = sns.countplot(x ='Gender',data =df)\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f13a551a-e82a-4a90-b023-bbc2964fa9e7",
   "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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'],as_index=False)['Amount'].sum().sort_values(by='Amount',ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8474f709-d5d2-488f-bdad-fbbb69d4d7cb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Gender', ylabel='Amount'>"
      ]
     },
     "execution_count": 24,
     "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": [
    "# plotting a bar chart for gender vs total amount\n",
    "\n",
    "sales_gen = df.groupby(['Gender'],as_index=False)['Amount'].sum().sort_values(by='Amount',ascending = False)\n",
    "\n",
    "sns.barplot(x='Gender', y='Amount', data = sales_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88fcd724-4739-415d-a836-739d0d7e5cbb",
   "metadata": {},
   "source": [
    "From above graphs we can see that most of the buyers are females and even the purchasing power of females are greater than men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "362703ba-f4c7-49a1-84d9-66b14ae69560",
   "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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0677fc7b-3233-4a04-ae15-6df1f10e16a9",
   "metadata": {},
   "source": [
    "### Age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "99214c14-a696-4eda-bc23-a30c445124ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Age Group', ylabel='count'>"
      ]
     },
     "execution_count": 26,
     "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='Age Group', hue = 'Gender' , data = df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ae154316-b145-4fac-aee8-5b9769a22522",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x='Age Group', hue = 'Gender' , data = df)\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4b5475ec-eab4-46e5-b4d4-94cb7cc6f626",
   "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": [
    "# Total Amount vs Age Group\n",
    "\n",
    "sales_gen = df.groupby(['Age Group'],as_index=False)['Amount'].sum().sort_values(by='Amount',ascending = False)\n",
    "\n",
    "sns.barplot(x='Age Group', y='Amount', data = sales_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e6def5-7165-454f-a9a3-29a671b99c7a",
   "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": "21c26480-5621-41d0-8cf8-a48e665d3e6f",
   "metadata": {},
   "source": [
    "### State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "cff6c029-ae19-48d4-890e-6df8f15eb9da",
   "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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8efcd0df-bcc0-4359-80d8-76537f091f84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Orders'>"
      ]
     },
     "execution_count": 30,
     "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": [
    "# total number of orders from top 10 states\n",
    "\n",
    "sales_gen = df.groupby(['State'],as_index=False)['Orders'].sum().sort_values(by='Orders',ascending = False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(15,5)})\n",
    "sns.barplot(x='State', y='Orders', data = sales_gen)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1ae096fe-2267-4b15-af70-82520dbec3e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='State', ylabel='Amount'>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABN4AAAHTCAYAAAD4V9VfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABhEUlEQVR4nO3dd3QUZf/+8Ws3hVASIHRBRZGEGmoCgYAQUakKCCpVuhUElRJFmtSAIFWKdKRILw8qAspXFIIIKg9dBKSFhBogkJDs/P7gxz4sCSSBDJts3q9zOIfM3Dv7mblnZ2avnWIxDMMQAAAAAAAAgHRldXYBAAAAAAAAgCsieAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADABwRsAAAAAAABgAoI3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAExC8AQAAAAAAACZwd3YBmdW0adO0detWzZ8/P1XtIyIi1L59+2THFStWTJs2bUrP8gAAAAAAAOBkBG8P4Ouvv9YXX3yhqlWrpvo1lSpV0tatWx2G/fHHH+revbveeeed9C4RAAAAAAAATkbwlgZnz57VwIEDFRERoeLFi6fptZ6enipQoID979jYWI0YMULNmjXTK6+8ks6VAgAAAAAAwNm4x1sa7N27Vx4eHlqzZo0qVKiQZPyPP/6o5s2bKyAgQM8//7y++OILxcfHJzutqVOn6vr16+rbt6/ZZQMAAAAAAMAJOOMtDUJDQxUaGprsuP/7v/9Tz549FRYWpho1aujff//VZ599pqNHj2r8+PEObS9cuKA5c+boww8/VJ48eR5B5QAAAAAAAHjUCN7SydSpU/Xqq6/q9ddflyQ98cQTGjx4sN544w2dPHlSxYoVs7dduHChvL299dprrzmrXAAAAAAAAJiM4C2d7Nu3T3/99ZeWLVtmH2YYhiTpyJEjDsHbqlWr1LRpU3l5eT3yOgEAAAAAAPBoELylE5vNpi5duqhZs2ZJxt35UIUDBw7oxIkTatKkyaMsDwAAAAAAAI8YD1dIJyVLltTRo0f15JNP2v9FRkYqPDxc165ds7fbuXOn8uXLp1KlSjmxWgAAAAAAAJiN4C2ddO3aVd9//70mTZqko0ePatu2bQoLC9OVK1ccznjbt2+f/P39nVgpAAAAAAAAHgUuNU0n9evX17hx4zRt2jRNnTpVefLkUWhoqD766COHdtHR0TzJFAAAAAAAIAuwGLefAAAAAAAAAAAg3XCpKQAAAAAAAGACgjcAAAAAAADABNzjLZUMw5DNxlW5AAAAAAAAWZnVapHFYklVW4K3VLLZDF24cM3ZZQAAAAAAAMCJfH1zys0tdcGb0y81vXTpkgYMGKDatWurcuXKatWqlXbu3HnP9idPntSbb76pypUrKyQkRF988YUSExMd2nz99dd67rnnFBAQoNatW2vfvn1mzwYAAAAAAADgwOnB2wcffKDdu3dr7NixWr58uUqXLq3OnTvrn3/+SdL25s2b6ty5syRp8eLFGjRokBYtWqTJkyfb26xcuVLh4eF6//33tWLFChUrVkwdO3bUhQsXHtk8AQAAAAAAAE4N3o4fP65ffvlFgwYNUtWqVfXUU0/p008/VcGCBbV27dok7b///nudPn1a4eHh8vPzU7169fTBBx9o7ty5io+PlyRNnTpVbdu21UsvvaRnnnlGw4cPV/bs2bV06dJHPXsAAAAAAADIwpwavOXNm1fTp09X+fLl7cMslls3qIuJiUnSfufOnSpbtqxy585tH1a9enVdvXpV+/fv1/nz53Xs2DEFBwfbx7u7u6tq1ar67bffzJ0ZAAAAAAAA4A5OfbiCj4+Pnn32WYdh33//vY4fP66PP/44SfvIyEgVLlzYYVjBggUlSWfOnJG7+63ZKVKkSJI2Bw4ceOh63d2dfmUuAAAAAAAAMokM9VTTXbt2KSwsTC+88ILq1KmTZPyNGzfk4+PjMCxbtmySpLi4OF2/fl2S5OnpmaRNXFzcQ9VmtVqUN2/Oh5oGAAAAAAAAso4ME7xt3LhRH330kSpXrqwxY8Yk28bLy8t+L7fbbgdqOXLkkJeXlyQl2yZ79uwPVZ/NZigmJvahpgEAAAAAAIDMzccnu9zcUndVZIYI3hYsWKBhw4apfv36GjVqVJIz1m4rXLiwDh065DAsKipKklSoUCH7JaZRUVEqUaKEQ5tChQo9dJ0JCbaHngYAAAAAAACyBqfftGzhwoX67LPP1KZNG40dO/aeoZskBQYGat++fbp69ap92Pbt25UzZ06VKlVK+fLl01NPPaWIiAj7+ISEBO3cuVOBgYGmzgcAAAAAAABwJ6cGb0ePHtXw4cP1/PPP680339S5c+cUHR2t6OhoXblyRfHx8YqOjrZfOlqvXj0VKFBAPXv21IEDB7Rx40aNHTtWnTp1sgd2nTp10uzZs7Vy5Ur9/fff+vjjj3Xjxg21aNHCmbMKAAAAAACALMZiGIbhrDefOnWqxo0bl+y4Zs2aqVmzZmrfvr3mzZunatWqSZKOHz+uwYMHa+fOncqdO7datGih7t27y2r9X4Y4c+ZMzZs3T5cuXVK5cuXUv39/lS5d+qFqTUy06cKFaw81DQAAAAAAAGRuvr45U32PN6cGb5kJwRsAAAAAAADSErw5/R5vAAAAAAAAgCsieAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADCBu7MLcEVWq0VWq8XZZWQpNpshm43nhAAAAAAAgIyD4C2dWa0W5cmTI9VPt0D6SEy06dKlWMI3AAAAAACQYRC8pTOr1SI3N6smL/pFp6IuO7ucLKFowdx6t1VNWa0WgjcAAAAAAJBhELyZ5FTUZR07ddHZZQAAAAAAAMBJuB4SAAAAAAAAMAHBGwAAAAAAAGACgjcAAAAAAADABARvAAAAAAAAgAkI3gAAAAAAAAATELwBAAAAAAAAJiB4AwAAAAAAAExA8AYAAAAAAACYgOANAAAAAAAAMAHBGwAAAAAAAGACgjcAAAAAAADABARvAAAAAAAAgAkI3gAAAAAAAAATELwBAAAAAAAAJiB4AwAAAAAAAExA8AYAAAAAAACYgOANAAAAAAAAMAHBGwAAAAAAAGACgjcAAAAAAADABARvAAAAAAAAgAkI3gAAAAAAAAATELwBAAAAAAAAJiB4AwAAAAAAAExA8AYAAAAAAACYgOANAAAAAAAAMAHBGwAAAAAAAGACgjcAAAAAAADABO7OLuBO06ZN09atWzV//vxkx0+cOFGTJk1Kdlzz5s01YsQISVLHjh3166+/OowPCgq653QBAAAAAACA9JZhgrevv/5aX3zxhapWrXrPNp06ddLrr7/uMGz27NlatGiROnToYB928OBBDRo0SPXq1bMP8/DwSPeaAQAAAAAAgHtxevB29uxZDRw4UBERESpevPh92+bMmVM5c+a0/71v3z7NmzdPn332mfz9/SVJ58+f1/nz51WhQgUVKFDAzNIBAAAAAACAe3L6Pd727t0rDw8PrVmzRhUqVEjTa4cMGaKqVauqWbNm9mEHDx6UxWLRU089ld6lAgAAAAAAAKnm9DPeQkNDFRoamubX/fjjj9q9e7dWrVrlMPzQoUPy9vbWkCFD9MsvvyhHjhyqX7++3nnnHXl6ej5Ure7uKeeUbm5OzzKzLJY9AAAAAADISJwevD2o2bNnq27duipdurTD8EOHDikuLk4BAQHq2LGj9u/fr/DwcJ0+fVrh4eEP/H5Wq0V58+ZMuSGcxscnu7NLAAAAAAAAsMuUwdvp06cVERGh6dOnJxk3ZMgQ9e3bV7lz55Yk+fn5ycPDQ7169VKfPn2UP3/+B3pPm81QTExsiu3c3KwEQE4SE3NdiYk2Z5cBAAAAAABcmI9P9lRfdZcpg7eNGzfK19dXNWvWTDLO3d3dHrrdVrJkSUlSZGTkAwdvkpSQQKiTkSUm2ugjAAAAAACQYWTKm2Lt3LlTQUFBcndPmhu2a9dOYWFhDsP27NkjDw+PFJ+aCgAAAAAAAKSXDB28JSYmKjo6Wjdu3HAYvm/fPpUqVSrZ17z44otavXq1Fi1apBMnTmj9+vUKDw9X586dlStXrkdRNgAAAAAAAJCxLzU9c+aMnnvuOY0YMULNmze3D4+OjlaePHmSfU3btm1lsVg0f/58DR8+XAUKFFCHDh3UrVu3R1Q1AAAAAAAAIFkMwzCcXURmkJho04UL11Js5+5uVd68OfXx+PU6duriI6gMxYvm1fD3G+rixWvc4w0AAAAAAJjK1zdnqh+ukKEvNQUAAAAAAAAyK4I3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAExC8AQAAAAAAACYgeAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADABwRsAAAAAAABgAoI3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAExC8AQAAAAAAACYgeAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADABwRsAAAAAAABgAoI3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAExC8AQAAAAAAACYgeAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADABwRsAAAAAAABgAoI3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAExC8AQAAAAAAACYgeAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADABwRsAAAAAAABgAoI3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAE2So4G3atGlq167dfdusWbNG/v7+Sf6dPHnS3ubbb79Vw4YNFRAQoKZNm2rbtm1mlw4AAAAAAAA4yDDB29dff60vvvgixXYHDx5UUFCQtm7d6vCvSJEikqTt27erd+/eev3117Vy5UoFBwerW7duOnLkiMlzAAAAAAAAAPyPu7MLOHv2rAYOHKiIiAgVL148xfaHDh2Sv7+/ChQokOz4GTNmqF69emrfvr0kqW/fvtq9e7fmzp2rIUOGpGfpAAAAAAAAwD05PXjbu3evPDw8tGbNGk2ePFmnTp26b/uDBw8qNDQ02XE2m027du1Sv379HIZXq1ZNGzZseOha3d1TPkHQzS3DnESY5bDsAQAAAABARuL04C00NPSeQdrdLl++rLNnz2rnzp1auHChLl68qICAAPXu3VtPPfWUYmJiFBsbq8KFCzu8rmDBgoqMjHyoOq1Wi/LmzflQ04C5fHyyO7sEAAAAAAAAO6cHb2lx+PBhSZJhGBoxYoRu3LihL7/8Uq1bt9batWuVkJAgSfL09HR4XbZs2RQXF/dQ722zGYqJiU2xnZublQDISWJirisx0ebsMgAAAAAAgAvz8cme6qvuMlXwVrVqVW3btk158+aVxWKRJE2aNEl16tTRihUr1LJlS0lSfHy8w+vi4uKUPfvDh2EJCYQ6GVlioo0+AgAAAAAAGUamCt4kydfX1+Hv7Nmzq1ixYjp79qzy5MmjHDlyKCoqyqFNVFSUChUq9CjLBAAAAAAAQBaXqe5Gv2TJElWrVk2xsf+75PPq1as6duyYnnnmGVksFlWuXFk7duxweF1ERISqVq36qMsFAAAAAABAFpahg7fExERFR0frxo0bkqTatWvLZrOpT58+Onz4sPbs2aPu3bvL19dXzZs3lyR17NhR//nPfzR79mwdOXJE4eHh2r9/v9544w1nzgoAAAAAAACymAwdvJ05c0YhISFav369JKlIkSKaM2eOYmNj1apVK3Xo0EHe3t6aN2+esmXLJkkKCQnR8OHDtWjRIjVr1kzbt2/X1KlTVaJECWfOCgAAAAAAALIYi2EYhrOLyAwSE226cOFaiu3c3a3KmzenPh6/XsdOXXwElaF40bwa/n5DXbx4jYcrAAAAAAAAU/n65kz1U00z9BlvAAAAAAAAQGZF8AYAAAAAAACYwN3ZBQCZgdVqkdVqcXYZWYrNZshm40p4AAAAAEDmRfAGpMBqtShPnhypvn4b6SMx0aZLl2IJ3wAAAAAAmRbBG5ACq9UiNzerJi/6RaeiLju7nCyhaMHcerdVTVmtFoI3AAAAAECmRfAGpNKpqMs8qRYAAAAAAKQa184BAAAAAAAAJiB4AwAAAAAAAExA8AYAAAAAAACYgOANAAAAAAAAMAHBGwAAAAAAAGACgjcAAAAAAADABARvAAAAAAAAgAkI3gAAAAAAAAATELwBAAAAAAAAJiB4AwAAAAAAAExA8AYAAAAAAACYgOANAAAAAAAAMAHBGwAAAAAAAGACd2cXAACPmtVqkdVqcXYZWYrNZshmM5xdBgAAAAA8UgRvALIUq9WiPHlyyM2NE34fpcREmy5diiV8AwAAAJClELwByFKsVovc3KyavOgXnYq67OxysoSiBXPr3VY1ZbVaCN4AAAAAZCkEbwCypFNRl3Xs1EVnlwEAAAAAcGFcawUAAAAAAACYgOANAAAAAAAAMAHBGwAAAAAAAGACgjcAAAAAAADABARvAAAAAAAAgAkI3gAAAAAAAAATELwBAAAAAAAAJiB4AwAAAAAAAExA8AYAAAAAAACYgOANAAAAAAAAMAHBGwAAAAAAAGACgjcAAAAAAADABBkqeJs2bZratWt33zaHDx9Wt27dVK1aNQUHB6tHjx46ffq0fXxiYqICAgLk7+/v8G/ixIlmlw8AAAAAAADYuTu7gNu+/vprffHFF6pateo921y8eFEdO3ZU5cqVNX/+fMXHx2vkyJHq0qWLVq5cqWzZsunYsWOKi4vT6tWrlS9fPvtrc+TI8ShmAwAAAAAAAJCUAYK3s2fPauDAgYqIiFDx4sXv23bjxo2KjY1VeHi4vLy8JEmjR49WnTp1tGvXLgUHB+vgwYPKlSuXSpUq9QiqBwAAAAAAAJLn9EtN9+7dKw8PD61Zs0YVKlS4b9vg4GBNmTLFHrpJktV6axZiYmIkSQcPHlSJEiXMKxgAAAAAAABIBaef8RYaGqrQ0NBUtS1WrJiKFSvmMGz69Ony8vJSYGCgJOnQoUNKSEhQ586ddeDAARUqVEhvvPGGXn755Yeu1d095ZzSzc3pWWaWZdayp0+dx4xlT386D8seAAAAQFbj9ODtYcyfP18LFixQ//795evrK+nWwxdsNpt69OihwoULa8uWLQoLC9PNmzfVokWLB34vq9WivHlzplfpMIGPT3Znl4B0Rp+6FvoTAAAAQFaTKYM3wzA0fvx4ffnll3r77bcdnoS6bt06JSYmKmfOWyFZqVKldPr0ac2cOfOhgjebzVBMTGyK7dzcrHy5dJKYmOtKTLSl+3TpU+cxo0/pT+cx6zMKAAAAAI+Sj0/2VF/Rk+mCt5s3byosLEzr1q1TWFiYOnTo4DD+zvu/3ebn56c1a9Y89HsnJPCFMSNLTLTRRy6GPnUt9CcAAACArCbT3XCnT58++u677/T5558nCd1iYmIUFBSkFStWOAzfs2ePSpYs+QirBAAAAAAAQFaXoc94S0xM1IULF+Tt7S0vLy+tWLFC69evV58+fRQUFKTo6Gh7W29vb/n4+Kh69eoaN26c8uXLpyeffFIbNmzQmjVrNG3aNCfOCQAAAAAAALKaDH3G25kzZxQSEqL169dLunX/NkkKDw9XSEiIw7/bbYYPH66GDRtq4MCBatKkidavX68JEyaoVq1aTpsPAAAAAAAAZD0Z6oy3kSNHOvxdrFgxHTx40P73rFmzUpxGrly5FBYWprCwsHSvDwAAAAAAAEitDH3GGwAAAAAAAJBZEbwBAAAAAAAAJiB4AwAAAAAAAExA8AYAAAAAAACYgOANAAAAAAAAMAHBGwAAAAAAAGACgjcAAAAAAADABARvAAAAAAAAgAkI3gAAAAAAAAATuKf1BadPn1aBAgXk4eGRZFxcXJz27t2rypUrp0txAACkxGq1yGq1OLuMLMVmM2SzGc4uAwAAAMjw0hy8Pffcc1qyZIkCAgKSjPvrr7/UpUsX/fnnn+lSHAAA92O1WpQnTw65uXEC96OUmGjTpUuxhG8AAABAClIVvI0aNUqXLl2SJBmGoSlTpihv3rxJ2u3fv1/e3t7pWiAAAPditVrk5mbV5EW/6FTUZWeXkyUULZhb77aqKavVQvAGAAAApCBVwdvTTz+tL7/8UpJksVj03//+V56eng5t3Nzc5O3trbCwsPSvEgCA+zgVdVnHTl10dhkAAAAA4CBVwVvLli3VsmVLSVJoaKimTJmiUqVKmVoYAAAAAAAAkJml+R5vmzdvNqMOAAAAAAAAwKWkOXgzDENLly7Vjz/+qOvXr8tmszmMt1gsmjt3broVCAAAAAAAAGRGaQ7ePv/8c3311VcqVqyYChcuLIvF4jDeMLjRMgAAAAAAAJDm4G3VqlXq2LGj+vbta0Y9AAAAAAAAgEuwpvUFV69eVZ06dUwoBQAAAAAAAHAdaQ7eqlSpol27dplRCwAAAAAAAOAy0nypaZcuXdS7d28lJCSoQoUKyp49e5I2gYGB6VIcAAAAAAAAkFmlOXjr2LGjJGny5MmS5PBwBcMwZLFYtH///nQqDwAAAAAAAMic0hy8zZs3z4w6AAAAAAAAAJeS5uAtKCjIjDoAAAAAAAAAl5Lm4G3VqlUptmnatOkDlAIAAAAAAAC4jjQHb/369Ut2uMVikZubm9zc3AjeAAAAAAAAkOWlOXjbtGlTkmGxsbHauXOnZsyYYX/oAgAAAAAAAJCVpTl4K1q0aLLDS5YsqZs3b+qzzz7TwoULH7owAAAAAAAAIDOzpufE/P39tXfv3vScJAAAAAAAAJAppVvwFh8fr2XLlilfvnzpNUkAAAAAAAAg00rzpaahoaGyWCwOw2w2my5evKi4uDj17ds33YoDAAAAAAAAMqs0B29BQUFJgjdJypUrl+rWrasaNWqkS2EAAAAAAABAZpbm4G3kyJFm1AEAAAAAAAC4lDQHb9Kt+7ktX75cO3bsUExMjPLmzauqVauqadOm8vLySu8aAQAAAAAAgEwnzcFbTEyM2rdvrwMHDuixxx5TgQIFdPToUa1bt05ff/21Fi5cKG9vbzNqBQAAAAAAADKNND/V9PPPP1dkZKQWLFigzZs3a8mSJdq8ebMWLFig8+fPa/z48Q9czLRp09SuXbv7trl48aI+/PBDBQYGKigoSIMHD9b169cd2nz77bdq2LChAgIC1LRpU23btu2BawIAAAAAAAAeRJqDt02bNqlnz56qWrWqw/CqVauqR48e2rBhwwMV8vXXX+uLL75IsV2PHj10/PhxzZkzR+PHj9eWLVs0aNAg+/jt27erd+/eev3117Vy5UoFBwerW7duOnLkyAPVBQAAAAAAADyINAdv165d0+OPP57suMcff1yXLl1K0/TOnj2rt956S2PGjFHx4sXv23b37t3asWOHRo0apbJlyyo4OFhDhgzR6tWrdfbsWUnSjBkzVK9ePbVv314lSpRQ3759VbZsWc2dOzdNdQEAAAAAAAAPI83B29NPP60ff/wx2XE//vijnnzyyTRNb+/evfLw8NCaNWtUoUKF+7bduXOnChQooBIlStiHBQUFyWKx6Pfff5fNZtOuXbsUHBzs8Lpq1arpt99+S1NdAAAAAAAAwMNI88MVOnfurA8//FCJiYlq1KiR8ufPr3PnzmndunX65ptvNHDgwDRNLzQ0VKGhoalqe/bsWRUpUsRhmKenp/LkyaMzZ84oJiZGsbGxKly4sEObggULKjIyMk11JcfdPeWc0s0tzVkm0olZy54+dR4zlj396Tz0p2th2QMAAAApS3Pw1rBhQx07dkxTp07V4sWLJUmGYcjT01PvvPOOXnvttXQv8rbr16/L09MzyfBs2bIpLi5ON27ckKQkbW6PfxhWq0V58+Z8qGnAXD4+2Z1dAtIZfepa6E/XQn8CAAAAKUtz8CZJ77zzjtq2bavdu3crJiZGuXPnVoUKFZQ7d+70rs+Bl5eX4uPjkwyPi4tTjhw5lC1bNklK0iYuLk7Zsz/cFwSbzVBMTGyK7dzcrHwZcZKYmOtKTLSl+3TpU+cxo0/pT+ehP12LWdtcAAAAIKPz8cme6itAHih4u/UmPnr22Wcf9OUPpHDhwtq4caPDsPj4eF26dEkFCxZUnjx5lCNHDkVFRTm0iYqKUqFChR76/RMS+IKRkSUm2ugjF0Ofuhb607XQnwAAAEDK0nyDltOnT+utt95SUFCQSpcuneRfmTJlzKhTkhQYGKjIyEgdP37cPmzHjh2SpCpVqshisahy5cr2YbdFRESoatWqptUFAAAAAAAA3C3NZ7x98skn+uOPP/TKK68oT548JpT0P4mJibpw4YK8vb3l5eWlChUqqHLlyurVq5cGDRqk2NhYDRgwQE2bNrWf0daxY0d169ZNZcqUUe3atbV8+XLt379fw4YNM7VWAAAAAAAA4E5pDt7++OMPDR06VI0aNTKjHgdnzpzRc889pxEjRqh58+ayWCyaNGmSBg8erDfeeEPZsmVT/fr1FRYWZn9NSEiIhg8frilTpmjcuHF65plnNHXqVJUoUcL0egEAAAAAAIDb0hy8FShQ4KEfVHAvI0eOdPi7WLFiOnjwoMOwfPnyacKECfedTtOmTdW0adP0Lg8AAAAAAABItTTf4+3NN9/UxIkTderUKTPqAQAAAAAAAFxCms94q1Onjr766ivVq1dPefPmTXL2m8ViSfLkUQAAAAAAACCrSXPwFhYWphMnTigkJET58+c3oyYAAAAAAAAg00tz8LZjxw4NHDhQLVu2NKMeAAAAAAAAwCWk+R5vPj4+KlKkiBm1AAAAAAAAAC4jzcFbq1atNH36dF29etWMegAAAAAAAACXkOZLTc+cOaO9e/cqJCRETz/9tHLlypWkzbx589KlOAAAAAAAACCzSnPwdvToUZUpU8b+t2EYDuPv/hsAAAAAAADIitIcvM2fPz/Z4WfPntXSpUu1bNmyhy4KAAAAAAAAyOzSHLzd7eeff9bixYu1ZcsWJSQk6PHHH0+PugAAAAAAAIBM7YGCtwsXLmjZsmX65ptvdOrUKeXKlUvNmjXTyy+/rKpVq6Z3jQAAAAAAAECmk6bgbfv27VqyZIk2btyoxMREValSRadOndLkyZMVFBRkVo0AAAAAAABAppOq4G3OnDlasmSJjh49qieffFLvvPOOmjVrphw5cigoKEgWi8XsOgEAAAAAAIBMJVXB28iRI+Xv76958+Y5nNl25coV0woDAAAAAAAAMjNraho1atRIx48f15tvvql33nlHP/zwgxISEsyuDQAAAAAAAMi0UnXG2+eff66rV69q7dq1WrFihbp37668efOqXr16slgsXGoKAAAAAAAA3CVVZ7xJUq5cudSqVSstXbpUa9eu1csvv6zNmzfLMAx9/PHHGj9+vP7++28zawUAAAAAAAAyjVQHb3cqWbKk+vXrpy1btmjixIl6+umnNWPGDDVp0kQvvfRSetcIAAAAAAAAZDqputT0ni92d9fzzz+v559/XufOndPKlSu1cuXK9KoNAAAAAAAAyLQe6Iy35OTPn19du3bV+vXr02uSAAAAAAAAQKaVbsEbAAAAAAAAgP8heAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADABwRsAAAAAAABgAoI3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAE7g7uwAAAIDbrFaLrFaLs8vIUmw2Qzab4ewyAAAAXBLBGwAAyBCsVovy5MkhNzdOyH+UEhNtunQp1rTwjTD10SNMBQAg4yB4AwAAGYLVapGbm1WTF/2iU1GXnV1OllC0YG6926qmrFaLKUENYapzmB2mAgCA1CN4AwAAGcqpqMs6duqis8tAOiBMffTMDlMBAEDaELwBAADAVISpAAAgq+K8fwAAAAAAAMAEBG8AAAAAAACACQjeAAAAAAAAABM4/R5vNptNkyZN0tKlS3XlyhUFBgZqwIABevzxx5O0nThxoiZNmpTsdJo3b64RI0ZIkjp27Khff/3VYXxQUJDmz5+f/jMAAAAAAAAAJMPpwduUKVO0cOFCjRw5UoULF9bo0aPVpUsXrV27Vp6eng5tO3XqpNdff91h2OzZs7Vo0SJ16NDBPuzgwYMaNGiQ6tWrZx/m4eFh6nwAAAAAAAAAd3Jq8BYfH69Zs2bpo48+Up06dSRJ48aNU61atbRhwwY1btzYoX3OnDmVM2dO+9/79u3TvHnz9Nlnn8nf31+SdP78eZ0/f14VKlRQgQIFHtm8AAAAAAAAAHdyavB24MABXbt2TcHBwfZhPj4+KlOmjH777bckwdvdhgwZoqpVq6pZs2b2YQcPHpTFYtFTTz2V7vW6u6d8Szw3N26b5yxmLXv61HnMWPb0p/PQn66F/nQt7ENdD8seAICMwanBW2RkpCSpSJEiDsMLFixoH3cvP/74o3bv3q1Vq1Y5DD906JC8vb01ZMgQ/fLLL8qRI4fq16+vd955J8mlq2lhtVqUN2/OlBvCaXx8sju7BKQz+tS10J+uhf50LfSn66FPAQDIGJwavF2/fl2SkgRi2bJl0+XLl+/72tmzZ6tu3boqXbq0w/BDhw4pLi5OAQEB6tixo/bv36/w8HCdPn1a4eHhD1yrzWYoJiY2xXZublYOdJwkJua6EhNt6T5d+tR5zOhT+tN56E/XQn+6FvahrsesPgUAALd+4Ert2eVODd68vLwk3brX2+3/S1JcXJyyZ7/3Qdrp06cVERGh6dOnJxk3ZMgQ9e3bV7lz55Yk+fn5ycPDQ7169VKfPn2UP3/+B643IYGDl4wsMdFGH7kY+tS10J+uhf50LfSn66FPAQDIGJx684fbl5hGRUU5DI+KilKhQoXu+bqNGzfK19dXNWvWTDLO3d3dHrrdVrJkSUlK8fJVAAAAAAAAIL04NXgrVaqUcuXKpYiICPuwmJgY7du3T4GBgfd83c6dOxUUFCR396Qn7LVr105hYWEOw/bs2SMPDw8VL1483WoHAAAAAAAA7sepl5p6enqqbdu2GjNmjHx9fVW0aFGNHj1ahQsX1gsvvKDExERduHBB3t7eDpei7tu3T6+88kqy03zxxRc1fPhwBQQEKCQkRHv27FF4eLg6d+6sXLlyPapZAwAAAAAAQBbn1OBNknr06KGEhAT1799fN27cUGBgoGbOnCkPDw+dPHlSzz33nEaMGKHmzZvbXxMdHa08efIkO722bdvKYrFo/vz5Gj58uAoUKKAOHTqoW7duj2iOAAAAAAAAgAwQvLm5ual3797q3bt3knHFihXTwYMHkwz/888/7zvNNm3aqE2bNulWIwAAAAAAAJBWTr3HGwAAAAAAAOCqCN4AAAAAAAAAExC8AQAAAAAAACYgeAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADABwRsAAAAAAABgAoI3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAExC8AQAAAAAAACYgeAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADABwRsAAAAAAABgAoI3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAExC8AQAAAAAAACYgeAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADABwRsAAAAAAABgAoI3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAExC8AQAAAAAAACYgeAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADABwRsAAAAAAABgAoI3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAExC8AQAAAAAAACZwevBms9k0YcIE1apVSxUrVlTXrl114sSJe7Zfs2aN/P39k/w7efKkvc23336rhg0bKiAgQE2bNtW2bdsexawAAAAAAAAAdk4P3qZMmaKFCxfqs88+0+LFi2Wz2dSlSxfFx8cn2/7gwYMKCgrS1q1bHf4VKVJEkrR9+3b17t1br7/+ulauXKng4GB169ZNR44ceZSzBQAAAAAAgCzOqcFbfHy8Zs2apR49eqhOnToqVaqUxo0bp8jISG3YsCHZ1xw6dEj+/v4qUKCAwz83NzdJ0owZM1SvXj21b99eJUqUUN++fVW2bFnNnTv3Uc4aAAAAAAAAsjinBm8HDhzQtWvXFBwcbB/m4+OjMmXK6Lfffkv2NQcPHlSJEiWSHWez2bRr1y6H6UlStWrV7jk9AAAAAAAAwAzuznzzyMhISbJfJnpbwYIF7ePudPnyZZ09e1Y7d+7UwoULdfHiRQUEBKh379566qmnFBMTo9jYWBUuXDhV00srd/eUc0o3N6dfvZtlmbXs6VPnMWPZ05/OQ3+6FvrTtbAPdT1mLXuLxSKr1WLKtJE8m82QYRjOLgMA8ICcGrxdv35dkuTp6ekwPFu2bLp8+XKS9ocPH5YkGYahESNG6MaNG/ryyy/VunVrrV27VgkJCfecXlxc3EPVarValDdvzoeaBszl45Pd2SUgndGnroX+dC30p2uhP12PWX1qsxkEb48YyxwAMjenBm9eXl6Sbt3r7fb/JSkuLk7Zsyc9WKhataq2bdumvHnzymK5tfOZNGmS6tSpoxUrVqhly5b26d3pXtNLC5vNUExMbIrt3NysHLw6SUzMdSUm2tJ9uvSp85jRp/Sn89CfroX+dC3sQ12PmZ/RyYt+0amopD+SI/0VLZhb77aqadpnFADwYHx8sqf67HKnBm+3LzGNiorSE088YR8eFRUlf3//ZF/j6+vr8Hf27NlVrFgxnT17Vnny5FGOHDkUFRXl0CYqKkqFChV66HoTEtjZZWSJiTb6yMXQp66F/nQt9KdroT9dj5l9eirqso6dumjKtJE8PqMAkHk59cYbpUqVUq5cuRQREWEfFhMTo3379ikwMDBJ+yVLlqhatWqKjf3fmWdXr17VsWPH9Mwzz8hisahy5crasWOHw+siIiJUtWpV82YEAAAAAAAAuItTgzdPT0+1bdtWY8aM0aZNm3TgwAH16tVLhQsX1gsvvKDExERFR0frxo0bkqTatWvLZrOpT58+Onz4sPbs2aPu3bvL19dXzZs3lyR17NhR//nPfzR79mwdOXJE4eHh2r9/v9544w1nzioAAAAAAACyGKc/aqpHjx5q0aKF+vfvr1atWsnNzU0zZ86Uh4eHzpw5o5CQEK1fv17SrUtT58yZo9jYWLVq1UodOnSQt7e35s2bp2zZskmSQkJCNHz4cC1atEjNmjXT9u3bNXXqVJUoUcKZswkAAAAAAIAsxqn3eJMkNzc39e7dW717904yrlixYjp48KDDsLJly2rWrFn3nWbTpk3VtGnT9CwTAAAAAAAASBOnn/EGAAAAAAAAuCKCNwAAAAAAAMAEBG8AAAAAAACACQjeAAAAAAAAABMQvAEAAAAAAAAmIHgDAAAAAAAATODu7AIAAAAAAI+e1WqR1WpxdhlZis1myGYznF0GgEeI4A0AAAAAshir1aI8eXLIzY2LoB6lxESbLl2KJXwDshCCNwAAAADIYqxWi9zcrJq86Bedirrs7HKyhKIFc+vdVjVltVoI3oAshOANAAAAALKoU1GXdezURWeXAQAui+ANAAAAAIBMjnv2PXrcsw+pQfAGAAAAAEAmxj37nIN79iE1CN4AAAAAAMjEuGffo/co7tnHWYyPnhlnMRK8AQAAAADgArhnn+vgLEbnMOMsRoI3AAAAAACADISzGB89s85iJHgDAAAAAADIgDiLMfPjnEUAAAAAAADABARvAAAAAAAAgAkI3gAAAAAAAAATELwBAAAAAAAAJiB4AwAAAAAAAExA8AYAAAAAAACYgOANAAAAAAAAMAHBGwAAAAAAAGACgjcAAAAAAADABARvAAAAAAAAgAkI3gAAAAAAAAATELwBAAAAAAAAJiB4AwAAAAAAAExA8AYAAAAAAACYgOANAAAAAAAAMAHBGwAAAAAAAGACgjcAAAAAAADABARvAAAAAAAAgAkI3gAAAAAAAAATOD14s9lsmjBhgmrVqqWKFSuqa9euOnHixD3bHz58WN26dVO1atUUHBysHj166PTp0/bxiYmJCggIkL+/v8O/iRMnPorZAQAAAAAAACRlgOBtypQpWrhwoT777DMtXrxYNptNXbp0UXx8fJK2Fy9eVMeOHeXl5aX58+drxowZunDhgrp06aK4uDhJ0rFjxxQXF6fVq1dr69at9n+dOnV61LMGAAAAAACALMypwVt8fLxmzZqlHj16qE6dOipVqpTGjRunyMhIbdiwIUn7jRs3KjY2VuHh4fLz81O5cuU0evRoHTlyRLt27ZIkHTx4ULly5VKpUqVUoEAB+7+cOXM+6tkDAAAAAABAFubU4O3AgQO6du2agoOD7cN8fHxUpkwZ/fbbb0naBwcHa8qUKfLy8rIPs1pvzUJMTIykW8FbiRIlTK4cAAAAAAAAuD93Z755ZGSkJKlIkSIOwwsWLGgfd6dixYqpWLFiDsOmT58uLy8vBQYGSpIOHTqkhIQEde7cWQcOHFChQoX0xhtv6OWXX37oet3dU84p3dycfvVulmXWsqdPnceMZU9/Og/96VroT9fCPtT18Bl1LfSna6E/XQv7UNeT3sveqcHb9evXJUmenp4Ow7Nly6bLly+n+Pr58+drwYIF6t+/v3x9fSXdeviCzWZTjx49VLhwYW3ZskVhYWG6efOmWrRo8cC1Wq0W5c3L5aoZmY9PdmeXgHRGn7oW+tO10J+uhf50PfSpa6E/XQv96VroT9eT3n3q1ODt9iWj8fHxDpePxsXFKXv2e8+oYRgaP368vvzyS7399ttq166dfdy6deuUmJhov6dbqVKldPr0ac2cOfOhgjebzVBMTGyK7dzcrHzwnCQm5roSE23pPl361HnM6FP603noT9dCf7oW9qGuh8+oa6E/XQv96VrYh7qe1PSpj0/2VJ8Z59Tg7fYlplFRUXriiSfsw6OiouTv75/sa27evKmwsDCtW7dOYWFh6tChg8P4OwO82/z8/LRmzZqHrjchIf0/TEg/iYk2+sjF0Keuhf50LfSna6E/XQ996lroT9dCf7oW+tP1pHefOvWi4VKlSilXrlyKiIiwD4uJidG+ffvs92y7W58+ffTdd9/p888/TxK6xcTEKCgoSCtWrHAYvmfPHpUsWTLd6wcAAAAAAADuxalnvHl6eqpt27YaM2aMfH19VbRoUY0ePVqFCxfWCy+8oMTERF24cEHe3t7y8vLSihUrtH79evXp00dBQUGKjo62T8vb21s+Pj6qXr26xo0bp3z58unJJ5/Uhg0btGbNGk2bNs2JcwoAAAAAAICsxqnBmyT16NFDCQkJ6t+/v27cuKHAwEDNnDlTHh4eOnnypJ577jmNGDFCzZs317p16yRJ4eHhCg8Pd5jO7TbDhw/XxIkTNXDgQJ0/f14lSpTQhAkTVKtWLWfMHgAAAAAAALIopwdvbm5u6t27t3r37p1kXLFixXTw4EH737NmzUpxerly5VJYWJjCwsLStU4AAAAAAAAgLZx6jzcAAAAAAADAVRG8AQAAAAAAACYgeAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADABwRsAAAAAAABgAoI3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAExC8AQAAAAAAACYgeAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADABwRsAAAAAAABgAoI3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAExC8AQAAAAAAACYgeAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADABwRsAAAAAAABgAoI3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAExC8AQAAAAAAACYgeAMAAAAAAABMQPAGAAAAAAAAmIDgDQAAAAAAADABwRsAAAAAAABgAoI3AAAAAAAAwAQEbwAAAAAAAIAJCN4AAAAAAAAAExC8AQAAAAAAACYgeAMAAAAAAABMQPAGAAAAAAAAmMDpwZvNZtOECRNUq1YtVaxYUV27dtWJEyfu2f7ixYv68MMPFRgYqKCgIA0ePFjXr193aPPtt9+qYcOGCggIUNOmTbVt2zazZwMAAAAAAABw4PTgbcqUKVq4cKE+++wzLV68WDabTV26dFF8fHyy7Xv06KHjx49rzpw5Gj9+vLZs2aJBgwbZx2/fvl29e/fW66+/rpUrVyo4OFjdunXTkSNHHtEcAQAAAAAAAE4O3uLj4zVr1iz16NFDderUUalSpTRu3DhFRkZqw4YNSdrv3r1bO3bs0KhRo1S2bFkFBwdryJAhWr16tc6ePStJmjFjhurVq6f27durRIkS6tu3r8qWLau5c+c+6tkDAAAAAABAFmYxDMNw1pv/9ddfatmypb777js99dRT9uGtWrWSn5+fBg8e7NB+xowZmjt3rrZu3WofFh8frwoVKujzzz9X/fr1VaVKFfXr10+vvfaavc24ceO0YcMGffvttw9cq2EYstlSXlQWi2S1WnX56g0lJtoe+P2Qem5uVuXO5SWbzSYz1mb69NEzs0/pz0eP/nQt9KdrYR/qeviMuhb607XQn66FfajrSUufWq0WWSyWVE3XPR1qe2CRkZGSpCJFijgML1iwoH3cnc6ePZukraenp/LkyaMzZ84oJiZGsbGxKly4cKqmlxYWi0VubqlbqJKUO5fXQ70f0s5qNfcETvr00TOzT+nPR4/+dC30p2thH+p6+Iy6FvrTtdCfroV9qOtJ7z516qWmtx+K4Onp6TA8W7ZsiouLS7b93W3vbH/jxo00TQ8AAAAAAAAwi1ODNy+vW8nt3Q9SiIuLU/bs2ZNtn9xDF+Li4pQjRw5ly5YtTdMDAAAAAAAAzOLU4O32ZaNRUVEOw6OiolSoUKEk7QsXLpykbXx8vC5duqSCBQsqT548ypEjR6qnBwAAAAAAAJjFqcFbqVKllCtXLkVERNiHxcTEaN++fQoMDEzSPjAwUJGRkTp+/Lh92I4dOyRJVapUkcViUeXKle3DbouIiFDVqlVNmgsAAAAAAAAgKac+XMHT01Nt27bVmDFj5Ovrq6JFi2r06NEqXLiwXnjhBSUmJurChQvy9vaWl5eXKlSooMqVK6tXr14aNGiQYmNjNWDAADVt2tR+RlvHjh3VrVs3lSlTRrVr19by5cu1f/9+DRs2zJmzCgAAAAAAgCzGYhhmPPg29RITEzV27FitWLFCN27cUGBgoAYMGKBixYrp5MmTeu655zRixAg1b95cknT+/HkNHjxYP//8s7Jly6b69esrLCzMfn83SVq1apWmTJmiyMhIPfPMM+rdu7eCg4OdNYsAAAAAAADIgpwevAEAAAAAAACuyKn3eAMAAAAAAABcFcEbAAAAAAAAYAKCNwAAAAAAAMAEBG8AAAAAAACACQjeAAAAAAAAABMQvAEAAAAAAAAmIHgzSbt27dSvX79kx/Xr10/t2rWz/3369Gn95z//sf998eJFLV261JSa/P397f9KlSqlSpUqqUWLFvrhhx/S/f1Onjwpf39/RUREpMv0VqxYIX9//3SZ1sMKDQ2Vv7+/Zs+enez4AQMGyN/fXxMnTkz19FLb9lGaOHGiQkND79vm7vXXVd2rj4YOHarSpUtr5cqVTqjqf37//Xft3Lkz1e3v3g4h7W5vB27/K1eunOrUqaOBAwfqwoULqZ7O3Z8zf39/rVixItXtM4v03m7e731uT8MZ+43b73n3uhEaGqpRo0YpLi4u3d/zfsccDyKldfB+rl69qgoVKqhGjRq6efPmQ9dydx8+qv2lq/djWo5TH2Z9SA/p0ecpTePuvi5durQCAwPVtWtXHTx48KHeOznpvW3KKPv0tKxXcC3JHRO9+OKL+uqrr+xt7lw/MtL3uqzOMAytWLFC7dq1U/Xq1VWuXDk9//zzGjZsmKKjo1M9nUf1GT98+LB++ukn098nvbk7uwBIffv2VdGiRdWoUSNJUnh4uE6ePKmWLVum+3s1aNBAn3zyiaRbH7Jr167pq6++Uo8ePbRkyRIFBASk+3u6Kg8PD33//ffq2LGjw/CEhARt2LBBFovFSZU9Wnevv1nJ0KFDtWjRIo0ePVqNGzd2ai2tW7fWiBEjVLVqVafWkdV06tRJnTp1kiTduHFDhw4d0ujRo9W2bVstWbJE3t7eprxnmzZt0n26j0JW2m5u3brV/v+bN29q9+7d+vjjjxUXF6cBAwY4sTJz/ec//1G+fPkUHR2tH374QQ0bNnR2SQ8lq/bjnbZu3WrKtiyj+fjjj+3rq81mU1RUlIYOHapOnTppw4YNypkzp5MrBDK2u4+J/vrrL/Xv31/Zs2dXmzZtNHHiRLm5uTm5StzJZrPpvffe086dO/XWW29pwIABypkzpw4fPqwvv/xSr7zyilauXKl8+fKlOK1PPvlEiYmJptf85ptvqlmzZqpTp47p75WeOOMtAzIMw7Rpe3l5qUCBAipQoIAKFiyop556SgMHDpSXl5fWrVtn2vu6ouDgYP3xxx+KjIx0GL59+3blyJFDRYoUcVJleBSGDRumxYsXa+zYsU4P3eA8OXLksG9TH3/8cT333HOaNWuWzpw54/Arb3rKmTOnfH19TZm22bLSdvP2elGgQAE99thjatSokV566SWtXbvW2aWZavny5apVq5aqV6+uxYsXO7uch5ZV+/FOBQoUkJeXl7PLMJ23t7e9rwsVKqTy5curb9++OnfunLZt2+bs8oAM7+5jokaNGqlJkyZavny5JClPnjxZIsTPTObMmaMtW7Zo9uzZ6tSpk0qWLKnHHntMzz77rObMmSMPDw/NnDkzVdPy9vZWnjx5zC04EyN4c7J27dppx44dWrlypUJDQ9WvXz+tXLlSO3bssJ9+e/nyZfXv31+1atVS2bJlFRwcrP79++v69euSpIiICJUpU0bTp09XtWrV1Lx5c9lstlTX4O5+68RHT09Pe02ffvqpWrZsqapVq2rNmjWKj4/XqFGjFBoaqnLlyikoKEjvv/++w+VUhw4dUvv27VWxYkU9//zzyR6kLF++XA0aNFBAQIAaNGiguXPnOtS6atUqNWrUSOXLl1etWrU0bNgwxcfHO0xjxYoVqlevnsqXL6/mzZvrzz//TPW8pqeAgAA99thj+u677xyGr1+/Xg0aNHA4c2Pp0qVq0qSJAgICVLFiRbVu3Vp79uxxeF10dLTee+89VaxYUdWqVdOIESMcfjVIaRq3L39p2LChqlWrph07dqS47kjSzJkzVa9ePfslNJMnT04S/k6fPl21a9dWQECA2rVrp2PHjklKuv4+TB2ZyfDhw7V48WJNmDBBL774on346dOn1atXLwUHB6ts2bKqXbu2Ro8ebV/HV6xYoeeff15Dhw5VlSpV9M4779g/v1u2bFHjxo1Vrlw51a9fXxs3brRPN6Xld3tbERYWZj+Ff+fOnWrfvr0qV66scuXKqUGDBlq9evU952nYsGEKDAzUX3/9JenW5/nNN99UYGCgypUrZw+UkLLHHntMzz//vP0S7CtXrujTTz9V9erVVaVKFbVv3z7J5/9uR48eVYcOHezbwmnTptnHZdZLTaW0bTellLd7V65cUd++fVW1alVVr179npex3mu/MXfuXFWqVMlhW2Sz2VS7dm19/fXXkqSNGzeqZcuWqlixov31P//88wPNv5ubm31fm9z2IDXvFx8fr+HDhys4OFhVqlRx2MbcduTIEXXt2lWVKlVSSEiIPvzwQ4fLRY4dO6bOnTurSpUqqlSpkjp37pzkcrr7rYP3cuTIEf3555+qWbOmXnjhBUVEROjo0aP28aGhoZo5c6a6d++uSpUqqVq1aho6dKgSEhLsbX744Qc1adJE5cuXV+vWrXX69Okk73O//eWDLte0cPV+vNudl5r269dPffr00dChQ1W1alUFBQVpwoQJOnLkiFq3bq2AgAA1adLE4dgsNfuTn3/+Wa+99poqVKig2rVra9y4cQ7HQA97jPSg7j5G9vf314QJE1S3bl2FhITo2LFjKe77pZTX6/j4eI0ePVq1atVSpUqV9OqrrzqcbZmYmKjRo0fr2WeftR8nLFq0yGEaN2/e1KhRo1S9enVVrFhR77zzjs6dO/fQy8AMKa0TEydOVNu2bdWrVy9VrlxZAwcOVHBwsCZNmuQwncWLFyskJEQJCQmp/r70MMdbSLs7Q/vkLkW+3/e6BzmuRuoZhqEFCxbopZdeUtmyZZOM9/Ly0rx589SzZ89kbyF197C7LzVNaT/4IJlDaGioTp06pUmTJmW6S9cJ3pxs4sSJqlSpkho0aKBly5bpk08+UYMGDVSpUiX7Drdfv37at2+fJk2apO+//15hYWFatWqVlixZYp9OYmKitmzZoiVLlmjYsGGyWlPXtRcvXtTw4cN148YNvfDCC/bhS5cuVfv27bVw4ULVqlVL4eHh2rBhg0aOHKnvv/9eI0eO1Pbt2/Xll19KuvXlp0OHDvL29tbSpUs1aNAg+7jblixZovDwcL333nv6z3/+o549e2rGjBkaM2aMJOnAgQPq37+/unfvru+//17Dhw/X6tWrk5w18s0332js2LFavny5PD091bNnzzQv9/TSoEEDhy+Q8fHx2rhxo8Nllz/88IOGDBmiLl266Ntvv9WcOXMUFxen/v37O0xr2bJlCgwM1Nq1a9W7d2/NmTPHft+w1E5jwYIF6t+/v7766itVrFgxxXVn8+bNmjZtmgYPHqwNGzboo48+0pdffqk1a9bYp3nq1Cnt2rVL06dP14IFCxQdHW2/XPnu9fdB68hMRo4cqblz56pLly5Jwo+3335bV65c0ezZs/Xdd9+pU6dO+uqrr7R582Z7m3///VdRUVFatWqVevXqJel/B9SffPKJ1q1bJz8/P/Xt21fXrl2TlPI24Pa24uOPP9Ynn3yis2fPqnPnzipfvrxWrlypVatWKSAgQJ988kmyB+Hh4eFavXq1Zs+erYCAAF2/fl2dOnVSnjx5tHjxYq1bt07169fXqFGjtH//flOWq6vx8/PTiRMndPXqVXXt2lUnTpzQtGnT9M0336hixYpq1aqV9u3bd8/XL1iwQE2bNtX69evVqlUrjR071mXOuEjNdlNK3XavZ8+e+uuvvzR16lTNnj1bP/30k06dOpXkPe+132jSpIlu3rypDRs22Nv++uuvunjxoho3bqz//ve/6t69uxo1aqS1a9fqm2++ka+vr/r06ZPkR6H7iY+P108//aTVq1erQYMG9uF3bw9S835Dhw7V+vXrNXLkSC1evFiRkZEO93c8e/asWrdurSeffFLLli3T1KlTdfXqVb322muKjY2VJH3wwQcqVKiQli9frqVLl8pqteq9995zqPlB1sFly5YpR44cql27tp5//nl5eHgkOett/PjxCgwM1Jo1a9SnTx8tWLDAfsb9rl271L17d7344otas2aNmjVrpunTpyf7PvfaXz7ock2NrNKPKVm/fr3c3Ny0YsUKdejQQZMnT9Zbb72lzp07a+nSpcqWLZsGDx4sSanan+zevVvdunVTlSpVtGLFCg0dOlSLFy/WlClT7O+ZHsdIaWEYho4dO6bRo0erYMGCqly5sn3cwoULNWHCBE2aNEnFixdPcd+fmvU6LCxMv/zyi8aMGaOVK1eqQYMGeuutt+z3MVq4cKG+++47jRs3Tt9//73atm2rQYMGOawzu3fvVkxMjBYuXKhp06bpjz/+UHh4+AMvA7Ok9hjjt99+U/78+bV69Wp17NhRL730ksPxqXTrB/uXXnpJ7u7uqf6+9DDHW0ibv/76S+vWrbvv7ZPu973uQY+rkTonT57UqVOnVKNGjXu2KVq0qP2Hh7RI7X43rZnDsmXLVLhwYXXq1ClD3h/9vgyYom3btkbfvn2THde3b1+jbdu292x79/j58+cbBw4ccJhGy5YtjbCwMMMwDGP79u2Gn5+fsXHjxhRrKlOmjFGxYkWjYsWKRvny5Q0/Pz+jbt26xooVKxzaNW3a1OG1q1atMn777TeHYT179jTat29vGIZhLFq0yKhYsaIRExNjH//DDz8Yfn5+xvbt2w3DMIzatWsbs2fPdpjGsmXLjPLlyxs3btwwfvjhB6NcuXLGX3/9ZR//119/Gf/8849hGIaxfPlyw8/Pz/j777/t4zds2GD4+fkZ586du++8p7e6desaEyZMMPbs2WP4+/sbkZGRhmEYxubNm40XX3zRoc2OHTuM1atXO7x+4cKFRqlSpRym9/777zu0adKkiTFw4EDDMIxUT+Pdd991aJPSujN79myjZs2axtGjR+3jf/vtN+PUqVOGYRjGhAkTjLJlyxpXrlyxj589e7YREBBg//vu9fdB6sgM6tata4SEhBgBAQHG66+/blStWtU4efKkffz169eNmTNnGqdPn3Z4XY0aNYxJkyYZhvG/dXj//v328bc/vz/88IN92P79+w0/Pz9j165dhmGkbvn5+fkZy5cvNwzDMI4fP27MmDHDsNls9vF///234efnZ/8c397OjB071ggKCjL27t1rb3v+/Hlj2rRpxtWrV+3Dbty4Yfj5+RkrV65M24JzYbc/48lZsmSJ4efnZ6xevdrw9/c3Ll686DC+TZs29s/NhAkTjLp169rH+fn5GeHh4Q7tq1SpYkyfPj3Z9plFWrabhpHydu/IkSOGn5+f8euvv9rHR0dHG+XKlbNPIzX7je7duxudOnWyj//ggw+MHj16GIZhGPv27TO+/vprhxq2bNli+Pn5Jfms33b7PW/vaytWrGj4+/sbAQEBRr9+/Yxr1645tLtze5DS+125csUoW7as8c0339jH37hxw6hRo4Z9fRo3bpzx0ksvOUwjNjbWCAgIsG8jqlSpYowePdqIj483DMMwoqKijO3btxuJiYmGYaS8Dibn5s2bRo0aNYwPPvjAPuzNN980goKCjBs3bhiGcat/3377bYfXvfzyy8ann35qGIZh9OrVy2jVqpXD+KFDhxp+fn72v1PaXz7Ick2Oq/fj3ceEd/4rU6aMw3HonfuXvn37GjVr1rS/x7Vr1ww/Pz9j7Nix9vZff/21UbZsWcMwUrc/6dWrl/Haa6851Pndd9/Zl2F6HSPda3t9ex7LlStnXwZly5Y1/P39jebNmxt//vmnQ7vhw4fb/07Nvj+l9frYsWOGn5+fsW/fPoc2ffr0sffD0KFDjcaNGxtnz561j//ll1/s27G7+8UwDOOzzz4zGjRocM95NkNq1qvUrBMTJkww/Pz8HL5XHDx40OHY6J9//jH8/PyMw4cPG4aR+u9LD3u8heTVrVvXKFu2rMNnyM/Pz2jZsqX9e8Sd3xlS2j8/6HE1Um/Xrl2Gn5+fsXXrVofhb775psNnt2HDhsaJEyccvtcbhpFk2J0ZRmr2uw+SORhGytvzjIqHK5jE3d39npd72mw2+6nrqdG6dWtt3rxZK1eu1LFjx/T333/r5MmTevrppx3aFS9ePMVphYaG6qOPPpIkWa1W5cqVS3nz5k3S7sknn3T4++WXX9avv/6qMWPG6NixY/rnn3909OhR+43cDx06pOLFiztct1+pUiX7/y9cuKDIyEiNHTtW48ePtw+32WyKi4vTyZMn7afXt2jRQsWKFVPNmjX13HPPqVy5cvecTx8fH0m3buDpDOXKldPjjz+u77//Xu3bt9f69euTnLURGBioI0eOaPLkyfrnn390/PhxHTx4MMn6cXf/5c6d2/7UtNRO4+5+S2ndeemll7R8+XK9+OKLeuaZZ1SjRg29+OKLeuyxx+zTyJcvn3LlymX/28fHJ8XlndY6MourV69q+vTp8vf3V5MmTfThhx9qwYIFcnd3l5eXl9q2bavvvvtOf/31l72Pzp07l2JfS3JYFreX9+0nAqZ1+T3xxBNq3ry55s2bp0OHDunff//VgQMHJMnh0pw//vhDv/32m3x9fR3ureXr66vWrVtr3bp12rdvn8Pr03IZe1Z25coVSdKJEydkGIbq1q3rMD4+Pv6+T0W8ex3x8fEx5SmKzpCa7aaU8nbv0KFDkqTy5cvbX5M/f349/vjjSaZ1v/3GK6+8orfffltRUVHKkSOHNm7cqAkTJkiSSpcurdy5c2v69On2GpL7LCVn1apVkiSLxaJs2bIpf/78yd5U+s7aUnq/o0eP6ubNmw7znC1bNpUpU8b+9759+3T48GGHfbAkxcXF6ciRI5KkXr16afjw4Vq4cKGCgoJUq1YtNW7c2OFs+bSug1u2bNG5c+cc+rJRo0b68ccf9e2336pp06aSpBIlSji8ztvb276tO3TokGrWrOkwvlKlSpo3b949l5nkuL9Mrg39+D939uOdx4R3GjNmjC5dunTPZVKsWDH7e+TIkUOSHD53Xl5e9j5Nzf4kuX6/8zYOyc3HgxwjpaRHjx72qz/c3NyUN2/eZB+ocOcxTmr2/Smt17fPfm7durVDm5s3b9q3V23atNHGjRv17LPPqnTp0qpZs6YaNWrkcMPzJ554wqHvc+fO7ZTj45TWq9QeY+TLl8/he4Wfn5/Kly+vVatWqVKlSvaz+Z955hlJqT9WSs/jLTh6/fXX7Zf/JSQk6Pjx4xo3bpzatGmjpUuXJvuae+2f8+XL91DH1UjZ7Qzg8uXLDsMHDx5s33bMnz/f4QzD1ErtfjetmUNmRvBmEh8fH8XExCQ77vLly8qdO3eqpmOz2fTmm2/q8OHDaty4sRo2bKiyZcvq008/TdI2W7ZsKU4vZ86cSVbw5Nx9E90BAwbo+++/V9OmTRUaGqp3331XM2fO1NmzZyXdOiC9eyN4Z7h4e1xYWFiyp7MWKVJEnp6emjdvnvbt26etW7dq69ateuutt9S0aVONGDHC3ja5A17DxAdSpOT2ZVOvvfaaNm3alGTHsnbtWvXr109NmjRR5cqV9frrr+vQoUMaMmSIQ7v7zVdqp3Fnv6Vm3fH19dXq1au1e/du/fLLL9q6davmzZun7t272y9XeZCnD6W1jsyiffv2qlatmiRpxIgR6tKliyZOnKhevXopNjZWbdu21Y0bN1S/fn01a9ZMAQEByT59MrmbVCd3GrdhGA+0/P7++2+1bt1aZcuWVY0aNfTCCy8ob968SU71z5Ejh6ZOnaoPP/xQQ4cO1eeffy7p1r10XnvtNfn6+io0NFQhISEqX768nn322TQtr6xs7969Kl68uDw8PJQrVy77/ZHudL9T9zPadi69pbTdlFLe7t2+H9z99j233W95hoSEKH/+/Fq3bp3y5MkjHx8fhYSESJJ27Nihzp07q06dOqpSpYqaNGmi69ev6913301xHlOzr5Uctwcpvd/teb57Xbh7f1u9enUNHDgwyXvd/hLbpk0b1a9fX1u2bNG2bds0YcIEffnll1q1apXy588vKe3r4O11/O5LHaVb92G6Hbzda1t3e/7u7k8PD48k7VNTW1qW6/24cj/e65gwZ86c9w3ekuuTe93iJDX7k9T8GJ0ex0gpyZcvX5qPkVOz709pvb49H19//XWSoO/2ci1evLg2bNigHTt26JdfftFPP/2kGTNmaMSIEWrWrJmkBzteM0NK61VqjzGSO1Z65ZVXNG7cOH3yySdau3atunTpIiltx5rpebwFR7lz53bo+xIlSih37txq3bq1fv3112Rfc6/P9sMeVyNljz/+uAoUKKCIiAiHJ5AXKlTI/v/7ZRb3++EqtfvdtGYOmRnBm0nKli2refPmKT4+3mEDHx8fr7/++su+o0jOnTeX3r9/v/7v//5P33zzjSpUqCDp1q8y//77b7K/6pvh4sWLWrJkicaNG+fwofznn3/sv3SWKlVKy5Yt04ULF+xP2/vvf/9rb5svXz75+vrqxIkTDhvk9evX64cfftCoUaO0ZcsW7dmzR++9957KlCmjbt266csvv9TUqVMdgreMpkGDBpo+fbqWL1+uxx9/PMmv+dOnT1eLFi3s9zuRpE2bNkm6tWO5+2biyXmQaaRm3VmzZo2uXLmiNm3aqEqVKurRo4f69++v9evXJ/vl6UFkhHU4vdz55SAkJERt27bV9OnTVb16dV25ckV79+7VL7/8Yv/Cc+nSJZ0/f/6hApMHWX6LFy9Wvnz5HG40f/vXqjtr8fPzU6VKlTRo0CB17dpVDRo0UL169bRu3TpdunRJ33//vf3Lwe0bdrtS+GOWyMhIbdq0SV27dpWfn5+uXr2qmzdv2n+Vl6T+/furVKlSatu2rRMrdZ6UtptSytu90qVLS7p1/6Tbj5SPiYnRv//+m6Za3Nzc1LRpU/3www/y8fHRyy+/bP8iMGvWLFWrVs3hPiLz58+315DeUnq/p556StmyZdOuXbvs85+QkKADBw7YfxQoWbKk1q9fb/9BS7q1Lerbt686duyokiVLavLkyerWrZuaN2+u5s2b6+zZs6pdu7Z27NjhsJ9PrfPnz2vLli1q3ry5Onbs6DBuzpw5Wr58uf0MxfspVaqUdu/e7TDszmOJB0U/Ok9q9iclSpRI8iCEuXPnat26dfc8S+ZO6XGc9aC2bt2a4r4/pfW6ZMmSkm6FlHee9Thu3DhZrVa9//77mjdvnvLly6dGjRqpZs2a6tOnjzp27Kj169fbg7fM4mGOMRo3bqyRI0dq9uzZOnfunP2p8ulxrOlKx6sZye0+TesZqKn5bOHhuLm5qX379po8ebJatWqlUqVKJWlz5swZSf/7seDq1av2cbcftpecB9nvpiZzyMx4uIJJWrRoIZvNpvfee0+7d+/WqVOntGPHDr3zzjtyd3dXixYt7G1z5sypU6dOKTIyUtKts1CioqJ04sQJ5c+fX+7u7vr222914sQJ7dmzRz179lR0dHSabgj8MHLlyiVvb29t2rTJfprvp59+qr1799pruH26+4cffqgDBw5ox44dGjZsmH0aFotFXbt21fz587VgwQL9+++/+uGHHzRo0CB5eXnJ09NTHh4emjx5subMmaMTJ07ov//9r3766ackl1lkNKVLl9aTTz6pzz//PNnLpYoUKaJdu3Zp7969+vfffzVnzhwtWLBAklLdhw8yjdSsO3FxcRo1apRWrVqlkydPaufOnfrtt9/StMzvXn8fpI7M6qOPPtJTTz2l3r17238dWrNmjU6dOqWdO3fqnXfe0c2bNx9qPlO7/HLkyKEjR47o4sWLKly4sCIjI7VlyxadOnVKGzZs0KBBgyQlv77Url1bjRs31qBBg3T58mUVLlxY169f13fffafTp09r69at+uCDD+75+qwsNjZW0dHRio6O1okTJ7Rx40Z16dJFxYoVU8eOHVWrVi2VLl1avXr10vbt23X8+HGNGDFCK1asSDZsyipS2m5KKW/3nnjiCdWvX19DhgzRr7/+qkOHDqX5Zvm33X6S2q+//urwJbZIkSI6ePCgdu7cqZMnT2r58uX22yWY8VlI6f1y5syptm3basKECdqwYYOOHDmigQMHOvwS3Lp1a125ckUfffSRDhw4oAMHDqhXr17as2eP/Pz8lDt3bv3000/q37+/9u/frxMnTmjx4sXy8PBIcmuH1FqzZo0SEhLsYfOd/9566y1ZrdYkD1lITqdOnXTgwAGNGjVKR48e1Zo1a+x9/jDoR+dJzf6kS5cu+uOPPzR+/HgdO3ZMW7Zs0ZQpU+yBekrS4zjrQRUuXFjS/ff9Ka3XJUuWVN26dTVw4EBt3rxZJ06c0IwZMzRt2jQ98cQTkm7dsmXIkCHatGmTTp06pZ9//ln79+/P8MfIyXmYYwxvb289//zzmjJlip577jn7pYnpcazpyserj8qdx0RRUVHauXOnhg8froIFCyo4ODhN00rNZwsPr0uXLqpbt65at26tqVOn6sCBAzp58qQ2b96sTp06afny5apevboKFiyookWLau7cuTpy5Ih+//13jR8//p4/bDzIfjc1mYN067vnsWPHMuxTm++F4M0kvr6+WrJkiXx8fOxPMvrggw+UP39+ffPNNw6nbd4+Jf6ll15SYmKimjZtquvXr9t/xRk5cqQ2b96shg0b6v3331ehQoXUoUOHdPkVODU8PDw0fvx4HTp0SE2aNFGXLl10/fp1ffDBB/r77791/fp15ciRQ3PnzpWHh4datWqlPn36JDmrr1OnTurXr58WLFighg0batiwYXr11Vftv1DWqFFDw4YN07Jly9S4cWN17txZTz75pMaOHftI5vNhNGjQQFevXk32F+ZPP/1U+fPnV9u2bdWyZUv9+OOP9qdMpfZR9w8yjUKFCqW47rRs2VLdu3fXlClT1KBBA/Xs2VMhISFpehLY3evvg9SRWXl5eSk8PFyXLl3S5MmTFRYWpnnz5qlBgwYKCwtTYGCgGjdunOp+Tk5ql1+nTp20YMEChYWFqX379mrQoIH69Omjxo0b68svv9QHH3ygokWL3rOWTz75RDdv3tTQoUNVv359de7cWSNHjlSDBg00fPhwtWjRQoGBgQ81L65o1qxZCgkJUUhIiBo0aKBRo0bpueee08KFC5UzZ065ublp1qxZKleunHr27KmXXnpJv/32myZNmpTmg1BXc7/tppS67d6oUaP07LPPqlevXmrTpo2eeeaZBwodihcvrgoVKqhMmTIOgWiPHj1UsWJF+20Pli5dquHDh8vLy8uUz0Jq3u/DDz9U69atNWTIELVo0UKGYTg8Yfnxxx/XggULdO3aNbVq1Upt27aVh4eH5s2bJ19fX7m7u2vGjBmyWq3q0KGDGjVqpF9//VXTp0+3f8lPqxUrVqhGjRrJ3gfpiSeeUL169bRmzRr70zjvpXTp0poxY4YiIiL00ksvac6cOXrrrbceqKY70Y/Ok5r9SenSpTV58mT99NNPaty4sQYPHqz27dvr7bffTtV7pMdx1oMKCAhIcd+fmvV63LhxeuGFFzRgwAA1bNhQq1at0rBhw+w/BLz33ntq0aKFhg4dqhdffFEDBgxQq1at9Oabb5o6f2Z42GOM5s2b68aNG2revLl9WHoca7ry8eqjcucx0bPPPqvu3bvbw5rs2bOnaVqp+Wzh4VmtVn3xxRcaNmyYdu7cqU6dOtl/1PT19dWCBQs0fPhwWSwWhYeH6+rVq3r55Zc1YMAAffDBB/e8zcCD7HdTkzlIUrt27fTTTz+pU6dOpi0XM1gMztUEAABZmGEYqlevnt56660k90IEAGQcK1as0MSJE7Vp06Z7fukH8Oh99NFHunDhgmbNmuXsUjIk7vEGAACypJs3b2rz5s3avn27YmNj73nZKwDAufbu3at//vlHEyZMUNu2bQndgAwiPj5ehw4d0r59+1SxYkVnl5NhEbwBAIAsycPDQ0OHDpUkjR492iVu3gsAruiPP/5QeHi46tSpozfeeMPZ5QD4/w4cOKD27dvb72+M5HGpKQAAAAAAAGACztEFAAAAAAAATEDwBgAAAAAAAJiA4A0AAAAAAAAwAcEbAAAAAAAAYAKCNwAAADh40Gdv8cwuAAAARwRvAAAAWcShQ4fUq1cv1axZU+XKlVNISIh69uypAwcO2Nv8/vvv6tatW5qnvWnTJvXt2zc9ywUAAMj03J1dAAAAAMx3+PBhvfbaa6pYsaL69++vfPnyKTIyUgsWLNCrr76qefPmqWLFilq6dKmOHDmS5unPmTMn/YsGAADI5AjeAAAAsoDZs2crb968mjFjhtzd/3cIWK9ePdWvX19TpkzR9OnTnVghAACA6+FSUwAAgCzg3LlzMgxDNpvNYXiOHDn08ccfq0GDBurXr59WrlypU6dOyd/fXytWrJAknTx5Un369FFISIjKli2r4OBg9enTRxcvXpQktWvXTjt27NCOHTvk7++viIgISdKlS5c0YMAA1ahRQ+XLl9err76qbdu2PdoZBwAAcCKLwV1wAQAAXN7ChQs1ePBglS1bVq+88oqqV6+up59+WhaLxd7m33//1dChQ7Vv3z5NmjRJTzzxhLJnz65GjRopb968euutt+Tt7a3du3dr0qRJeuWVVzRkyBD9/fff6t27tyRp4MCBeuaZZ+Th4aFXX31V586dU8+ePVWwYEEtX75cmzZt0ldffaXg4GBnLQoAAIBHhktNAQAAsoDWrVsrOjpaM2fO1JAhQyRJefPmVUhIiNq3b6+AgAA98cQT8vX1laenpypWrChJ2r9/vwoXLqxRo0bp8ccflyRVr15df/75p3bs2CFJeuaZZ5QrVy5Jsr/um2++0YEDB/TNN9+oQoUKkqTatWurXbt2GjNmjJYvX/4I5x4AAMA5uNQUAAAgi3j//ff1888/6/PPP1eLFi2UK1curV271v5wheSULl1aCxcuVNGiRXXs2DFt2bJFM2fO1D///KP4+Ph7vte2bdtUoEABlS1bVgkJCUpISFBiYqLq1q2r//73v7p8+bJZswkAAJBhcMYbAABAFpI7d241btxYjRs3liTt27dPvXv31ujRo9WkSZNkXzN79mxNnTpVly5dUv78+VWuXDllz55dV65cuef7XLp0SdHR0Spbtmyy46Ojo5U7d+6HnyEAAIAMjOANAADAxZ09e1avvPKK3n//fbVs2dJhXJkyZdSrVy+9++67OnHiRJLXrl27ViNHjlTv3r3VvHlz+fr6Srp19tyePXvu+Z7e3t4qXry4xowZk+z4YsWKPcQcAQAAZA5cagoAAODi8ufPL3d3dy1cuFBxcXFJxv/zzz/Kli2bnnzySVmtjoeHv//+u3x8fNSlSxd76Hbt2jX9/vvvDk9Ivft1QUFBOnPmjPLly6fy5cvb//3yyy/66quv5ObmZsKcAgAAZCwEbwAAAC7Ozc1NgwYN0qFDh/TKK69o0aJF2rFjh7Zs2aLhw4dr/Pjxeu+995Q7d275+Pjo3Llz2rJli6KiohQQEKCYmBiNHDlSERERWrt2rdq0aaNz587p+vXr9vfw8fHR0aNHtW3bNl2+fFnNmzfXY489po4dO2rlypXavn27xo4dq/Hjx6tgwYLy8PBw4hIBAAB4NCyGYRjOLgIAAADm27t3r2bOnKnff/9dFy5ckKenp8qUKaN27drphRdekCQdOnRI77//vk6cOKEePXqoa9eumjhxopYvX66LFy+qUKFCevbZZ+Xn56dPP/1U69evV4kSJbR9+3aFhYUpOjpaI0aMUJMmTXT+/Hl9/vnn+umnn3TlyhUVLVpULVq0UKdOnZKcIQcAAOCKCN4AAAAAAAAAE/BTIwAAAAAAAGACgjcAAAAAAADABARvAAAAAAAAgAkI3gAAAAAAAAATELwBAAAAAAAAJiB4AwAAAAAAAExA8AYAAAAAAACYgOANAAAAAAAAMAHBGwAAAAAAAGACgjcAAAAAAADABARvAAAAAAAAgAn+H01inI61mKXVAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# total amount/sales from top 10 states\n",
    "\n",
    "sales_gen = df.groupby(['State'],as_index=False)['Amount'].sum().sort_values(by='Amount',ascending = False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(15,5)})\n",
    "sns.barplot(x='State', y='Amount', data = sales_gen)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0920b75f-134d-4ec2-8ff0-8427d4835288",
   "metadata": {},
   "source": [
    "From above graphs we can see that most of the orders & total sales/amount are from Uttar Pradesh, Maharashtra and Karnataka respectively\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8b319dc-708b-44a1-967f-3b24beb5ef87",
   "metadata": {},
   "source": [
    "### Marital Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "84470bf6-da56-4313-9604-6c57a330e242",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(data = df, x='Marital_Status' )\n",
    "\n",
    "sns.set(rc={'figure.figsize':(5,7)})\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d2fad904-593d-4573-87a9-db25273d7bee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Marital_Status', ylabel='Amount'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Marital_Status','Gender'],as_index=False)['Amount'].sum().sort_values(by='Amount',ascending = False)\n",
    "sns.set(rc={'figure.figsize':(6,5)})\n",
    "sns.barplot(x='Marital_Status', y='Amount', hue = 'Gender', data= sales_state)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ffe24ed-f1af-49b7-835e-06fb7aae05c4",
   "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": "3cfd37db-3779-4463-9fbc-f20229aeeb29",
   "metadata": {},
   "source": [
    "### Occupation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e397a1a5-e22c-444c-b8e4-17d3b0f70360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABmUAAAKrCAYAAAAAtIlhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAACxx0lEQVR4nOzdd5iV1b0+7mf2DL2DUkQRxSgxig0QbFE0dhNbijUqGhQN9q7BhhrBClgRVEI0msSWeBKPxpxEY0MTOyoqUVFEQpPOlN8fHuf3nTMWxGHPAPd9XV7MrLXeNZ+1X/fM3vPMet+SqqqqqgAAAAAAALBCFeq7AAAAAAAAgNWBUAYAAAAAAKAIhDIAAAAAAABFIJQBAAAAAAAoAqEMAAAAAABAEQhlAAAAAAAAikAoAwAAAAAAUARCGQAAAAAAgCIoq+8CVlZVVVWprKyq7zIAAAAAAIB6ViiUpKSk5CvHCWWWU2VlVWbOnF/fZQAAAAAAAPWsffsWKS396lDG5csAAAAAAACKQCgDAAAAAABQBEIZAAAAAACAIhDKAAAAAAAAFIFQBgAAAAAAoAiEMgAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARCGUAAAAAAACKQCgDAAAAAABQBEIZAAAAAACAIhDKAAAAAAAAFIFQBgAAAAAAoAjK6rsAAAAAYOU1ffpHOfzwH+fSS0dkyy17V7cfd9zAvPTSC7XGjxlzR3r23DhJ8tZbk3P99dfl1VdfTuPGjdKnT78MHjwk7dt3qDH/6NHX5rnnnsmSJUuz1VZ9MnjwkKyzTrcVvzgAgDpWUlVVVVXfRayMKioqM3Pm/PouAwAAAOrNRx9Ny6mn/jxTpryT6667sTqUqaqqym677Zh99tk3O+20S41jevTYIM2aNcvMmf/J4Yf/OB07dspRR/0sixcvzg03jEzr1q1z8823p6ysLAsXLszAgYempKQkRx99bBo1apzbbx+T6dM/yvjxd6d16zb1sWwAgFrat2+R0tKvvjiZnTIAAADA11JZWZk//emPGT36mnzen3pOnfp+FiyYn/79t80mm2z6uXP8/e//k9mzZ+emm25L165rJ0latmyVU0/9eV566YVsscVW+etfH8277/47d9zxm6y/fo8kn4Y6P/zh9/OXvzySffc9YIWtEQBgRXBPGQAAAOBreeutNzNixGXZffe9cv75F9bqf/PN15Mk3/rWhl84x5IlS5IkzZu3qG5r0+bTnS9z585Jkmy//Y654YZbqwOZJGnUqFGN4wEAViYNKpS56aabcthhh9Vomz59ek455ZT07t07W2+9dU499dTMnDmzxpgJEyZk5513Tq9evXLwwQfn1VdfrdH//vvvZ9CgQdlyyy2z3Xbb5ZprrklFRcUKXw8AAACsijp16py77ro3P//5KWnSpGmt/jfffCPNmjXP6NHXZq+9ds6AAdvktNOG5N13p1SPGTBgl3TosEauvvqKzJgxIx98MDWjR1+bDh3WSO/efZMkLVu2zKabbpYkWbp0aSZPfjOXXDI0bdu2zYAB3yvKWgEA6lKDCWUmTJiQa665pkbbkiVLctRRR+WDDz7IHXfckZtvvjmTJk3KmWeeWT3m3nvvzRVXXJETTzwxv//977P22mvnyCOPrA5uli5dmoEDByZJ7rrrrlxwwQW58847M3r06KKtDQAAAFYlrVu3SceOnb6wf/LkN7Jw4YK0atUql146ImeeeV7ef/+9DB58TGbM+DhJ0qHDGjn99LPzxBN/y7777p4f/egHmTz5zYwYcV1atGhZa84zzzw5RxxxUJ5/fmIGDz4xa6yxxgpbHwDAilLvocxHH32UY489NiNGjEj37t1r9P3hD3/I1KlTM2rUqGy88cbZbLPNctZZZ+Wdd97JvHnzkiQ33nhjDj300Hz/+9/PBhtskEsvvTTNmjXLPffckyT585//nA8++CBXXHFFNtxww+yyyy455ZRTcvvtt9vqDAAAACvAMccMzqhRN+fnPz8lm222RXbbbc9ceeXIzJ8/L3fffWeS5OGH/5Rzzjk92223Q666alQuv/zKrL9+j5xyygn597+n1Jrzpz89Otdcc312332vXHrphfnDH+4r7qIAAOpAWX0X8Morr6RRo0Z54IEHMnr06EydOrW67/HHH0+/fv1q/PXL9ttvn0ceeSRJ8p///CdTpkxJ//79q/vLysrSu3fvPPvssxk0aFAmTpyY73znO9XXpU2Sfv36Zd68eXnttdey2WabLXftZWX1nmkBAABAvSotLVT/+9n75G9/u2etceuu2y3du6+Xt99+M2VlhYwbd3M23bRXhg37ZfWYfv365yc/OSBjxtyQyy4bXuP4rbba8n/H9MtHH32YO+4Yl3333X9FLQsAYIWo91BmwIABGTBgwOf2vfPOO+ndu3dGjx6d++67L+Xl5dluu+1y+umnp3Xr1pk2bVqSpEuXLjWO69ixYyZNmpQkmTZtWjp37lyrP0k+/PDD5Q5lCoWStGvX4qsHAgAAwCqsVaum1f+2a9ci5eXlefDBB9O9e/dsscUWNcYuXbokHTuumXbtWmTatA+z2267/p/31i2y2Wa98uabb6ZduxZ58cUX8/7772fPPfesMc9mm/XKSy+96H05ALDSqfdQ5svMmzcv9913X/r3758rr7wyc+bMyWWXXZbBgwdn/PjxWbhwYZKkcePGNY5r0qRJFi9enCRZtGhRWrduXas/SfWY5VFZWZW5cxcs9/EAAACwKvjkk0XV/86aNT9Jct11I7PGGmvm5pvHVo+bNOm1vPvuuznkkMMza9b8rLtu9zzzzMTMnDkvJSUlST59n/7SSy+ne/f1MmvW/Dz88KMZO3ZM1l9/o3Tq9OkfXFZUVOTxx/+RDTb4VvXXAwCob61bN6veQfxlGnQoU1ZWlubNm+fKK69Mo0aNkiRt2rTJD3/4w7z00ktp2vTTv8b5v/eGWbx4cZo1a5Ykadq06ef2J0nz5s2/UX3l5ZXf6HgAAABY2VVUVFb/+9n75COPPCbDhl2QoUPPy+6775lp06ZlzJgbs8EGG2bXXfdKeXlljj762Jx99mk5++wzsvfeP8jSpUvym9/8Oh9/PD2/+MUlKS+vzD777Jff//63OeWUE3PUUcekrKxR7r33nrz11pu5+urR3pcDACudBh3KdO7cOVVVVdWBTJJ861vfSpK8//772XrrrZMk06dPT48eParHTJ8+PZ06daqe44033qgx7/Tp05OkegwAAABQd/bYY+80btwkv/71HTn77NPStGmz7LDDjjn22BNSWlqaJNluu+9m+PBrc/vtY3LOOaenefPm2Xjj7+SWW+7IBht8+t6/ffsOuf76MbnhhpEZPvyyLFy4IBtvvEmuu+6mbLbZ5vW4QgCA5dOgQ5k+ffrkjjvuyKJFi6p3xXwWsKy77rrp0KFD1ltvvTz99NPp379/kqS8vDwTJ07MwQcfXD3Hfffdl3nz5qVly5ZJkqeeeiotWrRIz561bzwIAAAALLstt+ydxx+fWKt9552/l513/t6XHtuv3zbp12+bLx3TtevaueSSX36jGgEAGoqvvsBZPfrJT36S0tLSnHrqqXnzzTfz3HPP5bzzzsvWW2+d73znO0mSo446KuPGjcu9996byZMn55xzzsmiRYty4IEHJkl22WWXrLnmmjnppJMyadKkPPLII7nqqqty1FFH1boXDQAAAAAAwIrSoHfKtG/fPhMmTMhll12WH/7wh2ncuHF22WWXnHXWWdVjfvSjH+WTTz7JNddck9mzZ2eTTTbJuHHj0r59+yRJkyZNMmbMmFx44YX50Y9+lDZt2uTggw/O4MGD62tZAAAAsMIUCiUpFErqu4xVQmVlVSorq+q7DABgFVJSVVXl1cVyqKiozMyZ8+u7DAAAAKhWKJSkbdvmKS1t0BfGWGlUVFRm9uwFghkA4Cu1b99imV6DNeidMgAAAMCyKxRKUlpayOg7n8jU6XPqu5yVWteObXL8QdumUCgRygAAdUYoAwAAAKuYqdPnZMrUWfVdBgAA/4f9zAAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARCGUAAAAAAACKQCgDAAAAAABQBEIZAAAAAACAIhDKAAAAAAAAFIFQBgAAAAAAoAiEMgAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARCGUAAAAAAACKQCgDAAAAAABQBEIZAAAAAACAIhDKAAAAAAAAFIFQBgAAAAAAoAiEMgAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARCGUAAAAAAACKQCgDAAAAAABQBEIZAAAAAACAIhDKAAAAAAAAFIFQBgAAAAAAoAiEMgAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARCGUAAAAAAACKQCgDAAAAAABQBEIZAAAAAACAIhDKAAAAAAAAFIFQBgAAAAAAoAiEMgAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARCGUAAAAAAACKQCgDAAAAAABQBEIZAAAAAACAIhDKAAAAAAAAFIFQBgAAAAAAoAiEMgAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARCGUAAAAAAACKQCgDAAAAAABQBEIZAAAAAACAIhDKAAAAAAAAFIFQZhUzffpH2X33HfP88xO/cMzdd9+Z7bbrnQ8//KBG+4IFC3Lllb/M97+/W773ve1z2mlD8u67U2qMWbhwYUaPvjYHHrhPvve9HTJo0JGZOPGZFbEUAAAAAABYpQhlViEffTQtp5xyQubNm/eFY95999+56aZRn9t34YXn5rHHHsmxx56Q8867MB9//HF+/vNjM3fu3Ooxw4dfmnvvvSc/+tHBufTSK9KxY6eceurP88orL9f5egAAAAAAYFUilFkFVFZW5qGHHsxRRx2SmTNnfuG4ioqKXHrphWnTpm2tvpdffjFPPPH3nHvuBdlzz33y3e8OyDXXXJ8FC+bn3nvvSZIsXrwojz76cH7840Pyox8dlD59+mXo0EvSocMauf/+362o5QEAAAAAwCpBKLMKeOutNzNixGXZffe9cv75F37huDvvHJ+ZM/+TQw89olbf008/mWbNmqVv337Vbe3atcvmm2+Zp556IkmydGl5Kisr06JFi+oxZWVladGiRebMmV1n6wEAAAAAgFWRUGYV0KlT59x11735+c9PSZMmTT93zNtvv5WxY2/J2Wf/Ik2b1h7z739PyVprdU1paWmN9q5d18m77/47SdKyZcvsscfeueeeu/Lyyy/mk08+yZ13/irvvPN2dt11z7pfGAAAAAAArELK6rsAvrnWrdukdes2X9hfXl6eSy4Zmn32+UG22GKrfPjhB7XGzJs3L82bt6jV3rx588yfP7/680GDjs/kyW/m2GOPqm47+uhjs/PO3/uGqwAAAAAAgFWbUGY1cMcdYzNv3ic59tiff+GYqqrKL+wrFD7dUDVr1swcc8xPU1ZWlvPPvyhrrtkxTz31j4wbd0uaNm2an/zk0DqvHQAAAAAAVhVCmVXcG29Myvjx4zJ8+LVp1KhRyss/vS9MklRWVqaioiKlpaVp0aJlZs6cWev4BQvmp0WLlkmSBx+8L9Onf5Q77/x91lmnW5Jkyy17J6nKTTeNzh577J02bdoWa2kAAAAAALBSEcqs4v7+9//J0qVLc9JJg2v1/fjH+2bzzbfMqFE3p1u3dfPMM0+lsrKyemdMkrz//ntZd93uSZJp0z5Mu3btqwOZz2y22Zb59a/H5/333xfKAAAAAADAFxDKrOJ+8IP9s+2229doe+KJv2fcuFty+eVXpVu3TwOWvn375Y47xubpp59M//7bJklmzZqVF174Zw477Mgkybrrds+DD96Xd9+dkm7dulfP99JLL6RQKKRz587FWRQAAAAAAKyEhDKruDXWWDNrrLFmjba3334rSdKjxwbp0mWtJMnmm2+ZLbbYKhdddH4GDx6S1q3bZOzYm9OyZavsu++BSZK99/5Bfve7u3PqqSfmqKOOSceOnfLss0/nzjvHZ//9f5QOHdYo7uIAAAAAAGAlIpSh2rBhwzNq1NUZPfraVFVVZtNNN8vFF1+W1q1bJ0latGiZ66+/NTfeODKjR1+TRYsWZe21u+XUU8/KPvvsW7/FAwAAAABAA1dSVVVVVd9FrIwqKiozc+b8+i4DAAAAqpWVFdKuXYucc+1DmTJ1Vn2Xs1Lr3rVdLj1xz8yaNT/l5ZX1XQ4A0MC1b98ipaWFrxzXoHbK3HTTTXn88cczfvz4z+0/77zz8o9//CN/+ctfqtsqKyszatSo3HPPPfnkk0/Sp0+f/OIXv8g666xTPea1117LsGHD8vLLL6d9+/Y54ogjcvjhh6/w9SRJoVCSQqGkKF9rVVdZWZXKShkiAAAAAAArpwYTykyYMCHXXHNNevfu/bn9jzzySO6555507dq1Rvv111+fX//617n88svTuXPnDB8+PEcffXQefPDBNG7cOLNmzcqRRx6ZAQMG5MILL8y//vWvXHjhhWnRokUOOOCAFbqmQqEkbds2X6Z0jK9WUVGZ2bMXCGYAAAAAAFgp1Xso89FHH2Xo0KF5+umn0717988dM3369Jx//vnp27dvpk6dWt2+ZMmSjB07Nqeddlp23HHHJMnVV1+d7bffPg8//HD23nvv3H333WnUqFEuuuiilJWVpUePHvn3v/+dm2++uSihTGlpIaPvfCJTp89ZoV9rVde1Y5scf9C2KRRKhDIAAAAAAKyU6j2UeeWVV9KoUaM88MADGT16dI3QJUmqqqpy1lln5Qc/+EFatGiRe++9t7pv0qRJmT9/fvr371/d1rp162y88cZ59tlns/fee2fixInp27dvysr+/6X269cvN910U2bMmJE11lhjuWsvK/vyHTCf7ZCZOn2Oa/nWEbuOAAAAvpj3THXPYwoA1KV6D2UGDBiQAQMGfGH/bbfdlo8//jg33nhjbrrpphp906ZNS5J06dKlRnvHjh2r+6ZNm5YNN9ywVn+SfPjhh8sdyhQKJWnXrsVyHcvya926WX2XAAAAwGrE+1AAoC7VeyjzZSZNmpRRo0ZlwoQJady4ca3+hQsXJkmtviZNmmTOnE8vF7Zo0aLP7U+SxYsXL3dtlZVVmTt3wZeOKS0tePFWx+bOXZiKisr6LgMAAKBB8j607nkfCgAsi9atmy3TDtsGG8osXrw4p512Wo477rj07Nnzc8c0bdo0yaf3lvns48+ObdasWfWYJUuW1Jo7SZo3b/6Naiwv96Ks2CoqKj3uAAAAFI33oQBAXWqwocwLL7yQN998M6NGjcro0aOTJEuXLk15eXm22GKL3HLLLdWXLZs+fXq6detWfez06dOz0UYbJUk6d+6c6dOn15j7s887depUjKUAAAAAAAA03FCmV69eefjhh2u0jR8/Pg8//HDGjx+fTp06pVAopGXLlnn66aerQ5m5c+fm1VdfzaGHHpok6dOnT+66665UVFSktLQ0SfLUU09lvfXWS4cOHYq7KAAAAAAAYLXVYEOZpk2bZt11163R1qZNm5SVldVoP/TQQzNixIi0b98+Xbt2zfDhw9O5c+fsuuuuSZIDDjggY8aMybnnnpujjz46L774Ym677bZceOGFRV0PAAAAAACwemuwocyyGjJkSMrLy3Peeedl0aJF6dOnT2699dY0atQoSdKhQ4eMGTMmw4YNy3777Zc111wzZ5xxRvbbb796rhwAAAAAAFidlFRVVVXVdxEro4qKysycOf9Lx5SVFdKuXYucc+1DmTJ1VpEqWzV179oul564Z2bNmu8GiwAAAF/A+9C6430oAPB1tG/fIqWlha8c99UjAAAAAAAA+MaEMgAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARCGUAAAAAAACKQCgDAAAAAABQBEIZAAAAAACAIhDKAAAAAAAAFIFQBgAAAAAAoAiEMgAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARCGUAAAAAVkHTp3+U3XffMc8/P7FG+z/+8XiOPvrw7LLLdjnggL1z6603ZenSpV84z3nnnZFhwy6o1V5eXp5bbrkh+++/V3beedsMHnx0Xnnl5bpeBgCsUoQyAAAAAKuYjz6allNOOSHz5s2r0f7MM0/lrLNOyfrr98hll12Zgw8+LHfdNSFXXXVFrTkqKytz7bVX5q9//cvnfo1Ro67Ob34zIQcffFguvPCylJaW5qSTBuf9999bIWsCgFWBUAYAAABgFVFZWZmHHnowRx11SGbOnFmrf/z4cdloo54555yh6dNn6xxwwI/zk58ckoceeiALFy6sHjd58psZMuTYPPDA79OkSZNa83z00bTce+9vc/zxJ+XAA3+S7bbbIVdeOTKtW7fOhAm3r9A1AsDKTCgDAAAAsIp46603M2LEZdl9971y/vkX1uo/66zzc/75F9Voa9SoUSorK1NeXl7ddsklQ1NRUZGbbrot7dq1rzXPc889m4qKiuyww47VbY0bN84222yfJ598ou4WBACrmLL6LgAAAACAutGpU+fcdde96dixU617ySRJ165rV388f/68TJz4TO6881fZZZfd0qpVq+q+88+/KD16bPCFX2fKlHfSvHmLdOiwRo32tddeOzNmfJwFCxakefPmdbAiAFi1CGUAAAAAVhGtW7dJ69ZtvnLcjBkzsu++uydJ1lqra372s8E1+r8skEk+DXRatGhRq71580/bFiyYL5QBgM/h8mUAAAAAq5kmTZrk2mtvyEUXXZ7GjRtn0KAj8/HH05f5+MrKqi/tLykp+aYlAsAqSSgDAAAAsJpp1apVttqqTwYM2CXDh1+bWbNm5g9/uH+Zj2/ZsmUWLJhfq33+/HnV/QBAbUIZAAAAgNVARUVFHn30v/PGG5NqtHfpslZat26dGTM+Xua5unVbN/Pnz8+sWbNqtL///vvp3LlLmjRpWic1A8CqRigDAAAAsBooLS3NjTeOyo03jqrR/vrrkzJnzpz06PGtZZ6rT5+tkyR//euj1W1LlizJP/7x9/Tp069uCgaAVVBZfRcAAAAAQHEcddQxGTbsgowYcVl23HHnfPDB1Nx6601Zf/0e2WuvfZZ5ns6du2SPPfbOyJFXZfHiRVlnnXXzm99MyLx583LIIYevwBUAwMpNKAMAAACwmthjj73TtGnT/OpXt+dPf/pjmjVrnh122DGDBp3wtS85dvrp56RVq1aZMOGOLFy4IBtt9O1cffXorL32OiuoegBY+QllAAAAAFZBW27ZO48/PrFW+0477ZKddtplmef57W8f/Nz2xo0bZ8iQUzNkyKnLXSMArG6EMgAAAABFUiiUpFAoqe8yVnqVlVWprKyq7zIA4GsTygAAAAAUQaFQkrZtm6e0tFDfpaz0KioqM3v2AsEMACsdoQwAAABAERQKJSktLWT0nU9k6vQ59V3OSqtrxzY5/qBtUyiUCGUAWOkIZQAAAACKaOr0OZkydVZ9lwEA1AP7ZQEAAAAAAIpAKAMAAAAAAFAEQhkAAAAAAIAiEMoAAAAAAAAUgVAGAAAAAACgCIQyAAAAAAAARSCUAQAAAAAAKAKhDAAAAAAAQBEIZQAAAAAAAIpAKAMAAAAAAFAEQhkAAAAAAIAiEMoAAAAAAAAUgVAGAAAAAACgCIQyAAAAAAAARSCUAQAAAAAAKAKhDAAAAAAAQBEIZQAAAAAAAIpAKAMAAAAAAFAEQhkAAAAAAIAiEMoAAAAAAAAUgVAGAAAAAACgCIQyAAAAAAAARSCUAQAAAAAAKAKhDAAAAAAAQBEIZQAAAAAAAIpAKAMAAAAAAFAEQhkAAAAAAIAiEMoAAAAAAAAUgVAGAAAAAACgCIQyAAAAAAAARSCUAQAAAAAAKAKhDAAAAAAAQBEIZQAAAAAAAIqgQYUyN910Uw477LAabX/5y19ywAEHZIsttsiAAQPyy1/+MosWLaruX7x4cS688ML0798/W2yxRU499dTMnDmzxhxPPvlk9t9//2y22WbZfffd88c//rEo6wEAAAAAAPhMgwllJkyYkGuuuaZG28SJE3PCCSfke9/7Xu69994MHTo0Dz30UC688MLqMRdccEEef/zxjBw5MrfffnvefvvtDBkypLr/rbfeyqBBg7L99tvn97//fX74wx/mjDPOyJNPPlmspQEAAAAAAKSsvgv46KOPMnTo0Dz99NPp3r17jb677rorW2+9dY499tgkSffu3XPyySfnvPPOy4UXXphZs2blvvvuy4033pjevXsnSa666qrsvvvu+ec//5ktttgit99+ezbaaKOcfPLJSZIePXrk1VdfzZgxY9K/f/+irhUAAAAAAFh91Xso88orr6RRo0Z54IEHMnr06EydOrW676ijjkqhUHMzT6FQyNKlSzNv3rw899xzSZJ+/fpV96+33nrp1KlTnn322WyxxRaZOHFidtlllxpz9OvXL8OGDUtVVVVKSkqWu/aysi/faFRa2mA2Iq0yPKYAAABfzHumuleXj6nzU7c8ngCsjOo9lBkwYEAGDBjwuX0bb7xxjc+XLl2a2267LZtssknat2+fjz76KO3atUuTJk1qjOvYsWOmTZuWJJk2bVo6d+5cq3/hwoWZNWtW2rdvv1x1FwoladeuxXIdy/Jr3bpZfZcAAADAasT70IbLuQFgZVTvocyyKi8vzxlnnJE333wzEyZMSJIsXLgwjRs3rjW2SZMmWbx4cZJk0aJFtcZ89vmSJUuWu57KyqrMnbvgS8eUlha8QKhjc+cuTEVFZX2XAQAA0CB5H1r36vJ9qPNTt/yOAICGpHXrZsu0i3OlCGXmzZuXk046Kc8880xGjRqVXr16JUmaNm36ucHK4sWL06zZpy9ymjRpUmvMZ59/NmZ5lZf7wV9sFRWVHncAAACKxvvQhsu5AWBl1OBDmenTp+eYY47J1KlTc+utt6ZPnz7VfZ07d87s2bOzZMmSGrthpk+fnk6dOiVJunTpkunTp9eas3nz5mnVqlVxFgEAAAAAAKz2GvQd0ebMmZOf/vSnmTlzZiZMmFAjkEmSrbbaKpWVlXnuueeq295555189NFH1WN79+6dZ555psZxTz31VLbccssUCg16+QAAAAAAwCqkQacSl112Wd57770MHz487du3z8cff1z9X0VFRTp16pS99tor5513Xp5++um8+OKLOeWUU9K3b99svvnmSZLDDjssL774YkaMGJG33norY8eOzZ/+9KccffTR9bs4AAAAAABgtdJgL19WUVGRhx56KEuXLs1Pf/rTWv2PPvpo1l577Vx88cW59NJLc8IJJyRJdthhh5x33nnV4771rW/l+uuvz/Dhw3P77bdn7bXXzvDhw9O/f/+irQUAAAAAAKBBhTKXX3559celpaV58cUXv/KY5s2b55JLLskll1zyhWN22GGH7LDDDnVSIwAAAAAAwPJo0JcvAwAAAAAAWFUIZQAAAAAAAIqgQV2+DAAAoD5Nn/5RDj/8x7n00hHZcsvey9z3j388nrFjb86UKW+nTZu22XPPfXL44UelUaNGSZIDD9wn06Z9+Llfs0uXtXLPPQ+smAUBAAANilAGAAAgyUcfTcupp/488+bN+1p9zzzzVM4665TsvvteGTTo+Lz77pTceOPozJgxI2eeeW6S5NJLh2fJkqU1jnvllRczcuTV+cEP9l8xCwIAABocoQwAALBaq6yszJ/+9MeMHn1NqqqWve8z48ePy0Yb9cw55wxNkvTps3Vmz56dO+4YmyFDTkmzZs2y4YY9axwzf/68XHjhudlmm+1y6KFHrIBVAQAADZFQBgAAWK299dabGTHisuy334Hp3btvTj/9pGXq+8xZZ52fioryGm2NGjVKZWVlysvLa41PkttvvzWzZs3MyJE31eVSAACABk4oAwAArNY6deqcu+66Nx07dsrzz09c5r7PdO26dvXH8+fPy8SJz+TOO3+VXXbZLa1atao1ftq0abnnnrty6KFHpHPnLnW7GAAAoEETygAAAKu11q3bpHXrNl+77/+aMWNG9t139yTJWmt1zc9+Nvhzx91zz6/TqFHj/PCHBy1fwQAAwEqrUN8FAAAArAqaNGmSa6+9IRdddHkaN26cQYOOzMcfT68xZvHixfnDH+7P3nt/P61bt66nSgEAgPoilAEAAKgDrVq1ylZb9cmAAbtk+PBrM2vWzPzhD/fXGPPss09l/vz52XXXPeqpSgAAoD4JZQAAAJZTRUVFHn30v/PGG5NqtHfpslZat26dGTM+rtH+xBOPp0uXrunZc+NilgkAADQQQhkAAIDlVFpamhtvHJUbbxxVo/311ydlzpw56dHjWzXaX331pfTq1auYJQIAAA2IUAYAAOAbOOqoY/LMM09lxIjLMnHiM3nggXtzxhknZf31e2SvvfapHldRUZEpU95J9+7r12O1AABAfSqr7wIAAABWZnvssXeaNm2aX/3q9vzpT39Ms2bNs8MOO2bQoBPSpEnT6nFz585JRUVFWrVqVY/VAgAA9UkoAwAA8L+23LJ3Hn984tfu22mnXbLTTrt86dzt2rX/wuMBAIDVg1AGAABosAqFkhQKJfVdxkqvsrIqlZVV9V0GAACs9oQyAABAg1QolKRt2+YpLXUrzG+qoqIys2cvEMwAAEA9E8oAAAANUqFQktLSQkbf+USmTp9T3+WstLp2bJPjD9o2hUKJUAYAAOqZUAYAAGjQpk6fkylTZ9V3GQAAAN+Y6wAAAAAAAAAUgVAGAAAAAACgCIQyAAAAAAAARSCUAQAAAAAAKAKhDAAAAAAAQBEIZQAAAAAAAIpAKAMAAAAAAFAEQhkAAAAAAIAiEMoAAAAAAAAUgVAGAAAAAACgCIQyAAAAAAAARSCUAQAAAAAAKAKhDAAAAAAAQBEIZQAAAAAAAIpAKAMAAAAAAFAEQhkAAAAAAIAiEMoAAAAAAAAUgVAGAAAAAACgCIQyAAAAAAAARSCUAQAAAAAAKAKhDAAAAAAAQBEIZQAAAAAAAIpAKAMAAAAAAFAEQhkAAAAAAIAiEMoAAAAAAAAUgVAGAAAAAACgCIQyAAAAAAAARSCUAQAAAAAAKAKhDAAAAAAAQBEIZQAAAAAAAIpAKAMAAAAAAFAEQhkAAAAAAIAiEMoAAAAAAAAUgVAGAAAAAACgCIQyAAAAAAAARSCUAQAAAAAAKAKhDAAAAAAAQBEIZQAAAAAAAIpAKAMAAAAAAFAEQhkAAAAAAIAiEMoAAAAAAAAUgVAGAAAAAACgCIQyAAAAAAAARdCgQpmbbrophx12WI221157LYceemg233zzDBgwIHfccUeN/srKylx33XXZfvvts/nmm+eYY47Je++997XmAAAAAAAAWNEaTCgzYcKEXHPNNTXaZs2alSOPPDLdunXL7373uxx//PEZMWJEfve731WPuf766/PrX/86F198ce66665UVlbm6KOPzpIlS5Z5DgAAAAAAgBWtrL4L+OijjzJ06NA8/fTT6d69e42+u+++O40aNcpFF12UsrKy9OjRI//+979z880354ADDsiSJUsyduzYnHbaadlxxx2TJFdffXW23377PPzww9l7772/cg4AAAAAAIBiqPedMq+88koaNWqUBx54IJtttlmNvokTJ6Zv374pK/v/s6N+/fplypQpmTFjRiZNmpT58+enf//+1f2tW7fOxhtvnGeffXaZ5gAAAAAAACiGet8pM2DAgAwYMOBz+6ZNm5YNN9ywRlvHjh2TJB9++GGmTZuWJOnSpUutMZ/1fdUca6yxxnLXXlb25ZlWaWm9Z16rHI8pAMDqw2u/uuXxXD04z3WvLh9T56dueTwBWBnVeyjzZRYtWpTGjRvXaGvSpEmSZPHixVm4cGGSfO6YOXPmLNMcy6tQKEm7di2W+3iWT+vWzeq7BAAAWCl5LQ3Lx3On4XJuAFgZNehQpmnTplmyZEmNts+ClObNm6dp06ZJkiVLllR//NmYZs2aLdMcy6uysipz5y740jGlpQUvEOrY3LkLU1FRWd9lAABQBF5P1y2vpVcPnjd1ry6fO85P3fJ9DYCGpHXrZsu0i7NBhzKdO3fO9OnTa7R99nmnTp1SXl5e3datW7caYzbaaKNlmuObKC/3g7/YKioqPe4AALAcvJaG5eO503A5NwCsjBr0xTf79OmT5557LhUVFdVtTz31VNZbb7106NAhPXv2TMuWLfP0009X98+dOzevvvpq+vTps0xzAAAAAAAAFEODDmUOOOCAzJs3L+eee24mT56c3//+97ntttsyaNCgJJ/eS+bQQw/NiBEj8uijj2bSpEk5+eST07lz5+y6667LNAcAAAAAAEAxNOjLl3Xo0CFjxozJsGHDst9++2XNNdfMGWeckf322696zJAhQ1JeXp7zzjsvixYtSp8+fXLrrbemUaNGyzwHAAAAAADAitagQpnLL7+8VluvXr3ym9/85guPKS0tzemnn57TTz/9C8d81RwAAAAAAAArWoO+fBkAAAAAAMCqQigDAAAAAABQBEIZAAAAAACAIhDKAAAAAAAAFIFQBgAAAAAAoAiEMgAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARCGUAAAAAAACKQCgDAAAAAABQBEIZAAAAAACAIhDKAAAAAAAAFIFQBgAAAAAAoAiEMgAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARCGUAAAAAAACKQCgDAAAAAABQBEIZAAAAAACAIhDKAAAAAAAAFIFQBgAAAAAAoAiEMgAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARCGUAAAAAAACKQCgDAAAAAABQBEIZAAAAAACAIhDKAAAAAAAAFIFQBgAAAAAAoAiEMgAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARCGUAAAAAAACKQCgD9eCBB+7NoYf+KLvssl0OOeTA/O53d6eqqqq6/7jjBma77XrX+m/SpFc/d76///2v2W673nn++YnFWQAAAAAAAF9bWX0XAKubBx+8L1dcMSwHHvjjbLfdd/PCC//MNdcMz5IlS3LQQYemqqoqb701OT/+8SHZaaddahy77rrr1ZpvzpzZGT78smKVDwAAAADAchLKQJH98Y8PpFevzXPSSacnSXr37pt33/13fv/7u3PQQYdm6tT3s2DB/PTvv2022WTTr5zvyit/mbIyT2UAAAAAgIbO5cugyJYsWZwWLVrUaGvTpk3mzJmTJHnzzdeTJN/61oZfOdejjz6cZ599Oscd9/O6LxQAAAAAgDq1XKHMs88+m/nz539u39y5c/PHP/7xGxUFq7If/vCgPPPMU/nznx/KvHnz8vTTT+a//uuP2W23PZMkb775Rpo1a57Ro6/NXnvtnAEDtslppw3Ju+9OqTHPzJn/yVVX/TInnnhqOnRYox5WAgAAAADA17Fcoczhhx+et95663P7Xn311Zx99tnfqChYle2yy27Zbbc9c/HFv8juu++YU0/9eTbddLOceOKpSZLJk9/IwoUL0qpVq1x66YiceeZ5ef/99zJ48DGZMePj6nmuuGJYvvOdXtl9973qaykAAAAAAHwNy3wjijPPPDMffvhhkqSqqioXXHBBWrZsWWvclClTssYa/mofvshZZ52aF1/8VwYPHpJvf/s7efvtyRk79uacf/6ZufTSETnmmME5+ODDs/nmWyZJNttsi2yySa8ceugPc/fdd2bw4CH5r//6Q1544V8ZP/7uel4NAAAAAADLaplDmd122y3jxo2r0VZVVVXj89LS0my++eY55JBD6qY6WMW89NILefrpf+TMM8/LPvvsmyTZYoutstZaXXP66SflH/94PNtuu32t47p2XTvrrrteJk9+M9Onf5Rrrx2RE044OW3btk15eXkqKyuTJJWVlamoqEhpaWkxlwUAAAAAwDJY5lBmwIABGTBgQJLksMMOywUXXJAePXqssMJgVTRt2qe7zTbddLMa7Ztt9umumHfeeStz587JOut0yyab9KoxZvHiRWnXrm0mTnwm8+bNy+WXX5zLL7+4xpiTThqczp275Le/fXAFrgIAAAAAgOWxzKHM/2v8+PF1XQesFtZdt3uS5IUX/pnu3derbn/ppReSJGuttXZuuGFk1lhjjdxww63V/a+/PilTp76fQw75abbddvuMGXNHjXknTXotI0ZcltNOO7tW4AMAAAAAQMOwXKHMokWLcsMNN+Sxxx7LwoULqy+d9JmSkpI88sgjdVIgrEo23LBndtxxQEaNujqffDI3G2+8Sd555+2MG3dzNtro29lhhx2zePGiDBt2QS6++BfZffc9M23atIwZc2M22GDD7LHH3iktLU2bNm1rzLtgwYIkSbdu66ZHjw3qYWUAAAAAAHyV5Qplhg0blt/+9rfp27dvvv3tb6dQKNR1XbDKGjp0WG6//dbcf//vc+utN6VTp87Zc899csQRx6SsrCx77LF3Gjdukl//+o6cffZpadq0WXbYYccce+wJ7hUDAAAAALASW65Q5uGHH87JJ5+cn/3sZ3VdD6zyGjVqlKOPPjZHH33sF47ZeefvZeedv7fMc265Ze88/vjEuigPAAAAAIAVZLlCmaVLl6ZXr15fPRAauEKhJIVCSX2XsdKrrKxKZWVVfZcBAAAAANCgLVcos9122+Vvf/tb+vXrV9f1QNEUCiVp27Z5Sktdfu+bqqiozOzZCwQzAAAAAABfYrlCmT333DNDhw7NzJkzs9lmm6VZs2a1xuy7777ftDZYoQqFkpSWFjL6zicydfqc+i5npdW1Y5scf9C2KRRKhDIAAAAAAF9iuUKZk046KUly33335b777qvVX1JSIpRhpTF1+pxMmTqrvssAAAAAAGAVt1yhzKOPPlrXdQAAAAAAAKzSliuU6dq1a13XAQAAAAAAsEpbrlBm1KhRXznmhBNOWJ6pAQAAAAAAVkl1Hsq0bNkyHTt2FMoAAAAAAAD8P5YrlJk0aVKttgULFmTixIm54IILcv7553/jwgAAAAAAAFYlhbqaqHnz5tlhhx1y/PHH54orrqiraQEAAAAAAFYJdRbKfGattdbKW2+9VdfTAgAAAAAArNSW6/Jln6eqqirTpk3LmDFj0rVr17qaFgAAAAAAYJWwXKFMz549U1JS8rl9VVVVLl8GAAAAAADwfyxXKHP88cd/bijTsmXL7Ljjjunevfs3rQsAAAAAAGCVslyhzM9//vO6rgMAAAAAAGCVttz3lJk5c2bGjh2bZ555JnPnzk27du3Su3fvHHHEEenQoUNd1ggAAAAAALDSKyzPQdOmTct+++2X22+/PU2aNMnGG2+csrKyjBs3Lvvuu28++uijOi2yvLw81157bXbaaadsscUWOeSQQ/Kvf/2ruv+1117LoYcems033zwDBgzIHXfcUeP4ysrKXHfdddl+++2z+eab55hjjsl7771XpzUCAAAAAAB8meUKZYYPH56ysrI89NBDGT9+fK666qqMHz8+//Vf/5WmTZvm6quvrtMib7jhhtxzzz25+OKLc99992W99dbL0UcfnenTp2fWrFk58sgj061bt/zud7/L8ccfnxEjRuR3v/td9fHXX399fv3rX+fiiy/OXXfdlcrKyhx99NFZsmRJndYJAAAAAADwRZYrlHn88cczZMiQrLPOOjXa11lnnRx//PH529/+VifFfeaRRx7J3nvvne222y7rrrtuzjrrrHzyySf517/+lbvvvjuNGjXKRRddlB49euSAAw7IEUcckZtvvjlJsmTJkowdOzZDhgzJjjvumJ49e+bqq6/OtGnT8vDDD9dpnQAAAAAAAF9kuUKZioqKtGvX7nP72rdvn3nz5n2jov6vDh065LHHHsv777+fioqK/OY3v0njxo3Ts2fPTJw4MX379k1Z2f9/e5x+/fplypQpmTFjRiZNmpT58+enf//+1f2tW7fOxhtvnGeffbZO6wQAAAAAAPgiZV89pLaNNtooDz74YHbYYYdafffff3823HDDb1zY/+vcc8/NiSeemJ133jmlpaUpFAoZOXJkunXrlmnTptX6eh07dkySfPjhh5k2bVqSpEuXLrXGfNa3vMrKvjzTKi1drsyLL1GXj6nzU7c8ngBAXfP6om55PFcPznPd8z604fJ4ArAyWq5QZvDgwRk4cGDmzJmTPffcM2uuuWY+/vjj/PGPf8zjjz+e6667rk6LnDx5clq1apXRo0enU6dOueeee3LaaaflV7/6VRYtWpTGjRvXGN+kSZMkyeLFi7Nw4cIk+dwxc+bMWe6aCoWStGvXYrmPZ/m0bt2svkvgCzg3AAANm9drsHw8dxou5waAldFyhTLbbrttLr/88owYMaLG/WPWXHPNXHbZZfne975XZwV++OGHOfXUU3Pbbbeld+/eSZJNN900kydPzsiRI9O0adMsWbKkxjGLFy9OkjRv3jxNmzZN8um9ZT77+LMxzZot/w/vysqqzJ274EvHlJYWvECoY3PnLkxFRWWdzOX81K26PDcAAInXa3XN67XVg+dN3fM+tOHyfQ2AhqR162bLtItzuUKZJJk+fXo23njjnHnmmZkzZ04mTZqUkSNH1vn9ZF544YUsXbo0m266aY32zTbbLH/729+y1lprZfr06bVqS5JOnTqlvLy8uq1bt241xmy00UbfqLbycj/4i62iotLj3kA5NwAADZvXa7B8PHcaLucGgJXRcl18c+zYsbnmmmvSvXv39OjRI1tuuWV22mmn7LXXXrn88stzzz331FmBnTt3TpK8/vrrNdrfeOONdO/ePX369Mlzzz2XioqK6r6nnnoq6623Xjp06JCePXumZcuWefrpp6v7586dm1dffTV9+vSpszoBAAAAAAC+zHKFMnfddVdOOumknHPOOdVtXbp0yXnnnZcTTjght912W13Vl169emWrrbbKmWeemaeeeipTpkzJNddckyeffDI/+9nPcsABB2TevHk599xzM3ny5Pz+97/PbbfdlkGDBiX59F4yhx56aEaMGJFHH300kyZNysknn5zOnTtn1113rbM6AQAAAAAAvsxyXb7so48+qnU5sc9sttlmueGGG75RUf+vQqGQG264Iddcc03OPvvszJkzJxtuuGFuu+22bLbZZkmSMWPGZNiwYdlvv/2y5ppr5owzzsh+++1XPceQIUNSXl6e8847L4sWLUqfPn1y6623plGjRnVWJwAAAAAAwJdZrlCma9euefLJJ9O/f/9afc8++2z1JcfqSps2bTJ06NAMHTr0c/t79eqV3/zmN194fGlpaU4//fScfvrpdVoXAAAAAADAslquUOZHP/pRhg8fnqVLl2aXXXZJhw4dMnPmzDz22GMZN25cTj311LquEwAAAAAAYKW2XKHMEUcckY8++ijjx4+vcf+Y0tLS/PSnP82RRx5ZV/UBAAAAAACsEpYrlEmSM888M4MHD86//vWvzJ49O61bt06vXr3Srl27uqwPAAAAAABglbDcoUyStGrVKttvv31d1QIAAAAAALDKKtR3AQAAAAAAAKsDoQwAAAAAAEARCGUAAAAAAACKQCgDAAAAAABQBEIZAAAAAACAIhDKAAAAAAAAFIFQBgAAAAAAoAiEMgAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARlNV3AQAAAACwunn++YkZMuTYL+w/6qif5aijfpb3338v1113VV588Z8pLS3NTjvtkuOO+3latGhZPfadd97ODTdclxdffCGFQiHbbLNdjjvu5+nQYY1iLAWAr0EoAwAAAABFttFGPXPjjeNqtd9yyw2ZNOmV7LLLbvnkk08yZMix6dChQ84994LMmjUr119/XT744INcddXIJMmMGR9nyJBj07Xr2vnFLy7OokWLcvPNo3PSSYMzbtyvU1bm138ADYnvygAAAABQZC1atMwmm2xao+3xx/8nzz33TC6++PJ067Zuxo8fl7lz52Ts2Alp27ZtkmTNNTvm9NNPzIsv/iu9em2eBx64N/Pnz8svf3lV2rT5dEzbtm0zZMixee65Z7P11v2LvDIAvox7ygAAAABAPVu8eFGuvnp4ttlmu+y00y5JkqeffjK9em1RHcgkSd++/dK8eYs8+eQTSZL99vthrr9+THUgkySNGjVKkixZsrho9QOwbIQyAAAAAFDP7r77rv+9FNmp1W3//veUdOvWrca40tLSdOmyVt57799Jknbt2qVnz42TJIsXL87LL7+Uq676Zbp2XTt9+/Yr3gIAWCYuXwYAAAAA9Wjp0qX57W/vzM4775q1116nun3+/Hlp3rxFrfHNmzfP/Pnza7UfccRBee+9d9OkSZNceumINGnSdIXWDcDXZ6cMAAAAANSjxx57NP/5z39y0EGH1WivrKz8wmNKSmr/Wu+UU87MVVeNylZb9c0ZZ5yUp59+ss5rBeCbEcoAAAAAQD36618fzXrrrZ9vfWvDGu0tW7bMggULao1fsGB+WrZsWau9T5+t07dvv1x66fCstVbXTJhw+wqrGYDlI5QBAAAAgHpSXl6eZ555MgMGfK9W3zrrrJupU9+r0VZRUZEPPvgg667bPUny/PMT8+STj9cYU1ZWlh49NsiMGR+vsLoBWD5CGQAAAACoJ2+9NTmLFi3KpptuVquvb99++de/ns+sWbOq25555qksXLggffv2S5L86U9/zCWXDM2CBf//PWYWLJifl19+KT16fGvFLwCAr0UoAwAAAAD15O23JydJundfv1bfvvsemCZNmuTkk4/P//zPY3nwwfty0UXnp1+/bapDnIMPPjxLlizJGWecnH/84/H8z//8JSeeODgLFizIUUf9rKhrAeCrCWUAAAAAoJ7MnPmfJEmrVq1q9bVr1y7XXXdj2rRpm4suOi8333x9dtpp51x00WXVY7p3Xy+jR9+Sxo2b5OKLf5Fhwy5Mu3btc+ONt2a99WoHPQDUr7L6LgAAAAAAVleHHPLTHHLIT7+wf/31N8i1117/pXNsuGHPXHXVyLouDYAVwE4ZAAAAAACAIrBTBgAAAIDVXqFQkkKhpL7LWCVUVlalsrKqvssAaJCEMgAAAACs1gqFkrRt2zylpS4qUxcqKioze/YCwQzA5xDKAAAAALBaKxRKUlpayOg7n8jU6XPqu5yVWteObXL8QdumUCgRygB8DqEMAAAAACSZOn1OpkydVd9lALAKsycTAAAAAACgCIQyAAAAAAAARSCUAQAAAAAAKAKhDAAAAAAAQBEIZQAAAAAAAIpAKAMAAAAAAFAEQhkAAAAAAIAiKKvvAgAAYHX08ssv5aabRuW1115Js2bNs/XW/XP88SemXbv2SZJ//OPxjB17c6ZMeTtt2rTNnnvuk8MPPyqNGjVKkhx44D6ZNu3Dz527S5e1cs89DxRtLQAAACwboQwAABTZpEmvZciQY9O7d99ceumIzJjxcW66aVTOPvu93Hjj2DzzzFM566xTsvvue2XQoOPz7rtTcuONozNjxoyceea5SZJLLx2eJUuW1pj3lVdezMiRV+cHP9i/PpYFAADAVxDKAABAkd1ww3XZcMONcvnlV6ZQ+PSKwi1atMi1116ZDz6YmvHjx2WjjXrmnHOGJkn69Nk6s2fPzh13jM2QIaekWbNm2XDDnjXmnD9/Xi688Nxss812OfTQI4q9JAAAAJaBUAYAAIpozpzZ+ec/n8u5515QHcgkyXe/OyDf/e6AJMlZZ52fioryGsc1atQolZWVKS+v2f6Z22+/NbNmzczIkTetuOIBAAD4RoQyAABQRG+9NTmVlZVp27ZdLrzwvDz++N+SVGWHHXbKSSednlatWqVr17Wrx8+fPy8TJz6TO+/8VXbZZbe0atWq1pzTpk3LPffclUMPPSKdO3cp4moAAAD4OoQyAABQRLNmzUqSXHbZRenXb5tcdtmIvP/+u7nxxtH54IOpuf76MSkpKUmSzJgxI/vuu3uSZK21uuZnPxv8uXPec8+v06hR4/zwhwcVZxEAAAAsl8JXDwEAAOpKefnSJMlGG/XMWWedn969+2bffQ/MaaedlZdeeiHPPvt09dgmTZrk2mtvyEUXXZ7GjRtn0KAj8/HH02vMt3jx4vzhD/dn772/n9atWxd1LQAAAHw9QhkAACii5s2bJ0m22Wb7Gu1bb71NkuSNNyZVt7Vq1SpbbdUnAwbskuHDr82sWTPzhz/cX+O4Z599KvPnz8+uu+6xgisHAADgmxLKAABAEa29drckydKlS2q0l5eXJ0kaN26SRx/97xrhTJJ06bJWWrdunRkzPq7R/sQTj6dLl67p2XPjFVg1AAAAdUEoAwAARdS9+3rp0mWtPProw6mqqqpuf+KJ/0mSbL75FrnxxlG58cZRNY57/fVJmTNnTnr0+FaN9ldffSm9evVa8YUDAADwjQllAACgiEpKSjJ48JC8/PJLGTr0nDz77NO55567cu21V2XHHQdkww175qijjskzzzyVESMuy8SJz+SBB+7NGWeclPXX75G99tqneq6KiopMmfJOundfvx5XBAAAwLIqq+8CAABgdbPTTrvk8sub5LbbbsmZZ56cVq1aZ999D8gxxxyXJNljj73TtGnT/OpXt+dPf/pjmjVrnh122DGDBp2QJk2aVs8zd+6cVFRUpFWrVvW1FAAAAL4GoQwAANSDbbfdPttuu/0X9u+00y7ZaaddvnSOdu3a5/HHJ9Z1aQAAAKwgQhkAAFZrhUJJCoWS+i5jlVBZWZXKyqqvHggAALCaEsoAALDaKhRK0rZt85SWutViXaioqMzs2QsEMwAAAF9AKAMAwGqrUChJaWkho+98IlOnz6nvclZqXTu2yfEHbZtCoUQoAwAA8AWEMgAArPamTp+TKVNn1XcZAAAArOJcpwEAAAAAAKAIhDIAAAAAAABFIJQBAAAAAAAoAqEMAAAAAABAEQhlAAAAAAAAikAoAwAAAAAAUAQrTShz3333Zc8998ymm26avfbaK//1X/9V3ff+++9n0KBB2XLLLbPddtvlmmuuSUVFRY3jJ0yYkJ133jm9evXKwQcfnFdffbXYSwAAAAAAAFZjK0Uoc//99+fcc8/NIYcckj/+8Y/Ze++9c8opp+Sf//xnli5dmoEDByZJ7rrrrlxwwQW58847M3r06Orj77333lxxxRU58cQT8/vf/z5rr712jjzyyMycObO+lgQAAAAAAKxmyuq7gK9SVVWVa6+9NocffngOOeSQJMlxxx2XiRMn5plnnsnUqVPzwQcf5O67706bNm2y4YYb5j//+U+uuOKKHHvssWncuHFuvPHGHHroofn+97+fJLn00kuzyy675J577smgQYPqc3kAAAAAAMBqosHvlHnnnXcyderU7LPPPjXab7311gwaNCgTJ07Md77znbRp06a6r1+/fpk3b15ee+21/Oc//8mUKVPSv3//6v6ysrL07t07zz77bNHWAQAAAAAArN4a/E6Zd955J0myYMGCDBw4MK+++mrWXnvtHHfccRkwYECmTZuWzp071zimY8eOSZIPP/wwZWWfLrFLly61xkyaNOkb1VZW9uWZVmlpg8+8Vjp1+Zg6P3VrVXg8Fy9enAEDtk9FRXmN9mbNmuWxx55IkkyZ8k5Gjbo2zz//XMrKSrPFFltlyJCT07Xr2tXjhw49N3/+83/l/7r00isyYMAuK3YRAHwtq8LPr4bG67WGy+O5enCe657vaw2Xc9OweUwBPl+DD2XmzZuXJDnzzDNzwgkn5LTTTsuf//znDB48OOPGjcuiRYvSunXrGsc0adIkyae/YF24cGGSpHHjxrXGLF68eLnrKhRK0q5di+U+nuXTunWz+i6BL7AqnJuXXno7FRXlGT58eLp161bdXigU0q5di3z44Yc59tiBWW+99XL11Vdl4cKFueaaa3LyySfkwQcfTNOmTZMkb789OXvvvXcOO+ywGvN37949bdv6vgHAqm1VeE2wqnJuYPl47jRczk3D5vwAfL4GH8o0atQoSTJw4MDst99+SZJvf/vbefXVVzNu3Lg0bdo0S5YsqXHMZ2FL8+bNq39J+nljmjVb/h8OlZVVmTt3wZeOKS0t+AFUx+bOXZiKiso6mcv5qVt1eW7qy3PPvZDS0rJsvfX2tYLcWbPm58orr07z5i1y9dWj0rTpp//vDB26Rk4//aQ8+eSz2XzzLbN48eK8/fY7+eEPD8q6636rxhxVVZ/OA0DD4fVA3fN6reFaFV6v8dU8b+qe72sNl3PTsPm5A6xuWrdutky7BBt8KNOpU6ckyYYbblijfYMNNshf//rX9O3bN2+88UaNvunTp1cf+9lly6ZPn54ePXrUGPPZ3MurvNwPlmKrqKj0uDdQq8K5ef31SVl33XVTKJTVWktVVVUee+zR/OQnh6asrEl1/7e+1TP33fenJJ9+T3jzzTdTUVGe9df/1kr/eADA8lgVXhOsqpwbWD6eOw2Xc9OwOT8An6/BX9zxO9/5Tlq0aJEXXnihRvsbb7yRbt26pU+fPnn11VerL3OWJE899VRatGiRnj17pkOHDllvvfXy9NNPV/eXl5dn4sSJ6dOnT9HWATR8b775RkpLS3Pyycdnl122yx57DMgVVwzLggXz8+GHH2TevHnp3LlLrrzyl9ljjwEZMGCbnHXWKZk+/aMacyTJH/5wX37wg92y4479Mnjw0XnllZfra1kAAAAAQAPR4EOZpk2b5uijj87o0aPzhz/8Ie+++25uuOGGPPHEEznyyCOzyy67ZM0118xJJ52USZMm5ZFHHslVV12Vo446qvryQ0cddVTGjRuXe++9N5MnT84555yTRYsW5cADD6zn1QENRVVVVd56a3Lef//9bLfddzNixHU5/PCj8sgjD+e0007M7NmzkiQ33DAyM2ZMz4UXXpozzzwvb7zxeoYMObb6/lVvvvl6kmThwoUZOnRYLrhgWJYsWZIhQwZl8uQ36219AAAAAED9a/CXL0uSwYMHp1mzZrn66qvz0UcfpUePHhk5cmS23nrrJMmYMWNy4YUX5kc/+lHatGmTgw8+OIMHD64+/kc/+lE++eSTXHPNNZk9e3Y22WSTjBs3Lu3bt6+vJQENTFVVVS6//Mq0bdsu66//6aUON998y3To0CEXXXR+nnnmqSRJ+/btM2zY8BQKn2baXbuuk2OPPTIPP/xf+cEP9s+BB/442267Q7beun/13Ftt1TcHHbRf7rhjbC666LLiLw4AAAAAaBBWilAmSY488sgceeSRn9u37rrrZuzYsV96/MCBAzNw4MAVURqwCigUCtlyy9612vv33y5JUln56XVw+/XbtjqQSZJNNtk0LVu2rN4h061b93Tr1r3GHK1atcqmm26WyZNr3v8KAAAAAFi9NPjLlwEUw4wZH+eBB+7NtGnTarQvXrw4SdKyZauUlJRkyZIltY6tqKhIkyZNkiSPPvpw9a6a/ztP27btVkDlAAAAAMDKQigDkKS8vCJXXDEs99//uxrtf/nLwyktLU2/fttks822yN/+9liNYGbixGeycOHC9Oq1RZLk/vt/n+HDL8vSpUurx3z88fS89NILn7sTBwAAAABWFYsXL853v7t1ttuud43/vve97T93/HXXXZnttvvy35mdd94ZGTbsghVQbf1YaS5fBrAide7cOXvuuU/uvHN8mjRpkk026ZUXX/xXxo8fl/33/1G6dVs3xx57Qn7+80E5/fQT85OfHJpZs2bmhhtGZuONN8l22+2QJDniiKNz0kmDc9ZZp+aHP/xJ5s6dm3Hjbk7r1m3yk58cWs+rBAAAAIAV55133kpFRUV+8YuLs9Zaa1e3l5bW3h/yr389n3vuuesL56qsrMzIkVfnr3/9S/bYY+8VUm99EMoA/K/TTjs7a63VNX/+80O5446xWXPNjhk4cFAOPvjwJMkmm/TKddfdmJtvvj7nnXdGmjZtmu233zHHH39SSktLkyRbbtk7V101KmPH3pyhQ89OSUkhW2/dP8cdNyQtW7asz+UBAAAAwAr15ptvpLS0NDvuuHMaN278heMWLFiQSy+9MGuu2THTp39Uq3/y5DdzzTXD89prr1TfNmBVIZQB+F+NGzfOEUccnSOOOPoLx2y66WYZOfKmL52nd+++6d27b12XB/C1LV68OLvuukMqKipqtDdr1iz//d9/T5I89NCDufPO8Zk6dWo6deqU/ff/UQ488McpKSmpNV95eXkGDz46W2/dPwMHDirKGgAAAFh5vPnm61l33e5fGsgkyfXXX5sOHTpkq6365rbbxtTqv+SSoWnWrFluuum2nHXWKSuq3HohlAEapEKhJIVC7V8I8vVVVlalsrKqvssA6sFXbRt/8MH78stfXpKDDz48ffv2y6uvvpxRo67OwoULcvjhR9WYa/HixbnkkqF59dWXs/XW/Yu6DgAAAFYOn+2UOfnk4/PSSy+kUaPG2WmnnXPCCSelefMWSZJnn30qf/rTHzN27IT893//6XPnOf/8i9KjxwbFLL1ohDJAg1MolKRt2+afe61Jvr6KisrMnr1AMAOroa/aNn7HHeOy4447Z/DgIUk+3en33nvv5ne/+02NUOaFF/6Zq666Ih9/PL1otQMAALByqaqqyltvTU5VVVX23nvf/PSnA/Paa69m3LhbMmXKOxk16uYsWLAgl112cQYOPDbduq37hXOtqoFMIpQBGqBCoSSlpYWMvvOJTJ0+p77LWal17dgmxx+0bQqFEqEMrIa+atv48OHX1Lo2b1lZoyxevKRG25lnnpJevTbPL395VQ48cJ8VVi8AAAArr6qqqlx++ZVp27Zd1l+/R5Jk8823TIcOHXLRRefn6aefzGOPPZJOnTrlxz8+uJ6rrT9CGaDBmjp9TqZMnVXfZQCstL5q23j37usl+fSF8yefzM3//M9j+fOf/5gf//iQGvOMHn3LKv1XSgAAAHxzhUIhW27Zu1Z7//7bJUlef/21PPLIwxkz5o5UVlamsrIyVVWf/hFxeXl5CoVCCoVV/8o5QhkAgFXQsmwb/+zF7iuvvJRjj/30cmU9e26cgw46tMZcAhkAAAC+yowZH+cf/3g8ffv2T+fOnavbFy9enCR54IF7s2TJ4hx++I9rHbvjjv2yxx5759xzLyhWufVGKAMAsApalm3j/ftvmyTp3LlLRo68KR9++EFuueWGHHvsURk7dkKaNm1an0sAAABgJVJeXpErrhiWww47MoMGHV/d/pe/PJzS0tJcc831WbBgfo1j7r//3jz44L0ZM+aOtGnTtsgV1w+hDADAKuirto1PnvxmdSizxhprZo011swWW2yVtdbqmhNO+Fkee+yR7LHH3kWtGQAAgJVX586ds+ee++TOO8enSZMm2WSTXnnxxX9l/Phx2X//H6Vbt3VrHfPEE39P8ulVG1YXQhkAgFXQV20bb9KkSR5++E/ZeOPvZO2116nu33DDnv97/IziFgwAAMBK77TTzs5aa3XNn//8UO64Y2zWXLNjBg4clIMPPry+S2swhDIAAKugr9o2vuWWvTNo0BHZbbc9c8YZ51b3P/vsU0mSDTZwHxkAAAC+nsaNG+eII47OEUccvUzjBw4clIEDB33pmN/+9sG6KK3BEMoAAKyCvmrb+AYbfCuHHXZkbr31prRr1z5bbtk7kye/kXHjbknv3n3Tr9+29b0EAAAAvoFCoSSFQkl9l7FKqKysSmVlVZ3MJZQBAFhFfdW28Z/+dGDatm2b3/3u7tx556/Stm3b/OAHB+Soo36WkhIv3IGGZ/Hixdl11x1SUVFRo71Zs2b57//+9Hrkkya9mlGjrsnrr7+W5s1bZM8998lRR/0sjRo1+tw5zzvvjDRr1jznnnvBii4fAKBoCoWStG3bPKWlhfouZZVQUVGZ2bMX1EkwI5QBAFhFfdW28ZKSkuy774HZd98Dl3nOxx+fWFflAXxt77zzVioqKvKLX1yctdZau7r9s182TJ36fk46aXC+851eueiiyzJlypTccsv1mTt3Tk4//Zwac1VWVmbkyKvz17/+JXvssXdR1wEAsKIVCiUpLS1k9J1PZOr0OfVdzkqta8c2Of6gbVMolAhlAABWFraN15263DYOrFzefPONlJaWZscdd07jxo1r9U+YcHuaN2+Ryy+/Mo0aNUr//tuladOmufrqK3LYYUelc+fOSZLJk9/MNdcMz2uvvZImTZoUexkAAEUzdfqcTJk6q77L4P8hlAEAWMFsG69bdbltHFi5vPnm61l33e6fG8gkyTPPPJX+/betcamyHXfcOVdeeXmeeebJfP/7+yVJLrlkaJo1a5abbrotZ511SlFqBwCARCgDALDC2TZed+p62ziwcvlsp8zJJx+fl156IY0aNc5OO+2cE044KaWlpZk27cOss063Gse0a9cuLVq0yLvv/ru67fzzL0qPHhsUu3wAABDKAAAUi23jAMuvqqoqb701OVVVVdl7733z058OzGuvvZpx427JlCnv5KKLLkuStGjRstaxzZu3yPz586s/F8gAAFBfhDIAAAA0eFVVVbn88ivTtm27rL9+jyTJ5ptvmQ4dOuSii87P889P/NLj3dcLAICGQCgDAABAg1coFLLllr1rtffvv12S5IMPpiZJFiyYX2vM/PnzP3cHDQAAFJu7zQIAANDgzZjxcR544N5MmzatRvvixYuTJGussUbWXLNj3n///Rr9s2bNzIIF89O9+3pFqxUAAL6IUAYAAIAGr7y8IldcMSz33/+7Gu1/+cvDKS0tTa9eW6RPn63zj3/8PUuWLKnu/+tf/5LS0tJsuWWfYpcMAAC1uHwZAAAADV7nzp2z55775M47x6dJkybZZJNeefHFf2X8+HHZf/8fpVu3dXPIIT/NI488nNNOG5If//iQvPfev3Pzzddnn332S+fOnet7CQAAIJQBAABg5XDaaWdnrbW65s9/fih33DE2a67ZMQMHDsrBBx+eJFl33e65+upRGT362px//plp06ZtfvSjg3P00cfWc+UAAPApoQwAAAArhcaNG+eII47OEUcc/YVjNttsi9x8823LPOdvf/tgHVQGAADLRigDAADA11YolKRQKKnvMlYJlZVVqaysqu8yAAAoAqEMAAAAX0uhUJK2bZuntLRQ36WsEioqKjN79gLBDADAakAoAwAAwNdSKJSktLSQ0Xc+kanT59R3OSu1rh3b5PiDtk2hUCKUAQBYDQhlAAAAWC5Tp8/JlKmz6rsMAABYadhrDgAAAAAAUARCGQAAAAAAgCIQygAAAAAAABSBUAYAAAAAAKAIhDIAAAAAAABFIJQBAAAAAAAoAqEMAAAAAABAEQhlAAAAAAAAikAoAwAAAAAAUARCGQAAAAAAgCIQygAAAAAAABSBUAYAAAAAAKAIyuq7AABg1XHOOafnjTcm5be/fbC67bnnns24cbdk8uQ307hx42y6aa8MHnxiunZdu3rMO++8nRtuuC4vvvhCCoVCttlmuxx33M/TocMa9bEMAAAAgBXCThkAoE78+c8P5W9/e6xG24sv/iunnHJC2rRpk6FDL85JJ52e999/L8cdNzCzZ89OksyY8XGGDDk2c+fOzS9+cXFOO+3svPzyiznppMEpLy+vh5UAAAAArBh2ygAA39iMGR/nmmtGpGPHTjXaJ0y4Pd27r5eLL/5lCoVP/xakV6/Nsv/+e+Whhx7MwQcflgceuDfz58/LL395Vdq0aZskadu2bYYMOTbPPfdstt66f7GXAwAAALBCCGUAgG/s8ssvTt++W6dx4yb55z+fq27feONNsv32O1YHMkmyxhprpkWLlvngg/eTJPvt98Nss8121YFMkjRq1ChJsmTJ4uIsAAAAAKAIXL4MgJXOOeecngMP3Odz+8rLy/Oznx2RW2+96UvnmDTptXz3u1vnoYce/NJxfLUHH7wvr78+KSeffGatvp/+dGD23vsHNdr++c/n8sknc7PeeusnSdq1a5eePTdOkixevDgvv/xSrrrql+nade307dtvxS8AAAAAoEjslAFgpfLZfUs6d+5Sq2/x4sW55JKhefXVl7/0kldLlizJsGFDU1FRsSJLXS1Mm/ZhRo68Ouec84u0bdv2K8fPnj07v/zlsKyxxprZY4+9a/UfccRBee+9d9OkSZNceumINGnSdAVUDQAAAFA/7JQBYKXxRfctSZIXXvhnfvazI/Lcc89+5TxjxtyQ+fPnr4gSVytVVVW57LKL0r//Ntlxx52/cvyMGTNy4onH5j//mZFhw4anefMWtcaccsqZueqqUdlqq74544yT8vTTT66I0gEAAADqhVAGgJXGZ/ct2WqrPrX6zjzzlHTq1Dljx/7qS+d46aUX8tvf3p1TTjljRZW52vj97+/OW2+9mSFDTk15eXnKy8tTVVWV5NPLyFVWVlaPfeutyRk06IhMnz49V155Xb7znU0+d84+fbZO3779cumlw7PWWl0zYcLtRVkLAAAAQDG4fBkAK4XP7lsyfvzdGT36mlr9o0ffkh49NvjSORYtWpRhwy7M4YcfmR49vrWCKl19PPbYo5k9e3Z+8IPda/XtuGO/HHnkMRk4cFCef35izj771LRo0TKjR9+S9dfvUWPs889PzOLFi9K//3bVbWVlZenRY4O8/fZbK3wdAAAAAMUilAGgwVuW+5Z8VSCTJDfeODLNmzfLoYcekY8/nl7HVa5+zjjjnCxYsKBG29ixt+T111/LL395VdZYY8288caknHHGSenSZa1cffXorLHGmrXm+dOf/pgnnvhb7rnngepLmi1YMD8vv/xSNt10s6KsBQAAAKAYhDIANGhf974lX+T55yfmgQfuzc03356yMj/+6kK3bt1rtbVp0yaNGjVKz54bJ0nOOOOklJeXZ+DAQZk2bVqmTZtWPbZdu3bp2nXtHHzw4XnssUdyxhkn5+CDD8/SpUvyq1/dngULFuSoo35WrOUAAAAArHB+KwVAg/bZfUtuv/2ulJeXJ0mN+5YUCoUUCl9+i7QFCxbksssuyiGH/DTdu69X434nlZWVKS8vF9SsAFOnvp833ng9SXLeeWfW6t9jj71z7rkXpHv39TJ69C258cbRufjiX6SioiKbb75lzj77/Ky33vrFLhsAAABghfEbKAAatGW9b8mXmTTp1Xz44QcZN+6WjBt3S42+yy+/OJdffnEef3xinda9ujr33AuqP+7ade1lflw33LBnrrpq5AqqCgAAAKBhEMoA0KAty31LvkrPnt/OmDF31GibMWNGzjrrlBx55DHZdtvt67Tm+lQolKRQKKnvMlZ6lZVVqaysqu8yAAAAgFWMUAaABm1Z7lvyVZo3b1Fr7IcffpAk6dJlrWWep6ErFErStm3zlJZ++eXc+GoVFZWZPXuBYAYAAACoU0IZAFhFFAolKS0tZPSdT2Tq9Dn1Xc5Kq2vHNjn+oG1TKJQIZQAAAIA6JZQBYKXz/9635PMsy31MunRZa5W9j8zU6XMyZeqs+i4DAAAAgP9DKAPA1+KeJXXHfUsAAAAAVi8rVSjzzjvvZP/998/555+f/fffP0ny2muvZdiwYXn55ZfTvn37HHHEETn88MOrj6msrMyoUaNyzz335JNPPkmfPn3yi1/8Iuuss059LQNgpeWeJXXLfUsAAAAAVi8rTSizdOnSnHbaaVmwYEF126xZs3LkkUdmwIABufDCC/Ovf/0rF154YVq0aJEDDjggSXL99dfn17/+dS6//PJ07tw5w4cPz9FHH50HH3wwjRs3rq/lAKyU3LOk7rhvCQAAAMDqZ6UJZUaOHJmWLVvWaLv77rvTqFGjXHTRRSkrK0uPHj3y73//OzfffHMOOOCALFmyJGPHjs1pp52WHXfcMUly9dVXZ/vtt8/DDz+cvffeux5WArDyc88SAAAAAPj6Vorrzzz77LP5zW9+k8svv7xG+8SJE9O3b9+Ulf3/2VK/fv0yZcqUzJgxI5MmTcr8+fPTv3//6v7WrVtn4403zrPPPlu0+gEAAAAAABr8Tpm5c+fmjDPOyHnnnZcuXbrU6Js2bVo23HDDGm0dO3ZMknz44YeZNm1aktQ6rmPHjtV930RZ2ZdnWu65UPfq8jF1fuqWc9Ow1dVj6tzUPc+dhsu5adh8X2u4PHcaLuemYfN9reHy3Gm4nJuGzWMK9ctzsO7V1WPa4EOZCy64IFtssUX22WefWn2LFi2qdV+YJk2aJEkWL16chQsXJsnnjpkz55vdC6FQKEm7di2+0Rx8fa1bN6vvEvgCzk3D5vw0XM5Nw+XcNGzOT8Pl3DRczk3D5vw0XM5Nw+XcNGzOD7Cqqavvaw06lLnvvvsyceLEPPjgg5/b37Rp0yxZsqRG2+LFi5MkzZs3T9OmTZMkS5Ysqf74szHNmn2zB7Cysipz5y740jGlpQU/gOrY3LkLU1FRWSdzOT91y7lp2Orq/Dg3dc9zp+Fybho239caLs+dhsu5adh8X2u4PHcaLuemYavL8wN8fb6v1b2v+r7WunWzZdpN06BDmd/97nf5z3/+kx133LFG+9ChQ/PQQw+lc+fOmT59eo2+zz7v1KlTysvLq9u6detWY8xGG230jesrL/eDpdgqKio97g2Uc9OwOT8Nl3PTcDk3DZvz03A5Nw2Xc9OwOT8Nl3PTcDk3DZvzA6xq6ur7WoMOZUaMGJFFixbVaNt1110zZMiQfP/738/999+fu+66KxUVFSktLU2SPPXUU1lvvfXSoUOHtGrVKi1btszTTz9dHcrMnTs3r776ag499NCirwcAAAAAAFh9NehQplOnTp/b3qFDh3Tq1CkHHHBAxowZk3PPPTdHH310Xnzxxdx222258MILk3x6L5lDDz00I0aMSPv27dO1a9cMHz48nTt3zq677lrMpQAAAAAAAKu5Bh3KfJUOHTpkzJgxGTZsWPbbb7+sueaaOeOMM7LffvtVjxkyZEjKy8tz3nnnZdGiRenTp09uvfXWNGrUqB4rBwAAAABgeVRWVuauuybkgQd+n+nTp2eddbrlkEMOz6677vG546+77srcffedefzxiTXaX375xdx00+i8+urLadasebbZZrsce+wJad++QzGWwWpqpQtlXn/99Rqf9+rVK7/5zW++cHxpaWlOP/30nH766Su6NAAAAAAAVrAxY27Mr399RwYOPDbf/vbGefLJJ3LRReenpKQk3/ve7jXG/utfz+eee+6qNcerr76cn/98UNZdd72ce+6FadKkSe68c3wGDToq48ZNSMuWLYu1HFYzK10oAwAAAADA6mnRokW5554788MfHpTDDjsiSdK7d9+8/vpr+e1vf1MjlFmwYEEuvfTCrLlmx0yf/lGNee64Y2xatGiZ6667Ma1bt06SbLVVnxx88AGZMOH2DBp0fNHWxOqlUN8FAAAAAADAsmjUqFFuuGFsfvKTQ2q1L1myuEbb9ddfmw4dOmTPPfepNc+UKVPSq9fm1YFMkjRt2jQbb7xJnnzyiRVTPEQoAwAAAADASqK0tDQbbPCtdOiwRqqqqjJz5n8yfvxtmTjxmey33w+rxz377FP505/+mLPPHpqSkpJa87Rt2zbTpn1Yq/2DD97PBx9MXaFrYPUmlAEAAAAAYKXzyCN/zve/v1tuumlU+vffNrvttkeSZN68ebnssoszcOCx6dZt3c89dq+9vp833piUa6+9MjNmfJz//GdGrr/+ukyZ8k4WLVpYzGWwmhHKAAAAAACw0tl4400yatTNOfnk0/Piiy/k1FOHpKqqKtddd2U6deqUH//44C88dp999s0JJ5yUP/zhvuy77x7Zd9898uGHH+T7398vTZo0LeIqWN2U1XcBAAAAAADwdXXtuna6dl07m2++ZZo3b5Fhwy7IbbeNySOPPJwxY+5IZWVlKisrU1VVlSQpLy9PoVBIofDpXoWf/OTQHHjgTzJ16vtp3bpN2rVrl4sv/kWN+8xAXRPKAAAAAACwUpg1a1aeeuqJ9Ou3Tdq1a1/dvtFGPZMk48ePy5IlS3L44T+udeyOO/bLHnvsnXPPvSCTJr2ajz6alu9+d0DWXbd79Zg33piUDTfsucLXwepLKAMAAAAAwEph8eLFGTbsggwadHwOO+zI6vZnnnkqSXLrrb/K4sWLahxz//335sEH782YMXekTZu2SZLnn38uY8bcmPvv75NWrVolSZ599qm8887bOfjgw4uzGFZLQhkAAAAAAFYKnTt3zl57fT/jxo1JaWlZNtxwo7zwwj8zYcLt2XvvH2S99davdcwTT/w9SdKz58bVbbvttkd+9avb8otfnJWDDz4sH300LSNHXp1NN90su+66R9HWw+pHKAMAAAAAwErjtNPOzlprdc0DD9ybjz76MB07dsrAgYNy0EGHLfMcHTqskauuGpVRo67OOeeckVatWmXPPb+fY445NqWlpSuwelZ3QhkAAAAAAFYajRo1yk9/OjA//enAZRo/cOCgDBw4qFZ7z57fzqhRN9d1efClhDIAAAAAACyXQqEkhUJJfZex0qusrEplZVV9l0ERCGUAAAAAAPjaCoWStG3bPKWlhfouZaVXUVGZ2bMXCGZWA0IZAAAAAAC+tkKhJKWlhYy+84lMnT6nvstZaXXt2CbHH7RtCoUSocxqQCgDAAAAAMBymzp9TqZMnVXfZcBKwb4yAAAAAACAIhDKAAAAAAAAFIFQBgAAAAAAoAiEMgAAAAAAAEUglAEAAAAAACgCoQwAAAAAAEARCGUAAAAAAACKoKy+CwAAAAAAaEgqKyvzwAO/z733/jYffDA1bdu2z/bb75CBAwelRYuWSZIXXvhnbrppdCZPfjMtW7bMDjvslJ/97Lg0b96iep533nk7N9xwXV588YUUCoVss812Oe64n6dDhzXqa2lAPbNTBgAAAADg/zFhwh25+urh6d9/u1x66YgcdNCh+dOfHsq5556RqqqqvP32Wzn55OPTqFHjXHTRZTnqqGPy8MP/lQsuOK96jhkzPs6QIcdm7ty5+cUvLs5pp52dl19+MSedNDjl5eX1uDqgPtkpAwAAAADwvyorKzNhwu35/vf3z7HHnpAk6dNn67Rp0yZDh56T119/Lf/zP48lKclll41I8+bNkyTl5RUZMeKyTJv2YTp37pIHHrg38+fPyy9/eVXatGmbJGnbtm2GDDk2zz33bLbeun89rRCoT0IZAAAAAID/NX/+/Oy++54ZMOB7Ndq7deueJJk69f0sWbI4ZWVladq0aXV/mzZtkiRz5sxJ585dst9+P8w222xXHcgkSaNGjZIkS5YsXrGLABosly8DAAAAAPhfrVq1ykknnZ5evTav0f73v/81SbLeeutnr72+nyQZOfLqzJkzO2+//VbGjbslPXpskA02+FaSpF27dunZc+MkyeLFi/Pyyy/lqqt+ma5d107fvv2KtBqgobFTBgAAAADgS7zyysv51a9uz7bbbp/1198gSTJ48M9z1VVX5J577kySdO7cJaNH35LS0tJaxx9xxEF5771306RJk1x66Yg0adK01hhg9WCnDAAAAADAF3jxxX/l1FN/nrXWWivnnDM0STJ+/G0ZMeLy7LvvAbn22hty4YWXpXnz5jnxxMGZOfM/teY45ZQzc9VVo7LVVn1zxhkn5emnnyz2MoAGQigDAAAAAPA5Hn304Zx00vHp1KlzrrnmhrRp0zbl5eW5/fYx2XXXPXLKKWdmq636ZOedv5drr70h//nPjPz61+NrzdOnz9bp27dfLr10eNZaq2smTLi9HlYDNARCGQAAAACA/+PXvx6fCy44N5tssmlGj74la6yxRpJk9uzZWbRoUTbddLMa49u1a59u3dbNO++8nSR5/vmJefLJx2uMKSsrS48eG2TGjI+LswigwRHKAAAAAAD8P+6773e5/vprM2DALrnyypFp2bJldV+7du3SunWbvPDCP2scM3v27Lz33rtZa62uSZI//emPueSSoVmwYH71mAUL5ufll19Kjx7fKs5CgAanrL4LAAAAAABoKP7znxkZOfKqdOmyVvbf/8d5/fVJNfq7dl07Awf+LFdfPTwtWrTITjvtkjlzZmf8+NtSKBTyk58ckiQ5+ODD89hjj+SMM07OwQcfnqVLl+RXv7o9CxYsyFFH/aw+lgY0AEIZAAAAAID/9eSTT2Tx4sX58MMPcvzxR9fqP+ecoTnggB+nZctWueuuX+Whhx5MmzZts9lmm1ffMyZJundfL6NH35Ibbxydiy/+RSoqKrL55lvm7LPPz3rrrV/sZQENhFAGAAAAAOB/7b33D7L33j/4ynG77bZndtttzy8ds+GGPXPVVSPrqjRgFSCUAQAAAAAarEKh5P9r777jezr//48/Y8aqUZSq0WoTIzLsiBm1KaJqRlujtEatilkzVpQi1N6z9hZ7rwqfomgqYoTauyUJOb8//HK+eSchofHOG4/77eZ2k3PO+5zrnOucc13XeZ3rOkqWzC6pk/FGiIw0FBlpJHUygLcaQRkAAAAAAAAANilZMjtlypRWyZMnS+qkvBGePInUnTv/EpgBkhBBGQAAAAAAAAA2KVkyOyVPnkwTFu7VpWt3kzo5r7Vc2TOqfRMPJUtmR1AGSEIEZQAAAAAAAADYtEvX7urcpdtJnQwA+M/o9wcAAAAAAAAAAGAFBGUAAAAAAAAAAACsgKAMAAAAAAAAAACAFRCUAQAAAAAAAAAAsAKCMgAAAAAAAAAAAFZAUAYAAAAAAAAAAMAKCMoAAAAAAAAAAABYAUEZAAAAAAAAAAAAKyAoAwAAAAAAAAAAYAUEZQAAAAAAAAAAAKyAoAwAAAAAAAAAAIAVEJQBAAAAAAAAAACwAoIyAAAAAAAAAAAAVkBQBgAAAAAAAAAAwAoIygAAAAAAAAAAAFgBQRkAAAAAAAAAAAArICgDAAAAAAAAAABgBQRlAAAAAAAAAAAArICgDAAAAAAAAAAAgBUQlAEAAAAAAAAAALACgjIAAAAAAAAAAABWQFAGAAAAAAAAAADACgjKAAAAAAAAAAAAWMFrEZS5c+eOfvzxR5UvX15FixZVkyZNdPjwYXP+/v375eXlJRcXF1WvXl3r1q2z+H1YWJgGDhwod3d3ubm5qVu3brp165a1dwMAAAAAAAAAALzFXougTNeuXXX06FGNHj1ay5YtU8GCBdWqVSudPXtWwcHBatu2rcqVK6fly5erYcOG6tGjh/bv32/+fsCAAdqzZ4/Gjx+v2bNn6+zZs+rUqVMS7hEAAAAAAAAAAHjbpEjqBMTn/Pnz2rt3rxYsWKBixYpJkvr166fdu3drzZo1unnzphwdHdWlSxdJUv78+XXy5ElNmzZN7u7uunr1qlauXKlJkyapePHikqTRo0erevXqOnr0qNzc3JJs3wAAAAAAAAAAwNvD5nvKZM6cWVOmTFGRIkXMaXZ2drKzs9O9e/d0+PBhubu7W/ymdOnSCgwMlGEYCgwMNKdF+fDDD/Xee+/pt99+s85OAAAAAAAAAACAt57N95R55513VKFCBYtpAQEBOn/+vHr37q0VK1YoR44cFvOzZ8+uhw8f6vbt27p69aoyZ86s1KlTx1rmypUr/yltKVI8P6aVPLnNx7xeO4l5TMmfxEXe2LbEOqbkTeLj2rFd5I1t475mu7h2bBd5Y9u4r9kurh3bRd7YNu5rtotrx3aRN7YtsY6pzQdlYjpy5Ih69eqlqlWrqmLFinr06JFSpUplsUzU3+Hh4Xr48GGs+ZKUOnVqhYWFvXQ6kiWzU+bM6V7693g577yTJqmTgGcgb2wb+WO7yBvbRd7YNvLHdpE3tou8sW3kj+0ib2wXeWPbyB/bRd7YLvLGtiVW/rxWQZktW7aoe/fuKlq0qEaNGiXpaXAlPDzcYrmov9OkSSN7e/tY8yUpLCxMadK8/EGMjDR0796/z10mefJkXEiJ7N69h3ryJDJR1kX+JC7yxrYlVv6QN4mPa8d2kTe2jfua7eLasV3kjW3jvma7uHZsF3lj27iv2S6uHdtF3ti2+PLnnXfSJKg3zWsTlJk3b558fX1VvXp1jRgxwuz9kjNnTl27ds1i2WvXrilt2rTKkCGDcuTIoTt37ig8PNyix8y1a9f03nvv/ac0PX6cOBcIEu7Jk0iOu40ib2wb+WO7yBvbRd7YNvLHdpE3tou8sW3kj+0ib2wXeWPbyB/bRd7YLvLGtiVW/rwWA8stWLBAgwcPVrNmzTR69GiL4Erx4sV16NAhi+UPHDigokWLKlmyZCpWrJgiIyMVGBhozg8JCdHVq1dVokQJq+0DAAAAAAAAAAB4u9l8UCYkJERDhw5VlSpV1LZtW924cUPXr1/X9evXdf/+fXl7e+vYsWMaNWqUgoODNWPGDG3cuFGtW7eWJL333nuqVauW+vbtq4MHD+rYsWPq2rWrSpYsKVdX16TdOQAAAAAAAAAA8Naw+eHLAgICFBERoc2bN2vz5s0W8+rXr6/hw4dr4sSJ8vPz0+zZs/XBBx/Iz89P7u7u5nKDBw/W0KFD1aFDB0lS+fLl1bdvX6vuBwAAAAAAAAAAeLvZfFCmXbt2ateu3XOXKV++vMqXL//M+WnTptWQIUM0ZMiQxE4eAAAAAAAAAABAgtj88GUAAAAAAAAAAABvAoIyAAAAAAAAAAAAVkBQBgAAAAAAAAAAwAoIygAAAAAAAAAAAFgBQRkAAAAAAAAAAAArICgDAAAAAAAAAABgBQRlAAAAAAAAAAAArICgDAAAAAAAAAAAgBUQlAEAAAAAAAAAALACgjIAAAAAAAAAAABWQFAGAAAAAAAAAADACgjKAAAAAAAAAAAAWAFBGQAAAAAAAAAAACsgKAMAAAAAAAAAAGAFBGUAAAAAAAAAAACsgKAMAAAAAAAAAACAFRCUAQAAAAAAAAAAsAKCMgAAAAAAAAAAAFZAUAYAAAAAAAAAAMAKCMoAAAAAAAAAAABYAUEZAAAAAAAAAAAAKyAoAwAAAAAAAAAAYAUEZQAAAAAAAAAAAKyAoAwAAAAAAAAAAIAVEJQBAAAAAAAAAACwAoIyAAAAAAAAAAAAVkBQBgAAAAAAAAAAwAoIygAAAAAAAAAAAFgBQRkAAAAAAAAAAAArICgDAAAAAAAAAABgBQRlAAAAAAAAAAAArICgDAAAAAAAAAAAgBUQlAEAAAAAAAAAALACgjIAAAAAAAAAAABWQFAGAAAAAAAAAADACgjKAAAAAAAAAAAAWAFBGQAAAAAAAAAAACsgKAMAAAAAAAAAAGAFBGUAAAAAAAAAAACsgKAMAAAAAAAAAACAFRCUAQAAAAAAAAAAsAKCMgAAAAAAAAAAAFZAUAYAAAAAAAAAAMAKCMoAAAAAAAAAAABYAUEZAAAAAAAAAAAAKyAoAwAAAAAAAAAAYAUEZQAAAAAAAAAAAKyAoAwAAAAAAAAAAIAVEJQBAAAAAAAAAACwAoIyAAAAAAAAAAAAVkBQBgAAAAAAAAAAwAoIygAAAAAAAAAAAFgBQRkAAAAAAAAAAAArICgDAAAAAAAAAABgBQRlAAAAAAAAAAAArICgDAAAAAAAAAAAgBUQlAEAAAAAAAAAALACgjIAAAAAAAAAAABWQFAGAAAAAAAAAADACgjKAAAAAAAAAAAAWAFBGQAAAAAAAAAAACsgKAMAAAAAAAAAAGAFBGUAAAAAAAAAAACs4K0JykRGRmrcuHEqV66cXF1d1aZNG128eDGpkwUAAAAAAAAAAN4Sb01QZuLEiVqwYIEGDx6sRYsWKTIyUq1bt1Z4eHhSJw0AAAAAAAAAALwF3oqgTHh4uGbMmKFOnTqpYsWKKlCggMaMGaMrV65o06ZNSZ08AAAAAAAAAADwFrAzDMNI6kS8aseOHVPDhg21ceNGffjhh+b0Jk2ayMHBQQMHDnzhdRqGocjI5x86OzspWbJkuvvgkZ48iXzhbeD/JE+eTBnT2ysyMlKJdcaSP4mDvLFtiZ0/5E3i4dqxXeSNbeO+Zru4dmwXeWPbuK/ZLq4d20Xe2Dbua7aLa8d2kTe2LaH5kyyZnezs7OJdX4pETJvNunLliiQpZ86cFtOzZ89uzntRdnZ2Sp48/gMsSRnT27/UNhBbsmSJ37mL/Ekc5I1tS+z8IW8SD9eO7SJvbBv3NdvFtWO7yBvbxn3NdnHt2C7yxrZxX7NdXDu2i7yxbYmVP2/F8GUPHz6UJKVKlcpieurUqRUWFpYUSQIAAAAAAAAAAG+ZtyIoY2//NBoYHh5uMT0sLExp0qRJiiQBAAAAAAAAAIC3zFsRlIkatuzatWsW069du6b33nsvKZIEAAAAAAAAAADeMm9FUKZAgQJKnz69Dh48aE67d++eTp48qRIlSiRhygAAAAAAAAAAwNsiRVInwBpSpUql5s2ba9SoUcqSJYty5colPz8/5ciRQ1WrVk3q5AEAAAAAAAAAgLfAWxGUkaROnTrp8ePH6tu3rx49eqQSJUpo+vTpSpkyZVInDQAAAAAAAAAAvAXsDMMwkjoRAAAAAAAAAAAAb7q34psyAAAAAAAAAAAASY2gDAAAAAAAAAAAgBUQlAEAAAAAAAAAALACgjIAAAAAAAAAAABWQFAGAAAAAAAAAADACgjKAAAAAAAAAAAAWAFBmSTi7e2tnj17xjmvZ8+e8vb2TrRtOTo6avny5ZIkwzC0YsUK3bx5U5K0fPlyOTo6Jtq28HyGYWj58uXy9vZW6dKl5eTkpCpVqsjX11fXr19PtO1cvnxZ69atS7T12ZoHDx7IxcVFZcqUUURExAv/fvv27Tpz5swrSFnCxXedh4aGytHRUQcPHpT0/HtGUnj8+LFmz54tLy8vubm5qXTp0mrZsqUOHDhg1XQEBgbq8OHDr3Qbt2/f1pIlS8y/rZEX3t7ecnR0jPPfiBEjXvm2n7d/np6eFukpUKCAihYtqubNm+u33357pWl71RK7/H1VPD09NX78+KROBhIg6nqZOXNmnPN//PFHOTo6/qf8jH4+UK+zFPN+5eTkpGrVqmnatGmJup3n3TdjludIuLjKQicnJ1WsWFGDBg3Sw4cPE7yuF637Rc/Tt+W6il4Gjh8//pn1kKh/oaGhca7n7Nmz6tKli9zd3eXk5CRPT08NHDhQN27cSNT02kJ93ha8bLto/Pjx8vT0TPDyERERmjVr1kv//k1hzec4z3Pw4EGL6zBme+VFfx9zv17n6+u/PiuIKeax+q9iHmtrtGdfVz179oy3LPovop/nMfP5bW9vJeR5T/RnzdaS2HWypM5ngjJvmd9++009e/Z8oUYMEkdkZKTat2+v4cOHq1KlSpo7d642bdqkvn376vjx42rQoIEZLPuvfHx8tHv37kRZly1at26d3n33Xd2/f1+bN29+od9eunRJ7dq1S7Rj/arkzJlTe/bskZubW1InJZawsDC1aNFCs2bNkre3t1asWKFZs2Ypf/78+vrrr7VmzRqrpaVp06a6cOHCK93GyJEjtXr1avPv8ePHq0+fPq90m5JUo0YN7dmzJ9a/9u3bv/Jtx6dly5Zmenbt2qVFixYpffr0at26tS5fvpzUyXtpffr0easr33g1UqZMqYCAgFjTHz9+rE2bNsnOzi7RtlWzZk3t2bMn0db3Joh+v9qwYYM6deqkCRMmaP78+VbZvi2X56+DmGXhunXr1KZNG/36668JfknhZep+1irrbVX062bPnj3KkSNHrGk5c+aM9bsbN26oadOmsre317Rp07Rx40YNHDhQhw8flre3t8LDwxMlfa9Lfd4aXrZd1LJlSy1dujTBy69du1bDhg176d8jcbm5uVlchzHbKy8q+j3vdb++/suzgrjEPNaJzRrt2ddVnz59LModSerdu3esaS8j5nn+qvP5dWJLz3tietPaOimSOgGwLsMwkjoJb61Zs2Zp586d+vXXX1W4cGFz+vvvv69SpUqpVq1amj59unr06JGEqXw9LFu2TOXKldPly5e1aNEi1axZM8G/fV2ugeTJkytbtmxJnYw4jR07Vn/++afWrl1rUWnp06ePHjx4oCFDhsjT01Pp0qVLwlQmnpjnTKZMmayyXXt7e5s9B9KmTWuRtuzZs2vgwIEqX768Nm/erC+//DIJU/fyMmTIkNRJwBvI3d1du3fv1pUrV5QjRw5z+oEDB5Q2bVqlSZMm0bZlb28ve3v7RFvfmyDm/Sp37tw6ePCgli1bpmbNmr3y7dtyef46iKsszJs3r06cOKH169drwIAB8a7jZep+1irrbVW6dOks6nHJkyePdS3FZePGjXr8+LGGDh1qBpw/+OADvf/++6pZs6Z2796typUr/+f0vS71eWt42XZRzDyOT8xj/qK/R+JKlSqVxfX4X6+J6Pe81/36+i/PCuIS81jDejJkyBCrfZYhQ4ZEyY+Y5zn5/H8S+rwnKbxpbR16yti4+/fvq1+/fipdurSKFSumFi1a6Pjx4+b8yMhITZ48WdWqVZOTk5OKFi2q1q1bxxlpP3jwoFq0aCFJqly5skU3s+XLl+vTTz9VkSJF5OXlpd9//92cFxERobFjx6pSpUpycXGRl5eX9u7da87fsmWLGjZsKFdXV/P30XtpeHt7q1+/fmrYsKGKFy9uvsGxbNky1ahRQ87OzqpRo4Zmz56tyMjIxDt4NsQwDM2bN0+fffaZRUAmir29vebMmaPOnTtLkv7++291795dHh4ecnV1VatWrXT69GlJ/9etMjg42GIdLVq0UPfu3eXt7a1Dhw5pxYoVb2SX8uDgYP3+++/y8PBQ1apVdfDgQYWEhJjz4+p+GDUtNDTUbAi2aNHCXC44OFjt2rVTqVKlVKxYMXXq1EmXLl0yf+/t7a0RI0aoe/fucnNzU9myZbVw4UIFBgaqbt26cnFxUePGjXXu3DnzN8/LwyiPHz/W4MGDVbRoUZUqVUqDBg1SWFiYpPiHOwkODlabNm3M9HTr1i1Rh8B7loiICC1btkxeXl5xvkXSuXNnTZ06Vfb29rpz544GDhyoChUqyNnZWY0bN7bYn/Hjx+urr77SlClTVL58eRUpUkTNmze3OLd37twpLy8vubi4yN3dXT179tTdu3clyey22qtXL/Xs2dM8ZpMnT5aHh4cqV66sBw8exNmtNua03bt3q1GjRnJxcVH58uU1ZswYPXnyRD179tSKFSt06NAhc3sxu5wfPXpULVq0ULFixVSqVCn16tVLt2/fNud7enpq+vTp6tixo9zc3FSqVCkNGTJEjx8//i9ZoSdPnmjWrFmqVq2aihQpomrVqmnhwoUWy8R3boeHh2vo0KFyd3dXsWLF5Ofn99L34RQpnr7nkSpVKklP93vEiBGqWbOmSpUqpUOHDiUozefPn9e3335rHs+uXbtavKkXX9mxcuVK1apVS0WKFFG5cuXk6+trvpn75MkT+fn5qUKFCnJyclL16tUtth992ImDBw+qUKFC2rlzp2rXrm0uv2XLFos8GDNmjMqWLStXV1d16tRJvr6+SToEWnh4uEaMGCFPT085OTmpZMmS+v7773Xr1i1FRkbK3d3dYiit2bNny9HR0aJu0bFjR/Xp00ft27c36w1Rzp49K0dHR/3111+Snnb79/LykrOzs6pUqaKff/7Z4k1oR0dHjRs3TpUqVVLZsmUt7pNvC2dnZ73//vvauHGjxfT169erRo0aFj1ljhw5ombNmsnZ2VkVK1bUwIED9eDBA3P+/fv35ePjo+LFi6t06dKxhkWL2aU/KChIbdu2VYkSJeTk5KTKlStrxowZr2hPXx/RG3N3795V3759Va5cORUuXFju7u7q27ev2as8IfeC6B4/fqxOnTqpYsWKunDhQpzDkY4aNUq9e/dW8eLFVbRoUXXr1s0in0+cOKFmzZrJxcVFlStX1urVq1WoUCGGQIsmderUZrkTHh4uPz8/lStXTm5ubvriiy/MtyifVfdLSPvFloaNfV3Y2dnpn3/+iTWcaf78+bVu3TqVLl3anBZf+fHPP/9o8ODBKlu2rNzc3NS8eXOdOHHiP9Xn42qTvs6e1y7y9vY225ZRfvvtNzk6Our8+fOxhh87fPiwWrRooaJFi8rJyUk1atTQqlWrJD0tW3r16iVJ5v0s5u/ja/v07NlTPXv21IgRI+Tu7i4XFxe1bdtWV69efVWHJ0nF1w56+PCh+vTpIw8PDxUpUkT16tXTpk2bzPne3t7y9fVV165dzfbJlClTzAfJ0Ydaiqu9El/ZFlPUPS+u66tevXpm/kfZvXu3ihQpojt37iTmYfvP4ntW8PDhQ/Xv31+lSpVS0aJF1adPH3Xr1s1iuMoqVapoyJAhKlasmL777rtYw1o97/lYXEOdPW/4s2e1Z6OfKzGn9ezZU506dVLLli1VtGhRTZ06VVL899Q30fP2OSAgQI6Ojha91bt166ZKlSrp5MmTsc7z+Iapi6+O/qZ4kec9khQSEqKvvvrKbHtPnjzZYvmEtBUXL16spk2bqkiRIqpRo4aOHDmixYsXq2LFiipatKg6d+6sR48eSYrd1nlWXSHKkiVLVKdOHTk7O8vV1VVNmza1aPcmNYIyNswwDLVp00YXL17U5MmT9euvv8rV1VVNmjTRyZMnJUlz5szR9OnT1bNnTwUEBGjChAk6d+6chg8fHmt9bm5uZqV1yZIlFm8M/Prrrxo9erSWLVumVKlSWVTgfH19tWjRIvn4+GjNmjUqV66c2rVrp7Nnz+rEiRPq2LGjatWqpTVr1ujXX39VlixZ1KNHD4sLbcmSJWrRooUWLFigcuXKafHixRo5cqQ6dOigdevWmRf2qFGjXtHRTFqhoaG6dOmSypQp88xlcuXKpVSpUunBgwdq0qSJrl69ql9++UWLFi2Svb29mjdvrkuXLqlkyZL64IMPLLoMXrlyRb/99pu8vLw0fvx4ubm5qUaNGm9kl/KlS5cqbdq0Kl++vKpUqaKUKVNq0aJFCfptzpw5zbF2x48fr5YtW+rSpUtq1KiRUqVKpdmzZ2vGjBm6fv26mjdvblHIzp07VwULFtTq1atVuXJlDRkyRAMGDFDv3r01b948Xbt2TT/99JMkxZuHUY4cOaKbN29q8eLFGj58uAICAuTn5xfvfly9elVNmzZV3rx5tXTpUk2aNEkPHjxQo0aN9O+//77I4XxhFy9e1J07d1S0aNE457/33ntydnaW9HRYg8OHD8vPz0/Lly+Xg4ODWrVqpWPHjpnLHz58WIGBgZoyZYoWLFigmzdvauDAgZKkW7duqUOHDmrQoIHWr18vf39//fbbbxo5cqQkWXRhjj7EyIoVKzR79mz9/PPPSp8+fbz7dPToUX3zzTcqVqyYli9friFDhmjRokWaOHGi+vTpoxo1apjdmWM6duyYvL299cknn+jXX3/V2LFj9fvvv6tVq1Z68uSJudzYsWNVokQJrV69Wj169NC8efO0du3aeNP2PMOHD9fEiRPVoUMHrVmzRs2aNZOvr6853ndCzu0hQ4Zo/fr1Gj58uBYtWqQrV6681JjGV69e1aBBg5Q2bVpVqFDBnD5v3jz17dtX06ZNk6ura7xpvnfvnpo1a6bw8HDNnj1bM2fO1IULF8wyKb6y4/Tp0+rbt686duyogIAADR06VKtWrTK/H7FgwQJt3LhRY8aMUUBAgJo3b64BAwY8c5+jgjh9+vTR2rVr5eDgIB8fH/3zzz+SpFGjRmnx4sXq37+/li1bpmzZsmnu3LkvfPwS08iRI7Vp0ybznjJ8+HAdOHBAv/zyi5IlS6YKFSpYvFixb98+2dnZmQ29iIgI7d27V5UrV5aXl5cOHTqkv//+21x+5cqVKlKkiD755BPt2rVLnTt31hdffKG1a9eqf//+2rBhg3744QeLNC1YsEDjxo2Tv7+/8uXLZ5XjYGtq1KhhEZQJDw/Xli1bVKtWLXPa6dOn9fXXX6tcuXJavXq1Ro0apT/++EMtW7Y0H8J07txZx44d06RJkzRz5kzt2LHDolyJ7uHDh2rZsqUyZcqkRYsWae3atapevbpGjBihU6dOvdodtmHHjh3T2rVr1bBhQ0lPH26cPHlS/v7+CggIUK9evbRy5UotXrzY/E1894Loy/Xo0UMnTpzQ3LlzlSdPnjjTMGvWLGXNmlVLly6Vn5+ftm7dat4Hr169qi+//FK5cuXSsmXL9OOPP5ovCuBp0GvHjh1atWqV6tatK+npw6y9e/dq1KhRWrFihWrUqKF27dppx44dcdb9Etp+wYurVauWcubMKW9vb9WrV0/Dhw/Xli1b9ODBA3388cdmz4qElB+dO3fWrl27NGzYMK1cuVK5c+dWy5YtlS5dupeuz8dsk77untcu8vLy0vbt2y32f/Xq1SpatKjy5s1rsZ6rV6+qVatWKlKkiFasWKGVK1fK2dlZffr00Y0bN1SzZk317t1bkuIcjjGhbZ+1a9fqzp07mjdvnqZOnao//vhDP//88ys6OknnyZMn8baDot5EnzJlitavX6/y5curS5cuFg+EFy5cqAwZMmj58uXq0qWLJkyYYD6Ajy6u9kpCyra4xHXP9PLyUkBAgPlQVHpaH/T09LS5XoXxPSvw8fHR3r17NWbMGC1atEj379+P9S3eCxcu6Nq1a1q5cqW6dOkSaxvPez72op7Vno1PQECAypQpo2XLlql27doJrpO/SeLb52rVqqlu3boaPHiw7t69q7Vr12rDhg3y8/OTo6NjrPP8eRJSR39TJPR5T/LkySU9be/Xq1dP69evV5MmTTR69Gjt379fUsLKekkaM2aMWrdurVWrVilDhgxq166dAgICNGXKFA0bNkxbtmx55jeznlVXuHv3rjZv3qxBgwapdevW2rBhg2bNmqWwsDD17ds3EY/Yf2QgSTRv3twoVKiQ4erqGutfoUKFjObNmxv79u0zHB0djdu3b1v8tlmzZoaPj49hGIaxdetWY9u2bRbz/fz8jMqVK5t/Ozg4GMuWLTMMwzAOHDhgODg4GBcvXjQMwzCWLVtmODg4GGfOnDGX37Rpk+Hg4GDcuHHDuH//vlG4cGFj0aJFFtv46aefjN9//904efKkMX/+fIt5O3fuNBwcHIzLly+b+1qvXj2LZcqXL2/MnDnTYtrSpUuNIkWKGI8ePUrIIXytHDlyxHBwcDD27NljMb1t27YWeV+zZk1j/vz5hrOzs3Hz5k1zuYcPHxoeHh7GiBEjDMMwjPHjx1vk8dSpU40KFSoYT548MQzj6TGPOkfeJBEREUaZMmWMrl27mtPatm1rlCxZ0jxvKlWqZIwbN87id9GnXbx40XBwcDAOHDhgGIZhjBw50ihXrpwRFhZmLn/t2jWjSJEixrx58wzDeHo8P//8c3N+UFCQ4eDgYCxevNicNnLkSKNq1aqGYRgJykMfHx/Dw8PD4nxfvHixUbhwYePff/+Nlc7oeTpmzBjjs88+s9jHf//913B2djav9Vcl6lzeu3fvc5fbsWOH4eDgYPz555/mtMjISKNevXpGp06dDMMwjHHjxhmOjo7GnTt3zGVmzZplFC5c2DAMwzh58qTh4OBgcY8LCgoyTp06Zf4d/f4Wdcxmz55tkZboy8Q1rUuXLkajRo0s5m/cuNG8t/n4+BjNmzc350XPi++//97w8vKy+O2pU6cMBwcHY8eOHYZhPD3/vv32W4tl6tata/Tr1y+OI/d/24irjGjVqpVhGIZ5b547d67F73x9fQ13d3cjMjIy3nM7ah2//vqrOf/Ro0dGmTJlnnv/qFSpklG4cGEzTU5OToaDg4NRo0YNc5+jlmvfvr35d0LSvHDhQsPV1dXinDh16pQxatQoIywsLN6yY/PmzYaTk5Nx7Ngxc/6xY8eMs2fPGoZhGEOGDDFq165tXL161Zy/d+9e48aNG4ZhWOZ1VHm5efNmi7Q4ODgYR44cMa+5hQsXmvMjIyON+vXrW5wvr0Jc97koK1euNH777TeLaZ07dzZatGhhGIZhBAQEGC4uLkZYWJgRHh5uuLq6Gt99953RunVrwzAMY9++fYaLi4vx8OFDIyIiwvDw8DAmTZpkGIZhPHnyxChfvrx5b2zSpIkxZMgQi23t37/fop7h4OBgDB06NPF2/jUTlVfHjx83HB0djStXrhiGYRjbtm0zqlWrZrFM9+7dY90rLly4YJYFwcHBhoODg7Fv3z5z/vXr1w0nJyfzfIiq1xmGYdy8edOYPHmy8eDBA3P5R48eGQ4ODsaKFSte5W7bjJj3q8KFCxsODg5Gw4YNjfv37xuGYRhz5841Tp8+bfG7hg0bGr169TIMI/57gWE8vWf/8MMPxg8//GB4enoaoaGh5rJxled169a12N53331ntGzZ0jAMwxg7dqxRoUIFIzw83Jy/ZcsWi3W8LeIqCwsUKGB4enoa48ePNyIiIoxz584ZDg4OxsmTJy1+26NHD/NeHDMPEtp+iSoLo19Xb7KY9Z3onlfuxHT79m1j9OjRRvXq1Q0HBwfDwcHBcHZ2Nvz9/c1l4is/ou53u3fvNuc/evTIGDp0qBESEvLS9fmYbdLXWXzton/++cdwdXU17/dhYWFGiRIlzHrfuHHjjEqVKhmGYRjnz583pk6dakRGRprrOnPmjOHg4GDWKWJeB9F/n9C2T+nSpS3ubb6+vmb76XWRkOc4CWkHffvtt0aLFi2Mu3fvGoZhGI8fPzZ27dpl3Lt3z9xO7dq1LfLEz8/P8PDwMCIjI2M914l5/Sa0bIv6ffR7Xszr69atW0bhwoWNNWvWGIbxtE7v7OxsUe+3BfFdE1F1ql27dpnzHz16ZHh4eMS630dvb0Y/VvE9H4t5XGP+3jBiP6uJqz0bvbyPOc3Hx8coUaKExfYTUid/E0Q/VgnZ5/v37xsVK1Y0OnToYBQvXtwYP368uWzM4xozn6KXe/HV0d8kCX3eYxhP82PkyJEW04oVK2ZMmTLFMIyEtxWjr2PevHmGg4ODERISYk77/PPPzWcn0cui+OoKhw4dMlatWmWx/QULFhgFChQw/36R+s2rwDdlkpCnp6e6d+8ea/qoUaN0584d/fHHHzIMQ5UqVbKYHx4ebg5x5Onpqd9//11jx45VSEiIQkJCdObMGb333nsvlJbob66+8847kqRHjx7p8uXLioiIkIuLi8XyXbt2Nf+fMWNGTZkyRWfPntX58+fNbsrR3+iL/jbOrVu3dOXKFY0ePVpjx441p0dGRiosLEyhoaHKnz//C6Xf1mXOnFmSzGGXogwcONB842Tu3Lnatm2bgoKClC9fPmXJksVczt7eXs7OzgoKCpIk1atXT/7+/jp69Kjc3Ny0evVq1a1bV8mSvdmd33bu3KkbN25YvFlcq1Ytbd++XRs2bFC9evVeeJ1BQUFycnIyh1ySpGzZsunDDz80j7cki7ddo8b/z507tznN3t5eERER5jrjy0NJcnJyUurUqc2/nZ2dFRERoXPnzj332xYnT57UX3/9FesttbCwsFjD2iW2qH2Kr6t6UFCQMmTIIAcHB3OanZ2dihcvbtHjJGvWrMqYMaP5d4YMGczjWLBgQdWuXVvt2rVTtmzZ5OHhoYoVK6pKlSrP3XbMt//iExQUJA8PD4tp1apVe+nfFihQQBkyZNCff/5p9hqJeU+Lvp/PElcZEdVN+OzZs4qIiFCxYsUs5pcsWVKzZ8/WzZs34z23Q0JCFBERoSJFipjzU6dOrUKFCsW7340bNzaH6EqWLJkyZcoU5zkbPS8SmuZ8+fJZnBMFChRQgQIFElR2RA1d8/nnn+uDDz4wh7FzcnKSJDVr1kxbtmxRhQoVVLBgQXl4eKhWrVp69913n7mvH330kfn/qJ5XERERCg4O1qNHj+Tq6mrOt7OzU7FixWINVWhNdevW1b59+zRq1CidO3dOZ8+eVUhIiIoXLy5J8vDw0JMnTxQYGKgUKVIoXbp0atSokb7//nvzDXQPDw/zXPvss8+0atUqtW3bVgcOHNCtW7dUu3ZtSU/vRceOHbPolWn8/7fFgoOD9cEHH0h68WvyTeTk5KTcuXMrICBALVq00Pr16y3KMunp8Tx//nycH4QPDg42h0WMfs1mzZrVoiyKLkuWLGratKnWrl2rkydP6sKFC+a5+aYOFxuX6Perx48f6/z58xozZoyaNWumJUuWqGnTptq2bZtWrFihc+fO6cyZMwoNDbW49qVn3wuibNiwQREREcqfP3+8Y5LHXHeGDBl07949SU/PAycnJ6VMmdKcX6JEiZfY8zdDVFloGIaOHTsmX19flSlTRu3atVOKFCnM0QOaNm1q8buIiAizTRNTwYIFE9R+wcvJlCmTunTpoi5duujatWvav3+/lixZonHjxilz5sxq2rRpvOVH1BBL0cvY1KlTm0MoxRxeJqH1+TepPEpIu6h69epas2aN6tWrp507dyo8PFw1atSIta48efLIy8tLc+bMUVBQkEV5kZBrIqFtnzx58ljc2xJSH7ZF8T3HSUg7qE2bNmrXrp3c3d3l7OwsDw8P1alTx6I+XapUKYshTt3c3DR16lSLYZKfJaFlW0JkzpxZlStX1sqVK1W7dm1t2LBBGTJkUNmyZV94Xa9SfNdEVBs+ej0rderU5igP0T2rZ3dU++lZz8esNcxozHtZQuvkb5KE7HP69Ok1bNgwffnllypcuLC+/fbbl97W8+ropUqVermdsEEJfd4TJea18s4775jPq1+mrRh1nUZ//mZvbx9nT+ao8uVZdYV8+fIpODhYEyZMMOt7f/75p021gwjKJKF06dLFWTFMly6d7ty5o8jISKVPnz7WtxCk/xuzf8qUKZowYYLq168vd3d3ffXVV9q6dWusLpjxiep6Fp1hGBaVprgcOnRIrVq1UsWKFVWsWDHVqVNHDx8+VPv27S2Wiz52d9QF0KtXrziH84pr3MLXXe7cuZUtWzYdPHjQYti46MGzqIeQxjO6P0ZGRppjZ3/wwQcqWbKk1qxZo3Tp0unPP/+M9R2VN1HUtdChQ4dY8xYtWvTMoMzzvt3xvOMd/fyP61p4VhAsIXkoxb7uoq6N6A3KZ62ndOnS6t+/f6x5r/pD5blz51bWrFl15MiROD+aGBwcLF9f32d2dzUMw+IYxLevP/30k9q3b69du3Zp3759+uGHH1SsWDHNnj37mb+J78NvMc+H6Ol5Uc/K65j3z7j281m/jfKsMuJ5v406h1KkSBHvuR3VyIu5XEKOR8aMGRP0YCN6XiQkzc/bdkLKjlSpUmnOnDk6efKk9uzZoz179qhdu3aqV6+ehg0bpnz58mnTpk06dOiQ9u7dqx07dmjq1KkaNmyY6tevH+d2n5V3UWmNLx+t7ccff1RAQIDq1asnT09PtW/fXtOnTzfHbE+XLp1KlSqlvXv3KmXKlCpVqpSKFy+uiIgIHT9+XDt27FDbtm3N9TVo0EDTp0/XiRMnzOEbo8qryMhItW7dOs5jF/2h9Jv0Mcb/ImoIs0aNGmnr1q2xuuFHRkaqTp06ateuXazfZsmSRfv27TOXi+5Z183169fVqFEjZcmSRZ6enipbtqyKFCliMcTg2yDm/Sp//vzKmDGjmjZtqn379mn+/Pn666+/VLt2bdWsWVOFCxdWv379Yq0nvvt49uzZNXr0aLVs2VL+/v4WLzAlZF1RkidPblONxaQWvSzMly+fsmfPrq+//lrJkyfXgAEDzDyYP39+rI+OP6ueltD2C17clClT9MEHH5h1xOzZs6tu3bqqU6eOGjVqpJ07d6pp06bxlh9R97uESmh9/k0qjxLSLvLy8tKXX36pGzduaM2aNfr000/jHNr3zJkzatq0qQoXLqwyZcqoatWqypw5sznMY3wS2vaJr97/uojvOc7z2gdRx8PNzU07d+7U3r17tX//fq1cuVK//PKLpk2bJnd3d0mxy/eosiGuZzcxl2vbtm2CyraEatCggdq1a6ebN2+aL4TGlw5ri++aaN26taSEvZjyrHtFfM/H4vJfg/1x/T5m+hJaJ3+TJHSfT5w4oRQpUigkJESXL19+5stM8W3reXX0N0lCn/dEBT6e9SxZSngexdWWScgL5/E9t1izZo169uypOnXqqGjRomrcuLGCgoI0aNCgeNdtLW/2a/WvOQcHBz148EARERHKmzev+W/q1KnaunWrJGnSpElq3769BgwYoEaNGsnV1VXnzp17ZkUg+psWCZE3b16lTJky1oeQvvjiC82aNUszZsxQqVKlzA92e3h4mGPPPysN7777rrJkyaKLFy9a7NebOqas9PRG1aJFC61cufKZb1BHHTdHR0edO3fO4sPWYWFhOnHihD7++GNzmpeXlzZt2qR169bFOTbwm+bmzZvmR99Xrlxp8a9BgwY6evSogoKClDJlSouxkx88eGBxLGNeA1EfuI4eeb9x44bOnz//0j22EpqHp06dsqgUBgYGyt7ePt6KwieffKLg4GDlzJnTvH4yZsyooUOHWryN9iokS5ZMn3/+uZYvX27xnYko06ZN0/Hjx+Xk5KT79+9bpMcwDAUGBlocg+f5/fffNXToUH300Uf66quvNGXKFA0dOlQHDhywOLbxiXlOnD9/3mJ+/vz5Y93jZs+ebTZEn3ffdHR0VGBgoMW006dP68GDB6+0x1/+/PmVMmXKWNs+fPiwsmXLpowZM8Z7bn/44YdKnTq1jhw5Ys5//PjxK+vlkZA0f/zxxzp37pzu379vzv/jjz/k7u6uiIiIeMuOnTt3yt/fX4UKFdI333yjOXPmqFOnTlq/fr2kp99h27Rpkzw8PNSjRw+tWbNG7u7u5vwXkTdvXtnb2+t///ufxfTff//9hdeVWG7fvm1+46ZXr17y8vJSwYIFdfbsWYsyuVKlStq7d68OHjwod3d3pU2bVq6urlq8eLEuXryoihUrmsvmz59fbm5u2rBhg7Zu3SovLy9z3ieffKKQkBCL/Lhy5YpGjhwZ61sbkPnhymXLlil37tyx7hGffPKJzpw5Y3E8Hz9+rGHDhunvv/9WwYIFJcnimr13754uXLgQ5/aixu5fuHChvvvuO1WpUsXssWtrwURri9r/EydOaNeuXRo7dqy6d++uzz77THny5NGFCxde+BiVKFFCLi4u6t69uxnIfBkFChTQyZMnLd4eP3r06Eut601UunRpff3111q4cKF27dqlTz75RNLTIGT0a2f58uXmA7qY5fjLtF+QMMeOHdMvv/wS6wWYZMmSKX369GbP1PjKj6j7Y/T62ePHj+Xp6amNGzdapT5vyxLaLipevLhy5cqlVatWaceOHRZleHSLFi3Su+++q5kzZ6pNmzaqUKGCbty4Ien/ron46sMJafu8LRwdHeNtB40bN06BgYGqXLmy+vbtq4CAALNHbZSY7ZMjR47ogw8+sOhRHiV6/pw6deo/lW1x5XXZsmWVLVs2/frrrzp8+PAzz6WkkpBrInfu3LKzs7Oou4eHh+uPP/5I8Hbiez4WFbSJ3vY8d+5cgtf/sr9/G+vkCdnn06dPa+zYsRo4cKAKFy6sHj16mM9fXuTZaHx19DdJQp/35MqVK951verzMr66wpQpU/T5559r+PDhatasmUqUKKGLFy9Ksp36HkEZG1auXDkVLFhQXbp00YEDB3T+/HkNGzZMy5cvN0++nDlzau/evTpz5ozOnj2rMWPGaNOmTc/8SGXatGklPb05JeQiSJMmjZo3b66xY8dq69atunDhgkaPHq2goCCVL19eOXPm1J9//qnDhw8rNDRUy5YtM4eVeVYa7Ozs1KZNG82dO1fz5s3ThQsXtHnzZg0YMED29vZvzBs0MbVu3VqVKlVS06ZNNWnSJJ0+fVqhoaHatm2bWrZsqWXLlql06dKqU6eOMmXKZH7M9/Tp0+revbv+/fdfNWrUyFxftWrV9O+//2ru3LmxKkXp0qXTpUuXdOXKFWvv5iuzevVqPX78WG3atJGDg4PFv3bt2ilZsmRatGiRXF1dtX79eh05ckRnzpxR7969LaL3UddAUFCQ7t+/ryZNmuiff/7RDz/8oNOnT+vYsWP6/vvvlTlz5lhDyyRUQvPw77//Vu/evfXXX38pICBA48ePV+vWreO9Bpo2bar79++re/fuOn36tE6fPq0uXbro+PHjFt3kX5V27dopX758atq0qVauXKkLFy7o2LFj5gckBw8ebN6/unXrpkOHDik4OFiDBg1SUFCQvvzyywRtJ3369FqwYIH8/Px0/vx5BQUFaf369cqXL585JGDatGkthvWJi6urq5YsWaJTp07p5MmTGjBggMUxbt26tf73v/9p7NixOnfunHbu3KmJEyeaD6bTpk2ra9eumQV4dF9//bX+/PNPDR48WMHBwTp48KC6d++uQoUKmW+5vQrp06dXo0aNNG7cOK1du1bnz5/X/PnztWDBArVs2VJ2dnbxntvp0qVT8+bNNW7cOG3atEnBwcHq37+/2aMiKdJcp04dZcyY0UzziRMn1L9/fzk4OChnzpzxlh0pU6bUhAkTNGvWLF28eFEnTpzQjh07zK7mt27d0qBBg7R161ZdunRJu3fv1qlTp+Lsih6fNGnSyNvbW+PGjdOWLVsUEhKiESNGWC0oc/78ee3atcvi319//aUMGTJo69atZvfsfv366Y8//rAokz09Pc1zIuo8LV26tFatWiU3N7dYb3w1aNBA8+bNk729vcVwfW3atFFAQID8/f0VEhKi/fv3q1evXrp///4b+1bef1GwYEHlzZtXP/30U5zlS8uWLXXy5EkNHDhQwcHBOnr0qLp166Zz584pX758ypMnj6pXr65BgwZp3759CgoKeu6HyXPkyKGHDx9q48aNunz5svbs2WP23nibPmb+77//6vr167p+/bquXbumw4cPa+jQocqePbsaNmyoFClSaMOGDbp48aKOHz+uzp076/r16y99jBo3bixnZ2f16tXrpdbRtGlT3bt3T/369VNwcLD27dunwYMHS3rxl6veVN9//73y5cunAQMG6P3331elSpXUv39/bdu2TRcvXtTUqVM1efJkc+iLmHW/l2m/IGHat2+v0NBQtWrVSnv27NGlS5d09OhRDR8+XP/73//09ddfS4q//Pjwww9VtWpVDRw4UAcOHFBISIj69eunsLAwlSxZ0ir1eVuW0HaRnZ2d6tWrpwkTJihLliwqXbp0nOvLkSOHrly5op07d+rSpUvatGmTBgwYIOn/romoY37ixAmLD75LCW/7vC3Kli0bbzvo4sWL6t+/v/bv369Lly4pICBAly9ftqiTHj58WOPGjdO5c+e0dOlSzZ8/3+ztEVP09krWrFn/U9kW8/qSnj6orVevniZNmqQiRYrYXLAzIdfE4sWLVaNGDQ0ePFj79+/XmTNn1KdPH125ciXB5Wt8z8ccHByUNm1aTZkyRRcuXNDu3bs1c+bM564zens2e/bsypUrl2bPnq3g4GAFBgZq7Nix8abvbayTx7fP4eHh6tGjh0qWLKnPP/9cQ4YM0cmTJzV16lRJcZ/nzxJfHf1Nk5DnPVHH73le9XkZX10hZ86cOnLkiP744w9duHBBs2bN0rx58yTZTn2P4ctsWPLkyTVjxgz5+fmpc+fOevjwofLnzy9/f3/zIcrIkSM1aNAgNWjQQOnSpZOLi4sGDhyoAQMG6PLly3r//fct1ung4KAKFSqoc+fO6tq1qzJlyhRvOrp27arkyZOrf//+un//vgoUKKApU6boo48+UqdOnXTjxg2zG9/HH3+soUOH6ocfftDx48efWVi3bNlSqVOn1ty5czV8+HBlzZpVX3zxhTp16vTfDpoNS5YsmX7++Wdt2LBBy5Yt05w5c3Tv3j1lzZpVxYsX17x588wxw+fNm6fhw4frq6++kiQVK1ZMCxcutOhBkSZNGtWoUUPr1q2LNTZw48aN5ePjo88++0z79++3ua7FL2P58uUqU6ZMnOPg5smTR59++qlWr16tdevW6c6dO/r666+VIUMGtWzZ0hynXXo6Jm6DBg00cuRInT9/Xn379tW8efPk5+enRo0aKVWqVPLw8JCfn98zxyKPT4YMGRKUh5UrV1by5Mn1xRdfKE2aNGrSpIm+++67eNefO3duzZs3Tz/99JOaNGmi5MmTq2jRopozZ45Vus+mSZNG8+bN04wZMzR16lRdvnxZ9vb2KlSokObOnWt+u2LGjBkaMWKEOnTooPDwcDk5OWnWrFkWY34+T/78+TV+/Hj5+/trwYIFSpYsmUqXLq2pU6ea3VlbtmypadOmKTg4WH379o1zPQMGDNCAAQP0xRdfKHv27Pr+++8tApYFCxbUhAkTNG7cOE2dOlXZs2dXixYtzDFn69Wrp82bN6t27dratGmTxbpdXFw0bdo0/fzzz6pXr57Sp0+vTz/9VN26dXup7u0volevXsqcObNGjRqlGzduKF++fPrxxx/1xRdfSHo6zGF853a3bt2UOnVqDRo0SP/8849q1KghT0/PJEtzmjRpNH36dA0bNkyNGzeWvb29KlasKB8fH0nxlx1lypSRr6+vZsyYoTFjxsje3l4VKlRQz549JT0dziAiIkJDhgzR9evXlS1bNjVp0sRiuK4X8f333ysiIkJ9+/bVw4cPValSJVWuXNkcR/dVWrNmjdasWWMxLVeuXBo7dqyGDx9uBrhKlSqlrl27avLkyXr48KHSpEmjnDlzytHRUQ8ePDDfcipTpozGjx+vypUrx9pWVAO2Xr16FuVJ9erVNWbMGE2ePFmTJk1SpkyZnjnOOp6qUaOGfvnllziHA3B1ddW0adM0duxY1a9fX2nTppW7u7t8fHzMQPKIESM0YsQIdenSRZGRkWrUqJFu3boV57aqV6+uP/74Q8OHDzfzumHDhtq6dauOHz+uJk2avNJ9tRUzZszQjBkzJP3fN7CKFy+uUaNG6b333tPw4cM1fvx4zZ8/X9myZVPFihX11Vdfadu2bS+1PTs7Ow0ZMkR169bVxIkT9fnnn7/Q7999911NmzZNQ4cOVd26dZUjRw41adJEI0eOfOXlyusiderUGjx4sFq0aKExY8aY/3788UfdvXtXefLkka+vrzlcRsy638u2XxC/ggULasmSJZo4caJ69eql27dvK126dCpZsqQWLVpk9mxKSPkxdOhQjRw5Ut9//73Cw8Pl4uKi6dOnm3XdV12ft2UJbRd1795d9evXl7+/v7y9vZ85HEyLFi109uxZM9CfL18+de3aVePGjdPx48dVvnx5lS5dWi4uLmrcuLH8/Pwsfp/Qts/bIuo5zvPaQf3799eIESP0ww8/6M6dO8qVK5e6d++uunXrmuupXLmygoOD9dlnnyl79uzq1avXM8vumO2V/1K2xdVelp6O1DFp0iSb6yUjJfya2L59u3x9fdWxY0cZhqE6derIzc3thcrX5z0fkyQ/Pz+NGjVKNWvWVIECBeTj4/Pc4TGjt2cnTZqkkSNHmnWAvHnzqlevXvrmm2+em6a3sU4e3z6PGTNGoaGhmjx5sqSnQ6B26tRJY8aMUfny5VWwYEGL8/x5361NSB39TZLQ5z3xscZ5+by6Qr9+/fTjjz+qefPmSpUqlQoUKKCRI0eaLzQndD9eJTvDVvrsAAAAvEY2b96sYsWKWQRDW7ZsqRw5cmjo0KFJmLLEdfHiRVWtWlUbNmx4I98GA2zJmTNndPfuXRUrVsycduTIETVp0kQ7dux4I7+9CACw5O3trVy5cmn48OFJnRTTwYMH1bZtW+3evfuVf8f0VQgLC9Pu3btVunRpi28rVatWTZ999hnfFQNgdfSUAQAAeAnTp0/XggUL1KNHD6VPn15bt27VgQMHzLfyX3d///23jh07pgULFqhcuXIEZAAruHLlitq2bStfX1+VKFFC165d07Bhw8xhGAAAsKbg4GAFBQVp0qRJql+//msZkJGkVKlSaeDAgSpZsqS+++47JU+eXEuXLtXly5dVvXr1pE4egLcQPWUAAABeQmhoqIYPH67ffvtNjx490scff6x27do9t/v76+TkyZNq1qyZ8uXLJ39//wR90BHAf7dgwQLNnTtXoaGhypAhgznUQ0KGHQYAvP5sqafMtm3b1LVrV7m4uGj8+PGv9ZCAp06dkp+fn44dO6YnT56oUKFC6ty5szmMPABYE0EZAAAAAAAAAAAAK4j7S28AAAAAAAAAAABIVARlAAAAAAAAAAAArICgDAAAAAAAAAAAgBUQlAEAAAAAAAAAALACgjIAAAAAkMQMw0jqJAAAAACwAoIyAAAAAF4LgYGB6tixozw8PFSkSBFVrlxZffv2VXBwcFIn7T8JDAzUN998Y/4dGhoqR0dHLV++PAlTBQAAAOBVICgDAAAAwOZNmTJFzZo108OHD9W7d29Nnz5d7dq108mTJ1W/fn2tW7cuqZP40pYsWWIRWMqePbsWL16sihUrJl2iAAAAALwSKZI6AQAAAADwPNu3b9dPP/2kjh07qkOHDub0kiVLql69eurWrZt69uwpBwcHffLJJ0mY0sSRKlUqubq6JnUyAAAAALwC9JQBAAAAYNP8/f310UcfqX379rHmpUyZUoMGDVLy5Mk1depUSVJ4eLh+/vlnVa5cWc7Ozqpdu7ZWrFhh8buVK1eqfv36cnFxUcWKFfXTTz8pPDxcktSzZ095enpaLB9zSLGDBw/K0dFRe/bsUbNmzeTs7KyqVatqwYIFFr+7deuWBg4cqEqVKsnJyUklS5ZU+/btFRoaam5rxYoVunTpkrn+uIYvO3funDp16iQPDw+5urrK29tbgYGBsdK3YcMGderUSW5ubipZsqT69u2rf//992UPPQAAAIBERlAGAAAAgM26deuWTpw4oUqVKsnOzi7OZTJlyqQyZcpo69atkqTu3btr5syZatiwoSZPnqyyZcuqZ8+eWrt2rSRp/vz58vHxUeHCheXv769vvvlGc+fO1ZAhQ144fV26dFGhQoU0YcIElSlTRgMHDjQDM4ZhqG3bttq7d6+6d++u6dOnq0OHDtq/f7/69+8vSfruu+9UoUIFZcuW7ZlDlp05c0ZeXl4KDQ1V3759NWrUKNnZ2enLL7/UoUOHLJbt37+/cuXKpYkTJ6pVq1ZaunSpfvnllxfeLwAAAACvBsOXAQAAALBZly5dkiTlypXrucvlzZtXW7du1eHDhxUQEKDevXvryy+/lCS5u7vr0qVLOnjwoGrWrKkJEybo008/tQjCPHz4UOvWrVNERMQLpa9KlSrq06ePJKlcuXK6du2aJk6cqCZNmujatWtKkyaNfHx8VLx4cUlSqVKldOHCBS1evFiSlCdPHmXJksViyLKYPVv8/f2VKlUqzZkzR+nTp5ckVaxYUbVr19bIkSO1dOlSc9kKFSrIx8fH3O+9e/dqx44d6tat2wvtFwAAAIBXg6AMAAAAAJtlGIakp8OUPU/y5MklSYcPH5YkVa1a1WL++PHjJUnBwcG6efOmqlSpYjG/VatWatWq1Qunr379+hZ/V61aVVu3blVISIg++ugjzZkzR4ZhKDQ0VOfPn9fZs2d15MgRc6i0hDh06JAqVapkBmQkKUWKFKpVq5YmTJigf/75x5we81s0OXLkMANbAAAAAJIeQRkAAAAANiuqh0x8gYWLFy8qXbp0ZhDn3XffjXO5O3fuPHf+i3rvvfcs/o5a7927dyVJq1ev1ujRo/X3338rU6ZMKliwoOzt7V9oG3fv3lXWrFljTc+aNasMw9CDBw/MaWnSpLFYJlmyZOYxAQAAAJD0+KYMAAAAAJv17rvvytXVVQEBAYqMjIxzmQcPHmjv3r3y9PTUO++8I+npt2iiCw4OVmBg4DPn3759W3v37tW///4rOzs7PXnyxGJ+zCHFov8uups3b5rpPnz4sHx8fFS1alXt2rVLBw8e1KxZs2L1ZolPxowZdePGjVjTr1+/LknKnDnzC60PAAAAQNIhKAMAAADApnXo0EEhISEaPXp0rHlPnjxR//799ejRI7Vu3VrFihWTJG3bts1iuVGjRsnX11cfffSRMmfOrO3bt1vMX7Vqlb755htFREQoXbp0un37tsLCwsz5gYGBcaZty5YtFn9v3LhRuXLlUp48eXT06FFFRkaqY8eOZo+aJ0+eaN++fZJkBpmSJXt+s6xEiRLavn27RY+YJ0+eaN26dSpSpIhSpUr13N8DAAAAsB0MXwYAAADAppUrV049e/bUyJEjderUKTVo0EDZs2dXaGioFi5cqFOnTsnX11cFChSQJFWvXl1+fn569OiRChYsqF27dmn79u3y9/dX8uTJ1bFjRw0aNEjvvvuuPD09FRISonHjxqlZs2bKmDGjKlWqpLlz56pPnz76/PPPFRQUpJkzZ5rfrYlu5syZSp06tVxdXbVp0yZt375dP/30kyTJ2dlZkjRo0CA1aNBAd+/e1fz583X69GlJT3vfpE+fXu+8845u3LihnTt3qmDBgrG20aFDB+3atUstWrTQN998o5QpU2revHm6ePGipk2b9qoOOwAAAIBXgKAMAAAAAJv39ddfy83NTbNnz9aIESN069YtZcuWTR4eHvL19dXHH39sLuvn5yd/f3/Nnj1bt2/fVv78+TVu3Dh9+umnkqRmzZopbdq0mj59uhYvXqwcOXKoTZs2atOmjSTJw8NDPj4+mjt3rgICAlS4cGH5+/urcePGsdLVu3dvrVixQpMnT9ZHH32kcePGqVq1apKkUqVK6ccff9TMmTO1ceNGZc2aVaVKlZK/v7/at2+vwMBAVahQQV5eXtq5c6fat2+vTp06qWbNmhbb+OSTT7RgwQKNHj1avXr1kp2dnZydnTVnzhwVL178VR1yAAAAAK+AncFXHwEAAADghRw8eFAtWrTQnDlzVKpUqaRODgAAAIDXBN+UAQAAAAAAAAAAsAKCMgAAAAAAAAAAAFbA8GUAAAAAAAAAAABWQE8ZAAAAAAAAAAAAKyAoAwAAAAAAAAAAYAUEZQAAAAAAAAAAAKyAoAwAAAAAAAAAAIAVEJQBAAAAAAAAAACwAoIyAAAAAAAAAAAAVkBQBgAAAAAAAAAAwAoIygAAAAAAAAAAAFjB/wOahPuIgzk8GAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(20,8)})\n",
    "ax = sns.countplot(data = df, x = 'Occupation')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ea1f6b29-f170-4e37-b212-1911d0d5aa10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Occupation', ylabel='Amount'>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Occupation'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Occupation',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bcf5468-6a53-4fb1-8b3a-d5c6d6879453",
   "metadata": {},
   "source": [
    "From above graphs we can see that most of the buyers are working in IT, Healthcare and Aviation sector"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c9176d-9992-4d33-a530-2e346f0d9adf",
   "metadata": {},
   "source": [
    "### Product Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e6edeb21-83ba-498a-8887-cf631f0b5311",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(25,5)})\n",
    "ax = sns.countplot(data = df, x = 'Product_Category')\n",
    "\n",
    "for bars in ax.containers:\n",
    "    ax.bar_label(bars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "0fb1291d-2124-4823-a032-1fd189340955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_Category', ylabel='Amount'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Product_Category'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Product_Category',y= 'Amount')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28f38401-d04c-4898-baa4-0400a2fe84bf",
   "metadata": {},
   "source": [
    "From above graphs we can see that most of the sold products are from Food, Clothing and Electronics category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1049cb85-a91e-48da-95a3-e905e2e4305b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID', ylabel='Orders'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sales_state = df.groupby(['Product_ID'], as_index=False)['Orders'].sum().sort_values(by='Orders', ascending=False).head(10)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(20,5)})\n",
    "sns.barplot(data = sales_state, x = 'Product_ID',y= 'Orders')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3204581d-d52b-40b5-a91a-e79d74c8b18c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Product_ID'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 10 most sold products (same thing as above)\n",
    "fig1, ax1 = plt.subplots(figsize=(12,7))\n",
    "df.groupby('Product_ID')['Orders'].sum().head(10).sort_values(ascending=False).plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "025d8c9f-f144-4348-877a-71819be8cc2f",
   "metadata": {},
   "source": [
    "### Conclusion:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "755099b3-97bf-4039-9aff-b0d0f262ab2b",
   "metadata": {},
   "source": [
    "Married women age group 26-35 yrs from UP, Maharastra and Karnataka working in IT, Healthcare and Aviation are more likely to buy products from Food, Clothing and Electronics category"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40c149c1-45ff-4b05-8fb0-3f136c297e9d",
   "metadata": {},
   "source": [
    "Thank You!"
   ]
  }
 ],
 "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": "3.12.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}