12_2023_Instacart_Basket_Analysis/03_Scripts/Step9_IC_Stakeholders_key_questions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f7673b8d",
   "metadata": {},
   "source": [
    "# Project: 12-2023 Instacart Basket Analysis\n",
    "## Author: Nadia Ordonez\n",
    "## Step 9 IC Stakeholders key questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbcdb603",
   "metadata": {},
   "source": [
    "# Table of Contents\n",
    "* [1. Introduction](#1.-Introduction)\n",
    "* [2. Stakeholders key questions](#2.-Stakeholders-key-questions)\n",
    "* [3. Importing data](#3.-Importing-data)\n",
    "    * [3.1 Importing libraries](#3.1-Importing-libraries)\n",
    "    * [3.2 Importing data](#3.2-Importing-data)\n",
    "* [4. Schedule ads](#4.-Schedule-ads)\n",
    "* [5. Timing expenditure habits](#5.-Timing-expenditure-habits)\n",
    "* [6. Price tags](#6.-Price-tags)\n",
    "* [7. Frequency of product orders per department](#7.-Frequency-of-product-orders-per-department)\n",
    "* [8. Customers and ordering behaviors](#8.-Customer-and-ordering-behaviors)\n",
    "    * [8.1 Brand loyalty distribution](#8.1-Brand-loyalty-distribution)\n",
    "    * [8.2 Ordering habits and loyalty](#8.2-Ordering-habits-and-loyalty)\n",
    "    * [8.3 Ordering habits and regions](#8.3-Ordering-habits-and-regions)\n",
    "    * [8.4 Age and family status](#8.4-Age-and-family-status)\n",
    "    * [8.5 Demographic insights](#8.5-Demographic-insights)\n",
    "* [9. Recommnedations](#9.-Recommendations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09cb1625",
   "metadata": {},
   "source": [
    "# 1. Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e09786",
   "metadata": {},
   "source": [
    "The Instacart stakeholders are most interested in the variety of customers in their database along with their purchasing behaviors. They assume they can't target everyone using the same methods, and they’re considering a targeted marketing strategy. They want to target different customers with applicable marketing campaigns to see whether they have an effect on the sale of their products. This analysis will inform what this strategy might look like to ensure Instacart targets the right customer profiles with the appropriate products. \n",
    "\n",
    "NOTE: After deliverations with stakeholders, it was also agreed to run the below data analysis taking into account the volumen of orders per user, instead of single orders per user. In our current dataframe, a single order_id is repeated several times in the \"order_id\" variable depending on how many products were purchased within single orders.\n",
    "\n",
    "NOTE: Due to RAM memory issues, during the 3.1 Importing data step, the df was reduced in size by selecting relevant variables. These commands were silenced after being run once. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c229958b",
   "metadata": {},
   "source": [
    "# 2. Stakeholders key questions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f8dcb31",
   "metadata": {},
   "source": [
    "The stakeholders would like to be able to answer the following key questions:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05cf4be1",
   "metadata": {},
   "source": [
    "The sales team needs to know what the busiest days of the week and hours of the day are (i.e., the days and times with the most orders) in order to schedule ads at times when there are fewer orders."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad95a6f4",
   "metadata": {},
   "source": [
    "The sales team also wants to know whether there are particular times of the day when people spend the most money, as this might inform the type of products they advertise at these times."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08521fca",
   "metadata": {},
   "source": [
    "Instacart has a lot of products with different price tags. Marketing and sales want to use simpler price range groupings to help direct their efforts."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cdeb5a6",
   "metadata": {},
   "source": [
    "Are there certain types of products that are more popular than others? The marketing and sales teams want to know which departments have the highest frequency of product orders."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6ed359d",
   "metadata": {},
   "source": [
    "The marketing and sales teams are particularly interested in the different types of customers in their system and how their ordering behaviors differ. For example:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1538eb7f",
   "metadata": {},
   "source": [
    "a. What’s the distribution among users in regards to their brand loyalty?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b099b96",
   "metadata": {},
   "source": [
    "b. Are there differences in ordering habits based on a customer’s loyalty status?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b712d1fa",
   "metadata": {},
   "source": [
    "c. Are there differences in ordering habits based on a customer’s region?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd31759",
   "metadata": {},
   "source": [
    "d. Is there a connection between age and family status in terms of ordering habits?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9364db32",
   "metadata": {},
   "source": [
    "e. What different classifications does the demographic information suggest? Is there a relation betwen age and spending power (income)?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28f08d26",
   "metadata": {},
   "source": [
    "# 3. Importing data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "806c6cb4",
   "metadata": {},
   "source": [
    "## 3.1 Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bd860302",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import analytical libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6f7e8b7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import visualization libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "100286fa",
   "metadata": {},
   "source": [
    "## 3.2 Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e6c249c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Project folder path into a string to easily retrieve data\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a73e4f76",
   "metadata": {},
   "source": [
    "### Reducing df size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcc94d4f",
   "metadata": {},
   "source": [
    "NOTE: Due to RAM memory issues, during the 3.1 Importing data step, the df was reduced in size by selecting relevant variables. These commands were silenced after being run once. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "18ba291b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import “orders_products_der_step6.pkl”\n",
    "#This df is the result of merging customer, products and orders details after applying an exclusion flag\n",
    "#In this df, aggregation and derivations are included, to see details see \"Step 6 IC Derivations and Aggregations\"\n",
    "\n",
    "#stakeholders = pd.read_pickle(os.path.join(path, '02 Data', 'Prepared data', 'orders_products_der_step6.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "555b8908",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30992966, 30)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check df size\n",
    "\n",
    "#stakeholders.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1a427098",
   "metadata": {},
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>orders_day_of_week</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
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       "      <th>add_to_cart_sequence</th>\n",
       "      <th>reordered</th>\n",
       "      <th>product_name</th>\n",
       "      <th>...</th>\n",
       "      <th>max_order_number</th>\n",
       "      <th>price_range_products</th>\n",
       "      <th>busiest_days</th>\n",
       "      <th>busiest_period_of_day</th>\n",
       "      <th>region</th>\n",
       "      <th>loyalty_flag</th>\n",
       "      <th>mean_price_per_user</th>\n",
       "      <th>type_of_spender</th>\n",
       "      <th>median_days_since_prior_order</th>\n",
       "      <th>usage_frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>mid-range</td>\n",
       "      <td>Regular days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>20.5</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
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       "      <td>1</td>\n",
       "      <td>Soda</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>20.5</td>\n",
       "      <td>Non-frequent customer</td>\n",
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       "      <td>473747</td>\n",
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       "      <td>12</td>\n",
       "      <td>21.0</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Soda</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>20.5</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>29.0</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Soda</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Average orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>20.5</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>28.0</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Soda</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>Least busiest days</td>\n",
       "      <td>Most orders</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>6.367797</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>20.5</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "    </tr>\n",
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       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id  order_number  orders_day_of_week  order_hour_of_day  \\\n",
       "0   2539329        1             1                   2                  8   \n",
       "1   2398795        1             2                   3                  7   \n",
       "2    473747        1             3                   3                 12   \n",
       "3   2254736        1             4                   4                  7   \n",
       "4    431534        1             5                   4                 15   \n",
       "\n",
       "   days_since_prior_order  product_id  add_to_cart_sequence  reordered  \\\n",
       "0                     NaN         196                     1          0   \n",
       "1                    15.0         196                     1          1   \n",
       "2                    21.0         196                     1          1   \n",
       "3                    29.0         196                     1          1   \n",
       "4                    28.0         196                     1          1   \n",
       "\n",
       "  product_name  ...  max_order_number  price_range_products  \\\n",
       "0         Soda  ...                10             mid-range   \n",
       "1         Soda  ...                10             mid-range   \n",
       "2         Soda  ...                10             mid-range   \n",
       "3         Soda  ...                10             mid-range   \n",
       "4         Soda  ...                10             mid-range   \n",
       "\n",
       "         busiest_days busiest_period_of_day region  loyalty_flag  \\\n",
       "0        Regular days        Average orders  South           New   \n",
       "1  Least busiest days        Average orders  South           New   \n",
       "2  Least busiest days        Average orders  South           New   \n",
       "3  Least busiest days        Average orders  South           New   \n",
       "4  Least busiest days           Most orders  South           New   \n",
       "\n",
       "  mean_price_per_user  type_of_spender median_days_since_prior_order  \\\n",
       "0            6.367797      Low spender                          20.5   \n",
       "1            6.367797      Low spender                          20.5   \n",
       "2            6.367797      Low spender                          20.5   \n",
       "3            6.367797      Low spender                          20.5   \n",
       "4            6.367797      Low spender                          20.5   \n",
       "\n",
       "         usage_frequency  \n",
       "0  Non-frequent customer  \n",
       "1  Non-frequent customer  \n",
       "2  Non-frequent customer  \n",
       "3  Non-frequent customer  \n",
       "4  Non-frequent customer  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check headers\n",
    "\n",
    "#stakeholders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e16b0e0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                            int32\n",
       "user_id                             int32\n",
       "order_number                         int8\n",
       "orders_day_of_week                   int8\n",
       "order_hour_of_day                    int8\n",
       "days_since_prior_order            float64\n",
       "product_id                          int32\n",
       "add_to_cart_sequence                int32\n",
       "reordered                            int8\n",
       "product_name                       object\n",
       "aisle_id                             int8\n",
       "department_id                        int8\n",
       "prices                            float64\n",
       "gender                           category\n",
       "state                            category\n",
       "age                                 int32\n",
       "date_joined                        object\n",
       "number_of_dependants                int32\n",
       "family_status                    category\n",
       "income                              int64\n",
       "max_order_number                     int8\n",
       "price_range_products               object\n",
       "busiest_days                       object\n",
       "busiest_period_of_day              object\n",
       "region                             object\n",
       "loyalty_flag                       object\n",
       "mean_price_per_user               float64\n",
       "type_of_spender                    object\n",
       "median_days_since_prior_order     float64\n",
       "usage_frequency                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See headers\n",
    "\n",
    "#stakeholders.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "44ebe25d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a subset to avoid RAM issues\n",
    "#Selecting columns to avoid RAM memory issues\n",
    "\n",
    "#variables = ['order_id', 'user_id', 'order_number', 'orders_day_of_week', 'order_hour_of_day',  'days_since_prior_order', 'product_id', 'product_name',  'department_id', 'prices', 'age', 'number_of_dependants', 'family_status', 'income', 'price_range_products', 'region', 'loyalty_flag', 'type_of_spender', 'usage_frequency']\n",
    "#stakeholders = stakeholders.loc[:, variables]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2d2a80e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30992966, 19)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check df size\n",
    "\n",
    "#stakeholders.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bff5c73f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>orders_day_of_week</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>department_id</th>\n",
       "      <th>prices</th>\n",
       "      <th>age</th>\n",
       "      <th>number_of_dependants</th>\n",
       "      <th>family_status</th>\n",
       "      <th>income</th>\n",
       "      <th>price_range_products</th>\n",
       "      <th>region</th>\n",
       "      <th>loyalty_flag</th>\n",
       "      <th>type_of_spender</th>\n",
       "      <th>usage_frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196</td>\n",
       "      <td>Soda</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "      <td>196</td>\n",
       "      <td>Soda</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>473747</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>21.0</td>\n",
       "      <td>196</td>\n",
       "      <td>Soda</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>29.0</td>\n",
       "      <td>196</td>\n",
       "      <td>Soda</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>28.0</td>\n",
       "      <td>196</td>\n",
       "      <td>Soda</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id  order_number  orders_day_of_week  order_hour_of_day  \\\n",
       "0   2539329        1             1                   2                  8   \n",
       "1   2398795        1             2                   3                  7   \n",
       "2    473747        1             3                   3                 12   \n",
       "3   2254736        1             4                   4                  7   \n",
       "4    431534        1             5                   4                 15   \n",
       "\n",
       "   days_since_prior_order  product_id product_name  department_id  prices  \\\n",
       "0                     NaN         196         Soda              7     9.0   \n",
       "1                    15.0         196         Soda              7     9.0   \n",
       "2                    21.0         196         Soda              7     9.0   \n",
       "3                    29.0         196         Soda              7     9.0   \n",
       "4                    28.0         196         Soda              7     9.0   \n",
       "\n",
       "   age  number_of_dependants family_status  income price_range_products  \\\n",
       "0   31                     3       married   40423            mid-range   \n",
       "1   31                     3       married   40423            mid-range   \n",
       "2   31                     3       married   40423            mid-range   \n",
       "3   31                     3       married   40423            mid-range   \n",
       "4   31                     3       married   40423            mid-range   \n",
       "\n",
       "  region loyalty_flag type_of_spender        usage_frequency  \n",
       "0  South          New     Low spender  Non-frequent customer  \n",
       "1  South          New     Low spender  Non-frequent customer  \n",
       "2  South          New     Low spender  Non-frequent customer  \n",
       "3  South          New     Low spender  Non-frequent customer  \n",
       "4  South          New     Low spender  Non-frequent customer  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check headers\n",
    "\n",
    "#stakeholders.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8fa67df4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Exporting and Importing\n",
    "#Exporting a smaller df with relevant variables \n",
    "\n",
    "#stakeholders.to_pickle(os.path.join(path, '02 Data','Prepared data', 'stakeholders.pkl'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1677d59f",
   "metadata": {},
   "source": [
    "### Stakeholders df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a10dcc6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing smaller df in a second script run to avoid RAM memory issues\n",
    "stakeholders = pd.read_pickle(os.path.join(path, '02 Data', 'Prepared data', 'stakeholders.pkl'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "83b55b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30992966, 19)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check df size\n",
    "stakeholders.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "868f3382",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_id                     int32\n",
       "user_id                      int32\n",
       "order_number                  int8\n",
       "orders_day_of_week            int8\n",
       "order_hour_of_day             int8\n",
       "days_since_prior_order     float64\n",
       "product_id                   int32\n",
       "product_name                object\n",
       "department_id                 int8\n",
       "prices                     float64\n",
       "age                          int32\n",
       "number_of_dependants         int32\n",
       "family_status             category\n",
       "income                       int64\n",
       "price_range_products        object\n",
       "region                      object\n",
       "loyalty_flag                object\n",
       "type_of_spender             object\n",
       "usage_frequency             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See headers\n",
    "stakeholders.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5752afe6",
   "metadata": {},
   "source": [
    "# 4. Schedule ads"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f700e958",
   "metadata": {},
   "source": [
    "The sales team needs to know what the busiest days of the week and hours of the day are (i.e., the days and times with the most orders) in order to schedule ads at times when there are fewer orders."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b2160ad",
   "metadata": {},
   "source": [
    "## Busiest days of the week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8ab248ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "orders_day_of_week\n",
       "0    5913859\n",
       "1    5417064\n",
       "6    4294962\n",
       "5    4037350\n",
       "2    4028494\n",
       "3    3672954\n",
       "4    3628283\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing the frequency of a column will quickly inform you which values appear more often within that column\n",
    "stakeholders['orders_day_of_week'].value_counts(dropna = False)\n",
    "#the value 0 means Saturday\n",
    "#0 and 1 are the \"Busiest days\"\n",
    "#Frequency of days equals to frequency of orders based on order volumen in our df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "333c7ebc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Converting variable to categorical for better labeling\n",
    "stakeholders['orders_day_of_week'] = stakeholders['orders_day_of_week'].astype('category')\n",
    "\n",
    "#Defining the desired names for each day of the week\n",
    "day_names = ['Saturday', 'Sunday', 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday']\n",
    "\n",
    "#Plotting frequency of a variable\n",
    "ax = stakeholders['orders_day_of_week'].value_counts().sort_index().plot.bar()\n",
    "\n",
    "plt.title(\"Busiest days of the week\")\n",
    "plt.ylabel(\"Order counts\") #not as single orders, but as order volumen\n",
    "\n",
    "#Remove main x-axis label\n",
    "ax.set_xlabel(\"\")\n",
    "\n",
    "#Customize x-axis labels\n",
    "ax.set_xticklabels(day_names, rotation=45)\n",
    "\n",
    "#Changing y-axis to display values in millions\n",
    "ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}M\".format(int(x/1e6))))\n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'busiest day of the week.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "#Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa226b97",
   "metadata": {},
   "source": [
    "Fewer orders are placed during Wednesday and Tuesday."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa900c0",
   "metadata": {},
   "source": [
    "## Busiest hours of the day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cec23ab2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "order_hour_of_day\n",
       "10    2652317\n",
       "11    2617229\n",
       "14    2570213\n",
       "13    2541527\n",
       "15    2538253\n",
       "12    2497997\n",
       "16    2415839\n",
       "9     2366037\n",
       "17    1984069\n",
       "8     1660891\n",
       "18    1552540\n",
       "19    1194918\n",
       "20     931746\n",
       "7      864069\n",
       "21     763813\n",
       "22     607550\n",
       "23     384838\n",
       "6      281307\n",
       "0      208214\n",
       "1      109705\n",
       "5       84514\n",
       "2       65743\n",
       "4       50802\n",
       "3       48835\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing the frequency of a column will quickly inform you which values appear more often within that column\n",
    "stakeholders['order_hour_of_day'].value_counts(dropna = False)\n",
    "#Frequency of hours equals to frequency of orders based on order volumen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c262da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting frequency of a variable\n",
    "ax = stakeholders['order_hour_of_day'].value_counts().sort_index().plot.bar()\n",
    "\n",
    "plt.title(\"Busiest hours of the day\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Order counts\") #not as single orders, but as order volumen\n",
    "\n",
    "#Changing y-axis to display values in thousands\n",
    "ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}K\".format(int(x/1e3)))) \n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'busiest hours of the day.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "#Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971d0440",
   "metadata": {},
   "source": [
    "The fewest number of order (under 500k) are placed between 23:00 and 06:00 hours."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ad2a5cb",
   "metadata": {},
   "source": [
    "# 5. Timing expenditure habits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f1b2933",
   "metadata": {},
   "source": [
    "The sales team wants to know whether there are particular times of the day when people spend the most money, as this might inform the type of products they advertise at these times."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e619894",
   "metadata": {},
   "source": [
    "## Sampling data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c445e450",
   "metadata": {},
   "outputs": [],
   "source": [
    "#In order to create a subset that actually represents the entirety of your data, selected rows would beed to be randomized\n",
    "#In a 70/30 split, for example, the 30 % sample would statistically resemble the 70 % sample but contain less rows\n",
    "#30% df is used for plotting and 70% for data analyses\n",
    "np.random.seed(4)\n",
    "dev = np.random.rand(len(stakeholders)) <= 0.7\n",
    "#seed 4 = Python will always give you the same set of random numbers\n",
    "#random is a function that creates random numbers \n",
    "#rand is a function that ensures the random numbers generated by random follow the uniform distribution. \n",
    "#Uniform distribution simply means that there should be an equal chance for every number between 0 and 1 to occur\n",
    "#Now there is a list of 32,434,212 boolean values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b77d729e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Splitting df into two subsets\n",
    "#store 70 percent of df in the big sample \n",
    "big = stakeholders[dev]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "16ef3ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Storing 30 percent of df in the small sample\n",
    "small = stakeholders[~dev]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4983612b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30992966"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Double check sampling data actions\n",
    "#length of entire df\n",
    "len(stakeholders)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6123eda8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30992966"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#length of entire df must be equal to big and small df\n",
    "len(big) + len (small)\n",
    "#both lengths matched"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3e6ace8",
   "metadata": {},
   "source": [
    "## Product price per hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6dab2b04",
   "metadata": {},
   "outputs": [],
   "source": [
    "#reduce RAM usage by selecting only the columns of interest to plot for a line chart\n",
    "df = small[['order_hour_of_day','prices']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a9283e7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Price')"
      ]
     },
     "execution_count": 17,
     "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": [
    "#Creating the line chart\n",
    "line = sns.lineplot(data = df, x = 'order_hour_of_day',y = 'prices')\n",
    "#Translucent blue bands around the line are error bands that represent the chart’s confidence intervals \n",
    "#If this line were a prediction, there would be a 95 percent probability that \n",
    "#the predicted value would fall within the area of the error bands.\n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "plt.title(\"Product price vs hour\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Price\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce46d41f",
   "metadata": {},
   "source": [
    "Between 00:00 and 06.00 prices per product tend to fluctuate from 7.75 up to 7.95 dollars. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f05369f6",
   "metadata": {},
   "source": [
    "## Total sales per hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ef4fe58c",
   "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>order_hour_of_day</th>\n",
       "      <th>prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1627169.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>856736.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>513173.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>382472.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>398511.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>662230.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>2200893.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>6761827.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>12945550.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>18351348.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>20560738.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>20345489.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>19467933.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>19814754.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>20063142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>19821050.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>18856860.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>15496456.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>12129207.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>9317660.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>7271373.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>5965095.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>4751206.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>3001531.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    order_hour_of_day      prices\n",
       "0                   0   1627169.4\n",
       "1                   1    856736.6\n",
       "2                   2    513173.9\n",
       "3                   3    382472.9\n",
       "4                   4    398511.7\n",
       "5                   5    662230.7\n",
       "6                   6   2200893.0\n",
       "7                   7   6761827.3\n",
       "8                   8  12945550.2\n",
       "9                   9  18351348.9\n",
       "10                 10  20560738.6\n",
       "11                 11  20345489.6\n",
       "12                 12  19467933.0\n",
       "13                 13  19814754.7\n",
       "14                 14  20063142.0\n",
       "15                 15  19821050.3\n",
       "16                 16  18856860.8\n",
       "17                 17  15496456.9\n",
       "18                 18  12129207.6\n",
       "19                 19   9317660.7\n",
       "20                 20   7271373.6\n",
       "21                 21   5965095.7\n",
       "22                 22   4751206.7\n",
       "23                 23   3001531.2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Group by hours, then calculate the sum of prices\n",
    "grouped_hours = stakeholders.groupby('order_hour_of_day')['prices'].sum().reset_index()\n",
    "grouped_hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e94af096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting frequency of a variable\n",
    "ax = stakeholders.groupby('order_hour_of_day')['prices'].sum().reset_index().plot.bar(legend=None)\n",
    "\n",
    "plt.title(\"Timing expenditure habits\")\n",
    "plt.xlabel(\"Time\")\n",
    "plt.ylabel(\"Total sales\")\n",
    "\n",
    "#Changing y-axis to display values in millions\n",
    "ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}M\".format(int(x/1e6)))) \n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'timing expenditure habits.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "#Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "990b79fb",
   "metadata": {},
   "source": [
    "Between 09:00 and 16:00 hours, our users spent hourly more than 17M dollars. Strikingly, at 10:00, 11h00 and 14:00 more than 20M in sales are registered. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f02d57e",
   "metadata": {},
   "source": [
    "# 6. Price tags"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38e9ef52",
   "metadata": {},
   "source": [
    "* Instacart has a lot of products with different price tags. Marketing and sales want to use simpler price range groupings to help direct their efforts. In Step 6 IC Derivations and Aggregation, price tags were created under the variable \"price_range_products\", following the below criteria. \n",
    "    * If the item’s price is lower than or equal to 5 dollars, it will be labeled a “low-range”.\n",
    "    * If the item’s price is above 5 dollars but lower than or equal to 15 dollars, it will be labeled a “mid-range”.\n",
    "    * If the item’s price is above 15 dollars, it will be labeled a “high-range”.\n",
    "* NOTE: the total counts does not reflect the total product count that exists on our inventory, but rather the frequency at which our diferent product types are ordered.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f64b29ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price_range_products\n",
       "mid-range     20920614\n",
       "low-range      9674395\n",
       "high-range      393080\n",
       "NaN               4877\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing the frequency of a column will quickly inform you which values appear more often within that column\n",
    "stakeholders['price_range_products'].value_counts(dropna = False)\n",
    "#Frequency of hours equals to frequency of orders based on order volumen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bcc4fda4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting frequency of a variable\n",
    "ax = stakeholders['price_range_products'].value_counts(dropna = True).plot.bar(legend=None)\n",
    "\n",
    "plt.title(\"Price tags products\")\n",
    "plt.ylabel(\"Total counts\")\n",
    "\n",
    "#Changing y-axis to display values in millions\n",
    "ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}M\".format(int(x/1e6)))) \n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Remove main x-axis label\n",
    "ax.set_xlabel(\"\")\n",
    "\n",
    "#Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'price tags products.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "#Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a055817d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting frequency of a continuos variable\n",
    "#Frequency of prices\n",
    "ax = stakeholders['prices'].plot.hist(bins = 70)\n",
    "\n",
    "plt.title(\"Price frequency\")\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Changing y-axis to display values in thousands\n",
    "ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}K\".format(int(x/1e3)))) \n",
    "\n",
    "#Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'price frequency.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "#Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55db2388",
   "metadata": {},
   "source": [
    "Mid-range products are the most popular among our users. These are products that cost between 5 and 15 dollars. Products that cost more than 15 dollars are not purchased very frequently.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcf27346",
   "metadata": {},
   "source": [
    "# 7. Frequency of product orders per department"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b3b1352",
   "metadata": {},
   "source": [
    "Are there certain types of products that are more popular than others? The marketing and sales teams want to know which departments have the highest frequency of product orders."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "19ed7634",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Adding department names\n",
    "#Creating a variable containing the department names\n",
    "#Creating a dictionary\n",
    "#NOTE: Dictionary provided by Instacart team\n",
    "department_dict = {\n",
    "    '1': {'department': 'frozen'},\n",
    "    '2': {'department': 'other'},\n",
    "    '3': {'department': 'bakery'},\n",
    "    '4': {'department': 'produce'},\n",
    "    '5': {'department': 'alcohol'},\n",
    "    '6': {'department': 'international'},\n",
    "    '7': {'department': 'beverages'},\n",
    "    '8': {'department': 'pets'},\n",
    "    '9': {'department': 'dry goods pasta'},\n",
    "    '10': {'department': 'bulk'},\n",
    "    '11': {'department': 'personal care'},\n",
    "    '12': {'department': 'meat seafood'},\n",
    "    '13': {'department': 'pantry'},\n",
    "    '14': {'department': 'breakfast'},\n",
    "    '15': {'department': 'canned goods'},\n",
    "    '16': {'department': 'dairy eggs'},\n",
    "    '17': {'department': 'household'},\n",
    "    '18': {'department': 'babies'},\n",
    "    '19': {'department': 'snacks'},\n",
    "    '20': {'department': 'deli'},\n",
    "    '21': {'department': 'missing'}\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "90d58d08",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1': {'department': 'frozen'},\n",
       " '2': {'department': 'other'},\n",
       " '3': {'department': 'bakery'},\n",
       " '4': {'department': 'produce'},\n",
       " '5': {'department': 'alcohol'},\n",
       " '6': {'department': 'international'},\n",
       " '7': {'department': 'beverages'},\n",
       " '8': {'department': 'pets'},\n",
       " '9': {'department': 'dry goods pasta'},\n",
       " '10': {'department': 'bulk'},\n",
       " '11': {'department': 'personal care'},\n",
       " '12': {'department': 'meat seafood'},\n",
       " '13': {'department': 'pantry'},\n",
       " '14': {'department': 'breakfast'},\n",
       " '15': {'department': 'canned goods'},\n",
       " '16': {'department': 'dairy eggs'},\n",
       " '17': {'department': 'household'},\n",
       " '18': {'department': 'babies'},\n",
       " '19': {'department': 'snacks'},\n",
       " '20': {'department': 'deli'},\n",
       " '21': {'department': 'missing'}}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See dictionary\n",
    "department_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "08efb5a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert 'department_id' to string to match the keys in the dictionary\n",
    "stakeholders['department_id'] = stakeholders['department_id'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "162b092d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Map 'department_id' to 'department_name' using the dictionary\n",
    "stakeholders['department_name'] = stakeholders['department_id'].map(lambda x: department_dict.get(x, {}).get('department', 'unknown'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e73d064f",
   "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>order_id</th>\n",
       "      <th>user_id</th>\n",
       "      <th>order_number</th>\n",
       "      <th>orders_day_of_week</th>\n",
       "      <th>order_hour_of_day</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "      <th>product_id</th>\n",
       "      <th>product_name</th>\n",
       "      <th>department_id</th>\n",
       "      <th>prices</th>\n",
       "      <th>age</th>\n",
       "      <th>number_of_dependants</th>\n",
       "      <th>family_status</th>\n",
       "      <th>income</th>\n",
       "      <th>price_range_products</th>\n",
       "      <th>region</th>\n",
       "      <th>loyalty_flag</th>\n",
       "      <th>type_of_spender</th>\n",
       "      <th>usage_frequency</th>\n",
       "      <th>department_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2539329</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196</td>\n",
       "      <td>Soda</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2398795</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>15.0</td>\n",
       "      <td>196</td>\n",
       "      <td>Soda</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>473747</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>21.0</td>\n",
       "      <td>196</td>\n",
       "      <td>Soda</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2254736</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>29.0</td>\n",
       "      <td>196</td>\n",
       "      <td>Soda</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>431534</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>28.0</td>\n",
       "      <td>196</td>\n",
       "      <td>Soda</td>\n",
       "      <td>7</td>\n",
       "      <td>9.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>married</td>\n",
       "      <td>40423</td>\n",
       "      <td>mid-range</td>\n",
       "      <td>South</td>\n",
       "      <td>New</td>\n",
       "      <td>Low spender</td>\n",
       "      <td>Non-frequent customer</td>\n",
       "      <td>beverages</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  user_id  order_number orders_day_of_week  order_hour_of_day  \\\n",
       "0   2539329        1             1                  2                  8   \n",
       "1   2398795        1             2                  3                  7   \n",
       "2    473747        1             3                  3                 12   \n",
       "3   2254736        1             4                  4                  7   \n",
       "4    431534        1             5                  4                 15   \n",
       "\n",
       "   days_since_prior_order  product_id product_name department_id  prices  age  \\\n",
       "0                     NaN         196         Soda             7     9.0   31   \n",
       "1                    15.0         196         Soda             7     9.0   31   \n",
       "2                    21.0         196         Soda             7     9.0   31   \n",
       "3                    29.0         196         Soda             7     9.0   31   \n",
       "4                    28.0         196         Soda             7     9.0   31   \n",
       "\n",
       "   number_of_dependants family_status  income price_range_products region  \\\n",
       "0                     3       married   40423            mid-range  South   \n",
       "1                     3       married   40423            mid-range  South   \n",
       "2                     3       married   40423            mid-range  South   \n",
       "3                     3       married   40423            mid-range  South   \n",
       "4                     3       married   40423            mid-range  South   \n",
       "\n",
       "  loyalty_flag type_of_spender        usage_frequency department_name  \n",
       "0          New     Low spender  Non-frequent customer       beverages  \n",
       "1          New     Low spender  Non-frequent customer       beverages  \n",
       "2          New     Low spender  Non-frequent customer       beverages  \n",
       "3          New     Low spender  Non-frequent customer       beverages  \n",
       "4          New     Low spender  Non-frequent customer       beverages  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#See results\n",
    "stakeholders.head()\n",
    "#\"department_name\" was added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1d45318c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "department_name\n",
       "produce            9079276\n",
       "dairy eggs         5191364\n",
       "snacks             2766406\n",
       "beverages          2573822\n",
       "frozen             2123342\n",
       "pantry             1782903\n",
       "bakery             1125026\n",
       "canned goods       1012074\n",
       "deli               1003834\n",
       "dry goods pasta     822136\n",
       "household           699858\n",
       "breakfast           677073\n",
       "meat seafood        674785\n",
       "personal care       424362\n",
       "babies              410392\n",
       "international       255991\n",
       "alcohol             144627\n",
       "pets                 93065\n",
       "missing              64768\n",
       "other                34411\n",
       "bulk                 33451\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Counting values within new column\n",
    "stakeholders['department_name'].value_counts(dropna = False)\n",
    "#The top 3 grocery departments with the highest frequency of orders are produce, dairy eggs, and snacks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f49f6ae3",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">department_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>department_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>alcohol</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>babies</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bakery</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beverages</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>breakfast</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bulk</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>canned goods</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dairy eggs</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deli</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dry goods pasta</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frozen</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>household</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>international</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meat seafood</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>missing</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pantry</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>personal care</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pets</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>produce</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snacks</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                department_id    \n",
       "                          min max\n",
       "department_name                  \n",
       "alcohol                     5   5\n",
       "babies                     18  18\n",
       "bakery                      3   3\n",
       "beverages                   7   7\n",
       "breakfast                  14  14\n",
       "bulk                       10  10\n",
       "canned goods               15  15\n",
       "dairy eggs                 16  16\n",
       "deli                       20  20\n",
       "dry goods pasta             9   9\n",
       "frozen                      1   1\n",
       "household                  17  17\n",
       "international               6   6\n",
       "meat seafood               12  12\n",
       "missing                    21  21\n",
       "other                       2   2\n",
       "pantry                     13  13\n",
       "personal care              11  11\n",
       "pets                        8   8\n",
       "produce                     4   4\n",
       "snacks                     19  19"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check flags\n",
    "stakeholders.groupby('department_name').agg({'department_id' : ['min', 'max']})\n",
    "#nothing odd on the results\n",
    "#department_name was correctly assigned to the df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "f5aad892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting frequency of a variable\n",
    "ax = stakeholders['department_name'].value_counts(dropna = False).plot.bar(legend=None)\n",
    "\n",
    "plt.title(\"Customer favourite grocery departments\")\n",
    "plt.ylabel(\"Total orders\") #not single orders but order volumens \n",
    "\n",
    "#Changing y-axis to display values in millions\n",
    "ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}M\".format(int(x/1e6)))) \n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Remove main x-axis label\n",
    "ax.set_xlabel(\"\")\n",
    "\n",
    "#Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'department order frequency.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "#Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a3ad823",
   "metadata": {},
   "source": [
    "More than 8M products were ordered from the produce grocery department, followed by dairy eggs, snacks, beverages and frozen with more than 2M products being order at each department category. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5837aa31",
   "metadata": {},
   "source": [
    "## 8. Customers and ordering behaviors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61ba1f9b",
   "metadata": {},
   "source": [
    "The marketing and sales teams are particularly interested in the different types of customers in their system and how their ordering behaviors differ."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e09f536",
   "metadata": {},
   "source": [
    "## 8.1 Brand loyalty distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61c95cfb",
   "metadata": {},
   "source": [
    "* Since the business strategy team would like to employ some kind of bonus point program for loyal Instacart customers, a loyalty flag was created in Step 6 IC Derivations and aggregations following the below criteria: \n",
    "    * If the maximum orders the user has made is over 40, then the customer will be labeled a “Loyal”.\n",
    "    * If the maximum orders the user has made is over 10 but less than or equal to 40, then the customer will be labeled “Regular”.\n",
    "    * If the maximum orders the user has made is less than or equal to 10, then the customer will be labeled “New”."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ce70b76c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "loyalty_flag\n",
       "Regular    15891172\n",
       "Loyal      10293959\n",
       "New         4807835\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing the frequency of a column will quickly inform you which values appear more often within that column\n",
    "stakeholders['loyalty_flag'].value_counts(dropna = False)\n",
    "#Frequency of loyalty tags equals to frequency of orders based on order volumen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2a70dd75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting frequency of a variable\n",
    "ax = stakeholders['loyalty_flag'].value_counts(dropna = False).plot.bar(legend=None)\n",
    "#not unique customers but customers based on order volumen\n",
    "\n",
    "plt.title(\"Customer loyalty frequency\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "\n",
    "#Changing y-axis to display values in millions\n",
    "ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}M\".format(int(x/1e6)))) \n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Remove main x-axis label\n",
    "ax.set_xlabel(\"\")\n",
    "\n",
    "#Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'loyalty frequency.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "#Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a4f8c64",
   "metadata": {},
   "source": [
    "Nearly 16M customers are regular. These regular customers are those who placed a maximum of 10 to 40 orders. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "167dd517",
   "metadata": {},
   "source": [
    "## 8.2 Ordering habits and loyalty"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f071dfb2",
   "metadata": {},
   "source": [
    "How often do customers, in terms of their loyalty tags, place orders?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "76e77cbc",
   "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>loyalty_flag</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Loyal</td>\n",
       "      <td>6.026906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New</td>\n",
       "      <td>17.598809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Regular</td>\n",
       "      <td>12.155234</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  loyalty_flag  days_since_prior_order\n",
       "0        Loyal                6.026906\n",
       "1          New               17.598809\n",
       "2      Regular               12.155234"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Average \"days_since_prior_order\" per customer loyalty\n",
    "usage_table = pd.pivot_table(stakeholders, values='days_since_prior_order', index='loyalty_flag', aggfunc='mean').reset_index()\n",
    "usage_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f0bb3b7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Determining the order of loyalty categories based on average days since prior order\n",
    "loyalty_order = usage_table.groupby('loyalty_flag')['days_since_prior_order'].mean().sort_values().index\n",
    "\n",
    "#Plotting the bar chart with specified order\n",
    "ax = sns.barplot(x='loyalty_flag', y='days_since_prior_order', data=usage_table, order=loyalty_order, color = '#1f77b4')\n",
    "\n",
    "plt.title(\"Average app usage frequency by Customer loyalty status\")\n",
    "plt.xlabel(\"Customer status\")\n",
    "plt.ylabel(\"Average days since prior order\")\n",
    "\n",
    "#Rotate x-axis labels for better readability\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right') \n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "# Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'bar_customer_loyalty_app_usage_frequency.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "# Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88a46342",
   "metadata": {},
   "source": [
    "Loyal customers order once a week during our app while regular customers order once every two weeks. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b2bf37d",
   "metadata": {},
   "source": [
    "## 8.3 Ordering habits and regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ce02c3bb",
   "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>region</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Midwest</td>\n",
       "      <td>10.909287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Northeast</td>\n",
       "      <td>10.774406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>South</td>\n",
       "      <td>10.796315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>West</td>\n",
       "      <td>10.754394</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      region  days_since_prior_order\n",
       "0    Midwest               10.909287\n",
       "1  Northeast               10.774406\n",
       "2      South               10.796315\n",
       "3       West               10.754394"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Average \"days_since_prior_order\" per region\n",
    "usage_table = pd.pivot_table(stakeholders, values='days_since_prior_order', index='region', aggfunc='mean').reset_index()\n",
    "usage_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "480d4ce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Determining the order of region categories based on average days since prior order\n",
    "region_order = usage_table.groupby('region')['days_since_prior_order'].mean().sort_values().index\n",
    "\n",
    "#Plotting the bar chart with specified order\n",
    "ax = sns.barplot(x='region', y='days_since_prior_order', data=usage_table, order=region_order, color = '#1f77b4')\n",
    "\n",
    "plt.title(\"Average app Usage Frequency by US region\")\n",
    "plt.xlabel(\"US Region\")\n",
    "plt.ylabel(\"Average days since prior order\")\n",
    "\n",
    "#Rotate x-axis labels for better readability\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right') \n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'bar_region_app_usage_frequency.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "#Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23553b67",
   "metadata": {},
   "source": [
    "In all US regions, our customers are using our app within a similar frequency, placing orders every 10 to 11 days. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "158c7cc9",
   "metadata": {},
   "source": [
    "## 8.4 Age and family status"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f5dc8fa",
   "metadata": {},
   "source": [
    "Is there a connection between age and family status in terms of ordering habits?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01f94663",
   "metadata": {},
   "source": [
    "### Relation between age and family status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dd7a70b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reducing RAM usage by selecting only the columns of interest to plot for a line chart\n",
    "df = small[['age','number_of_dependants']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "33c53c6b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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nn38+bNvGPffcg3vvvRf33nsve47/8B/+A/7rf/2vuO666/Ce97wHTz75JD760Y/ive9977r/fXnQyQAJHyABAYGNBaW/swLIG92NWfcUmJmrBdb52KWWiyAIIEnChmSrY0MLoB/96Ed45Stfyb4mHc61116Lu+++G8eOHcPBgwfZz23bxg033IAjR46gWCzi/PPPx9e//nW89rWvZY/Zs2cPvvWtb+H666/HRRddhN27d+O6667DBz7wgfX7wwZA3ewUQQsNkICAwEbCcTunwFx/VBtgCSLoHC0wq+uxNdOB5wdQFVEAbXVsaAH0ile8oueO4u677+74+sYbb8SNN97Y93kvv/xy/PCHP1zt4a0rGlwP2xYFkICAwAaDpsCYBmiERdDdRoiWm8MIsYsBqrcduH4AVUn5BYEtg02vAdoqaHYXQEKHJyAgsIEgJ+iizo3Bj2gB1O38bOdIrqcpsFL0d9ZNV2xATxGIAmgE4PtBxw5GtMAEBAQ2GsQAFdR4CmxU1yVaP6lp1d3W6gXKPNtWDodkapYz0p5HAsODKIBGAA3b7dhYiRaYgIDARoM0QB0tsI08oB6gAqhshKoOO4dgm4qlbZXQCqVpevByMEgCmxeiABoB8DEYQHjx+mIHIiAgsIEgP5yiERZA3ginwdPY+1ghKoAyeqk5ng8v+pumSiED1LAc9j2BrQ1RAI0AqADS1fjjyONkKiAgIDBsUAFU1nkfoNEsDNpOqAGqFsMAbNvNxgDxLtDbKmEB1LQ9EUV0ikAUQCMASoKn3QvQ6QskICAgsN4gEXRZV7nvjeZ0RqubAfKyeanxfkHbq1ELzHJFAXSKQBRAI4ClqAAq6SpjgRqWKIAEBAQ2DlTslIy4ALLd0SyAyM15nDFA2dgqKoBUWcJE9LuiADp1IAqgEQAlwRc1GUZUAHU7mwoICAisJ4gBovFwIN94+XrB9wMmGZgoUQGUTUJALTBNldnvNi0P7ogyXQLDhSiARgDLbRtAyACR7Xy3sZeAgIDAesLhjBBpvNwZQQaINzKcjITMjpePAdIVmf1u03aZBYDA1oYogEYAjAHSFRQYAyQuQAEBgY0DFUCGKrPWvDWCBRAfhEptLNvLNgVGrTNNkZgPUMv2RvLvFBg+RAE0AqAk+LKuMtdV0QITEBDYSFALTFNlaEp4qxhFZoRG4HVFRknP5wNkRQyQpsiYinyALNeH6Yj191SAKIBGAMQAlQyFmY6JFpiAgMBGghggXZVZMOgoiqCb0WZRV2XoUXCrk9EHiJgeXZUxXtBYq2+xJQqgUwGiABoBNCyaAlMYA2Q53siajgkICGx98C0wYoCsjOLi9QRtFg1VZhtIx8tm2shrgFRVYk7StbYjJsEywvX8kbVH6AdRAI0AapHnT0VXGYXbdrP5WAgICAgMG0EQxC0wRYbOWmCjtyhRC8zQZDZEEhoh9v9dElBrigQJQDXyEaqZDlyRSJ0Jx2smDs43N/owBoIogEYAZHpYMhQ2cmo5vtiBCAgIbAj4tUdXZGjUAhtBh3pqgRmqzIZIHC9bniLlgOmqAlmSMFYIRdR104Wof7LB94GGNXrnRRaIAmgEQKaHFUNlrqum442s7byAgMDWhuN1F0ARAzSKGiCLCiCFMUBO5ikwaoFJkCUJ48Vw/a0LBigz/CBAy/I2ZX6lKIBGAFQAlQ2VMUCm42WicAUEBASGDX7aS1dlaBGzYo6gBigugGQUSASdVQPEGSFKEjAWjdE3TE8wQBnh+j5s3xvJCcF+EAXQCKDJMUAlI+5hCwZIQEBgI8CLWlVFgsEYoNFbk6gFVtAUpqF0PB9ZjpTG4I2oAKIojYblCgYoI3w/1IttxgJI7f8QgbWE7fpsFHO8pLEpBFMUQAIC64IgCDBbt+D6IWsQBEAQhNQ+aWF2TxZZe+VUABVAihy2hkZ5CoyMEA1NhqHFyfVZChiK0CANEIvDsF14Yv3NBC8AXC8YyfZoP4gCaINRj0wQAWCsoDENkOVk62ELCAisDi3bw+PHa2g7HqRAgiQBAQAJgAQJAQJMlvVTqwCKmB5VliBJEjQ1FEGP4rhzi2eAtLipQS7PvdB24ikwWZIwUYziMEQgamb4QQDX80UBJJAfdTPuXxuqgjJNgbnCB2gzw3ZDb4yyIS6xUYfrBXDcALuqRSiytOLnR5dap9zNkNoZqhyOh4+yEzRNIBXU2EcNiNtbvUBTYIaiQJbiLLEwEPXU+swHhecHcP0g0/s9ahAaoA3GUisMQi1qCnRVQlGPW2Cn2qILhJ4eWyEG5GTDwqGF1kYfhkAGuH7Ybk4qfgCEjNApthnpboGRD9AoFgXkA1RQZeiqwj7HPAyQocmQJAnjpXD9bdruyEoQGpaLZ+dGx3eHWsYtUQAJ5MVy1AIraAo0RWZTYHZGK/ethpN1C0cX2xt9GKuG6/lscRUYbbh+APSQzAaQTjk9CBU6qiIDElgY6igWQKwFpiuQpJBNB+IR914gBoim3KZKYR5Y0/JGtqXTsly2cR4FeIEPRd6c8U2iANpgLLXCAqiohyK84ineAvODYFPuJLrBi9sFRhuuHyBAMvsDAAhwyrGxHS0wCYwBGk0NUMQAaeEamqcAYgxQ9PdNlrXoOd2RbPcBJPAORsZ3Z7HpQJNlWO7mi8QQBdAGg5Lgi3pI3a43A+R4PpbbTv8HrhNcf3OK6brRdkJjsFPtxrkZ4fZbtCWccp4wTrcGiByWR/CNoAKoqMlQJImxVVkYWNqkUNG0rRwyQG3bg2mP3t8KhMW44wUj0aK779Fj+Hf/349w/89n4bqbb+0eSgFUq9Xwta99DY899tgwnu6UAkuC18ICiML8rHUag19qOThwsjEyuwnfDwWpo3I8g8JyfXiBKIA2A2zXhyKlM0AypE23s10teA2QtFk0QJoCWZZQUMM1NFMB5MS/CwCT5VAEHQBYbo9Om4mH6/nw/NGQSPz0aA0A8ORsA44XbLrrZKAC6M1vfjM++clPAgDa7TZe9KIX4c1vfjMuuugi3HvvvUM9wK2OWsS+lIzOFljWLJvVwg8CtJ3Radd4AeAGfqTL2Jzw/QBuVMSJAmj00Xa8VAE0AMjSaDIfawnGACkyJMQMiTOCBRBpgGjiUs/RAiNnayMan9c5N+nlljuSGzHbCzdXo8AAkYbqZN2Cj83nBTRQAfTAAw/gF37hFwAAX/3qVxEEAZaWlvCnf/qnuO2224Z6gFsdlARf0tSoBUZOpgHz4lhLeH4A03FHRrDr+X40Vrm5LiQeju/DDSIGaAQWKYHesF2/dwEkS/3bZFsM5PjMjBBJBD2C1yWtXUU9PEZic7IxQOHfU+LG56tRIGrNskfy+rWipPtRODQyxpytWwj8YGQ20lkxUAG0vLyMqakpAMB9992HN77xjSiVSvjlX/5lPPnkk0M9wK0O0gCVDQWKJHVciG1n7cfBPT9A2/FGpgDyg1DkN4o7zazw/ACeF8D3AW8T/x2nAqjl2rMFJkkYQQPkNYXTJYImHyDHzZaxtZ6gcfeKHhYuxFZZGcbg6YZNG08AGCtQIOpomiHaro9gRBggylKzXB9N28vEuo0SBnJp27NnDx588EFMTU3hvvvuw5e+9CUAwOLiIgqFwlAPcKujQQyQrkCWo0wahD1oCkldS/hBANsN0FqH18oCKhpGceHJCscL4Eb6n1HcQQrEcP0Ajx9fxpOzTXhBADNaxMO2cLgxmCrp+O1XnL3Rh7quiFtgYWGoR/91/ZB96FEvriuCIGAaoHKUo0jtrCybOmqBFTmXbwpErZvOyK1DQRAJoIGRWFt488Ollr3pRuEHKoB+7/d+D29729tQqVRwxhln4BWveAWAsDV24YUXDvP4tjzqjAFSmeCwoCloOx4LSV1LuF6YfTQqk2CuH7aONnPLwfMDzNctjJW0kWwZCMRoWg5uv++JvtqFV5+3Ey85cwrSqNz51xgxAxSGhDIfID/IFDK6XiA9DBCGSQOAocaDJH1/P3oM7yA9zgogb+S0iLSpCnwgGIGlhX+PF5phAeT7AeQeLeVRwkAtsN/5nd/BD3/4Q3z+85/H9773Pchy+DRnnXUWPvKRj2R+ngceeADXXHMNTjvtNEiShK997Ws9H3///fdDkqQV/x5//PHEx3/pS1+CJEn41V/91czHtN6gKIyyrkKOFlcS4a1HNe34PlRZQtvxNjzo0HZ9PHxoEW3bHbmFJw9+8NQcPvCVR/C1h46ccuPTmw3HaiZsN7wGfvnCGbzxhafj7S/Zi3e//Ez8P698HrZXwrHoxoi2Q9YK1ILWFAkS4jDUURm/JrSseM2qGGHhUtQouT5DCyxqk9HkGNCZCD9qn7nrB3huvom27Y3E58AXQHMNG47nj6x/UhIGKoA+/OEP49xzz8XrX/96VCoV9v1XvepV+Nu//dvMz9NsNnHxxRezibKseOKJJ3Ds2DH275xzzlnxmOeeew433HADE2uPKojlKRdUJsSk3UhzHQog1/NR0BVYjr/hvhdf+qeDuO3rj+NbPzsxkuO2WfHo0WUAwKGFtmCARhyzNQsAMFXW8du/eDZ+84p9+LV/tRe/cslu/NL5uzBdDQug1imWDh6PwcsdGiDX80dCfEsg01RVlljrixgg0sqkwfcDdrMm3RAQF0CjGIj6xPE6/tNXH8Vdf//MaBRAXAtsrmHB8U+BAujWW29Fo9FY8f1Wq4Vbb7018/NcffXVuO222/CGN7wh1+vv2LEDu3btYv8UpTOl2fM8vO1tb8Ott96Ks846q+/zWZaFWq3W8W+9UI8KoKquMiEm9aPXIxPL9UKXVz/wWT98o3AkisCYrVkbzkatBvON0D+kabuCARoyji+bOLo0vKiUk/WwAJooaYk/p3DipnVqfZZUAFFKOnOC9n1kbYJ5fsDe37UCaRd1VeYYdL4ASv9d/kZd4FpgE8XYDXrUCqCfn6gDAGbr5kj4AFncezhbt6Jg4c1zoQxUAAVBkNgL/5d/+Rc2HbaWuPTSSzEzM4OrrroK3/3ud1f8/MMf/jCmp6fx7/7dv8v0fLfffjvGx8fZvz179gz7kBMRBAFjgCoFlfVNC6wAWtsiIAgC/OOBeSy2bASQNlwITdMcDcvddNMEPOYa4aLfsj3Y3ub9O0YRTcvFQnN4N1X6rCaKeuLPS4yNPdUYoCgLjDlBRyLoSDOYBQ3TxdGl9pp6w9AaaagyE2YTE2T38VLj1xgaoQeAiSgRvmGN3me+GGWA9WO31gv8pN2JmgkAm4oByiWCnpycZLqb5z//+R1FkOd5aDQa+O3f/u2hHyRhZmYGd911Fy677DJYloU///M/x1VXXYX7778fV155JQDg+9//Pj73uc/h4Ycfzvy8N910E973vvexr2u12roUQW3HY1U8pRADcQvMXOMC6J8PLuGjf/M4zp2p4gO/9ALmSbRRIAaqYbmbzk+Cx0IzXKRatrupx/lHEbbno51hvDkriK1LY4BoPLplr48x6aiAihZqgekRy+752QsgPwj9vNbyfWtGLLnBpcAXeQaox+/ShkuWAE3mGKAStcC8NbWxWGrZkGUJY4Xkcy8J8824ABoFBsju0ABZcL3NFYeRqwC68847EQQB3vWud+HWW2/F+Pg4+5mu69i3bx8uv/zyoR8kYf/+/di/fz/7+vLLL8ehQ4dwxx134Morr0S9Xsfb3/52fPazn8X27dszP69hGDAMYy0OuScoBkOWQhE0oRRdwKbjr6mi/vBiCwDw9GwTmiyjYblwPT9MgN4A0I6sbrosDmOzTBPwoF1ay9p8vhijDsfzWeiiNoTz9GTEAJHuoxs0Wk3TLacK+DF4SZKg0Ri8l70FFiAU7a5lG4lG4A0tboHxGqAsDJCmyOCXmcmIAWra7ppGOzw338J4UctVAFF49npFJfUDL1Xwo2ni9ZheHhZyFUDXXnstAODMM8/EFVdcAU3L/sGtFV760pfinnvuAQA8/fTTePbZZ3HNNdewn/tR415VVTzxxBM4++zR8fOgEfiCpkDnphCKUTEUMkQB5F5J1at6/fBEtT0fc00LY0UVbcdDdYMKIItrgVleOAmmb7ICyPcDtkgFiKNOBIYD2/VZ6OIwCiBi66jt0Q12LY6gHmQtwTRA0fUXh6HmY4A8L1jTiU662fItMGpn2X0E28Qya4rc0c2YivLAwhb22hRAbdvDUttmE79ZsRRtrkKz2I1nWrqZ+sWWs6m8gAbyAfrFX/xF+L6Pn//855idnWVFBoHaUeuBhx56CDMzMwCAF7zgBXjkkUc6fn7zzTejXq/jv/yX/7Ju2p6sWIxulEVNYTssINYdWK63pjQnFWBAyAY9T6vAdHxUN8jLkt9NLLYcuL4PfTh5vesG0/FYYQmEO7Y0zZxAfpBL+LAWfyqAJvuIoNuON3J6kLUE0wBFRSYfhpr1XQhbYGsbbNywuBZYtwg6owZI54onAJgsx1Ng9hoNY9StsFAwc7ZzaXMFrL1EIguo3TVV1rHQtLHQtGG5/oZ2EvJgoALohz/8Id761rfiueeeWyHEkiQJXkbhZ6PRwFNPPcW+PnDgAB5++GFMTU1h7969uOmmm3DkyBF84QtfABC24Pbt24fzzz8ftm3jnnvuwb333ssCWAuFAi644IKO15iYmACAFd8fBZD5YFgAxSdLkVt015Lm5G/UB+ZaOHtHZUNbNvxuIiyANt8N52TD6rhB1KNRWlURBdBq4UXtFMcfjs4gCALWrkxrgZV4NnbjN9zrBn4KDOCNELO3XoIgfPzaMkCxCJpNgUVsutNnCixugUnsdwFgWymUQ/hBLFMYNpZbDiwn/8g4b1jbWIcp4X6gNfu08QIWmjbm6hbzAtqyBdBv//Zv40UvehG+/vWvY2ZmZuDd7Y9+9CO88pWvZF+TEPnaa6/F3XffjWPHjuHgwYPs57Zt44YbbsCRI0dQLBZx/vnn4+tf/zpe+9rXDvT6Gw1qjxR1pSOMkXad/cY4V4uOAmi+CU2RO1ih9QZffC237E3pBdQ99ltrh4WcqqT8Qhcs18MzJ5s4Z0dlUywg6wm6+ZIOaPXPF7AbXFoLjNjYtn1qMUBUYFLhThu0vCJoz8O6iKALmhJrgDJOgfEtML7TXjJCRt7xAiyuAYPrej7m6jYKqgLHDcOfe4Xx8uALoFHQ2lChfPpkEY8ereFkIxyFt10fKZfUSGGgAujJJ5/EX/3VX+F5z3veql78Fa94Rc9Rvrvvvrvj6xtvvBE33nhjrtfofo5RwnJKAcSmwNaYducvoAMnmzBUBTXT3TDxMX9TW267mzIOY7arAGpYbq4bgOn4aEZTcKIA6oTvhzdgRZKGwlSajsdaKBVDgeP5kCUJsgR2w+NbYKeiCFqLXP6JCXK8AFl7YH4AuMFaM0BRC0yTIUWXC4mgQ9fq9N9lLbAuDZAkSagYKhZbDssDGyaD27Q8NG0X1YIaxv74PhQ52w6JL4A2WmsTBHH6+56pMoBwFN4Lgk0zCj9QAfSSl7wETz311KoLoFMdlARf0pWONGqaCFtrpT8ftrrQsmE6HjRfguX6Hdk46wW+AKqZm7QFVjc7vq5b+WI9vCidfDOyX3ngeH6UNJ79xuJFCdiGpgxl8SffElkK2wlulLHkB6HORULsNNx2vE1ZkA8K2+tkgHSeAcpYAfl+KIJey8KRzoOCKrM11OCjMHoVQMQAdWmAAGCsoGGx5bB1KCuDmwXhc/owNA0N04XrBTAy3Ildz+847zfat40vcs7YVgIAnKhZCIJsMSSjgIEKoPe85z14//vfj+PHj+PCCy9cMQ120UUXDeXgtjqoBRYmwSeIoB1/TQPvutPmjyy1MTNeQNvxNqYA4nb19ba7KYuAk3W74+tmxKhlheuFKeTOFhacBEGAnx+vY/dkMbX1lITwZhrqPYYRukjtyrGihvGChgt2j0OCBD8qtLwgwLZKeHymvbZMxqjBdjtF0CwKw8/uPxNqgNY22JgKgiLfAqOJtX4tMMYAdWqAAGCsGN4a62Y+BjcL5hoWCqoCVZbD9yfjG0oeQIRmThbU9wMcmG9i90SRCcVXA37Dui8qgBqWC9PePPYfAxVAb3zjGwEA73rXu9j3JElivdKsIuhTHWQ8WOJiMOhrYO0ZIKKPdUWG7fl4dq6JneMG2ht08vIXVN1yN6WL8nyjqwWWM9jV9YNoimLr3mxtz0fLcXPvEmfrJv764SO4+oJdqBQ02J6PQsbWQfLzRR5ABQ2KJGO8qK1gpFQ51pO0R0B0ul6I0+C7RNBekMmBOAgC/H8PHoDjBTh3prpmx8mcoDWFsTh0c+9XABEDpKsKuutoPhB1mIVv2/ZQazvhmi9L8HIUiHNda0s7Jwvq+D5O1i0UNQWnTRRz/W4SeBfosYKGsYKKmuliqW0zcfqoY6AC6MCBA8M+jlMSdTOZASLztXAMfi1bYOFJun9XFY8cWcaBuSaueN72DaNWOwog09k0uwgetEszVBmW66Nl5dOOeH4A2/NGwuNjrWC7PkzHz61v+58/OoT//ZNj8APgDZftDgugVexk5yK2rlpQoanJ7bhqIV4iN9opfT0RT4F1MkCeny0N/rn5Fj73vWdR1hW8+UWnr9lxUl5iUYt1lDED1LtZ15sBigqgnAxuP9RNB6brY7IUHqMkZTeKJNdyQt4CyPfDv/lEzcTMeGHVwm6yLVFlCbIsYXvVQM10sdC00bK9TWH/MVABdMYZZwz7OE5J0BRW2VC7RNA8A7R2r0+Lx3mnjeGRI8t4Zq4JQ5GxtEHmfXZHAeTm9sgYBRADtGusgOcWWmjlZIAs14PrYVOHwfaD7fqwnPws11x0Azi23IY3hNDFk41QAzRW1DrSwHloisyK2eVTyNTSZiLoTg1QAGT63Ejf2LS9NWWUmQaIa4Hl9wFaWUSvFQO01HKgSOGE2cOHFrBr3ICT8fkXulpgeXVw1NpdatmoW24uB+ok2NwUXUGTMV0x8MzJJuabNtwoFd4YpnhqDTBQAUT42c9+hoMHD8K2Oz+Y173udas6qFMFddYCU7paYPwY/Nq3wM7aFir4Dy+2IEnhwmC53rqevOFEQXxB10wHjhtsil0Ej4XIqGzXeFgANS0vl4MwLSqbkf3KCjvSOeXVhlABcqIWei2tdtJkISqoKobak0kq6Qos1++wjdjqcLj2EBCHoQLZBK785mW5tXaFY7tDAxR+z2Dtun4+QPQ3rmSAKBy3ZQ+PAXI9H/MNGwVNwZ//8Fl87eGj+PUX78GFuycz/f58Vwiw6fi51kcvCOAjNFBcbjmrLoBiGwEJhqpgRzX0T2JeQO4WLYCeeeYZvP71r8cjjzzCtD9APDoqNEDZQCLkakGFzG1AKczPcvO3CbIiCIJ496TLqBoq6paLEzUTEyUdpr2+J6/rd46s1toOHN+D4wXQ1c1RAAXR7goAdo2Fi0HTyhehYLtBVIRuPvYrK2zXh+PlH5WtsQLIhO8Hq540mYt21JWCyjQuSShHI9H0WWb1bNnMINaDrj2ds2TI8rnxBXytvXbWGsRiF/R4lD3WAPWeQGuzFthKDRAFojYsb2gMUMNy0bRdTJU0/ODpeQDA8WUzM9u72OwsJCkpIOuEfuCH/0q6iuM1E7sniqv6THgfJUOVsSvSFc3WYy+gUcdARiPXXXcdzjzzTJw4cQKlUgk//elP8cADD+BFL3oR7r///iEf4tZFg1pgXSLoIscArVULzHLjqZbJosHGGA8utOAHPktmXy90G9tRsN5myl+yXZ8Z6502Eb6fLSefnsd0PeiqzMJgtyJCfcDKz7wfqK3i+gHq5upDF6mlMFZQe3ouVaIZ5eYplAdGRQ4VE4ossUTCLAUQ3/aqW2tnaUGvw4dJ8+3MXutYuyMKo4sBKsVxGMP6zOtm2E6brdtMgN8w3cxsL7mWE8ycSQHUAhsvalhuO+x6GhQWc9KWoasydk+EGUrHayYCxHEqo4yBCqAHH3wQH/7whzE9PQ1ZliHLMl7+8pfj9ttvx3vf+95hH+OWBbmYVgytY1fJWmCev2Y3QX4EfrysYs9UeMN+Zq4JQFp3ITS/CJD53GLL2VTj4AtNmzF2p0WLQct2MzMdrhe6whqqDDfwN9XfngdNy4UiI7fQmxchLzZXH7rIx2CoPXbC5agAymtquZnBWmAKiXUlJoTO1gLjCqA1GCUnUAuszBnp8O3MXvojiyuAuj9+PhF+GGP8QRBE4+8y/vngIvt+zXQybwQoB4wO1YpcpLOC1iZNkeH5/gpNUV7QcauKBAkSzojuISfrFvytzAB5nodKpQIA2L59O44ePQogFEc/8cQTwzu6LQzPj1tQ1aLasQMpaSp7zGqFnmngU5TLuspu2AfmmtAVed0nXtjFJEsYj3ZfSy1nU42Dn4hMEMu6wgIVTceHaWcsgPwAjxxZxuHFUOS7mf72rHCjGAtDVXLvEBvcOTnPhS4OAs8PmDalWuhdANEkWNteW1uKUQJ9NnxrkNygsxT0/Ij0sIXEPKhVTJOzQDSVJK08jrTfNRIYIJYIbw0nEb7txOPvHQVQ22Vax36g4RQSaJuOlysqiQw+AaCsa5itWasq7vgWmCQDeyZLUGQpZmg3gW3EQAXQBRdcgJ/85CcAQlfoj3/84/j+97+PD3/4wzjrrLOGeoBbFfxiPt4lRivo8ceyVicRCToLmgJdlVkB9GxUADWs9Y2i4OlUusDDHK3R30UQZmuxsd5YITb4W2xZab/SgcWmjU988wl87L7HYXtb0wvI9nw4ng9DlXN5oPh+0NHymmvEoYuDwHI81gKYKGrM7ycJ1VOwBdY9Bg+EjslA76KCwDMvDXNt3jeX+/z5FpgkSUy/2LMAcmMGqBusBWa7Q7GkqJsu2o4PRQYeObLMvr/cduAFfqbNAOkLqTjL6xPn+zF7VDFU1C1nVRO/NieClgCUDBXbomMjzdyoY6AC6Oabb4Yf3Zhuu+02PPfcc/iFX/gFfOMb38Cf/umfDvUAtypo8dUUCUW982PQldjWfa1OIp4BkiBh11gRqiyhaXtYbjuw3LUdX+0GP1FAi0/N3FwM0MloBH48GqsmMTuJQPvhyHKbMYPL7c3V/ssK2/UxW7Pw5GwjTHfPuIDXTbfD0+VkJLR03MHOj4WmzfR1EyWtZ9ZTJWKAQk+ngV5u04Fu+rz4mYqhLKw0v3YMU0fDo8W9Bu/XBMRFTS99jcUxQN2gG7njBahbq59iW2rZUCQJjx2vw3L9OGTX8dCys02K0hTkwAVQEMeYKLKEIIgnIQcBibfDMNmwRbq9Eg5/EEM76huGgQqgX/qlX8Ib3vAGAMBZZ52Fn/3sZ5ibm8Ps7Cxe9apXDfUAtypooqWgKR27LCDcwRAL1FgjR00yYSxoCjRVQlFXmA7o0GILTmRWt14wOQaI+u+1TRaHMRcJG6sFFYaqsEW5ZjqZbvQnazFTtNTeXMVfVtiuj//2wNP4yDcew3NzzcwL5EKXAPREPQxdtAacOD0RvdcVQ4WuKj0nu6pGWJC3bPeUSYRnLTBtZQvMzDIFxumzmhlv8HnRitZGWYonZwlU1LSc9A0krTlGggB+vKixNtpic3WbUCcafy/pCh46uAQAeMmZU6ztuti2M2126J6xPYpnsRwv15DMN396HN989ARrt1UNDbMNa2DPMYvTicmSBF2VsWOMCqCwvTbqOqChxU1PTU1tKr+WjcZSO1zQiwkFEH0fAFr22nhoUAusqIUaIF1VsGcyHGN8dr6FAOvrRcP3k6kA2mxxGOQCXTVUGJrMCqB6xh0wL0pcbttb0g3adDwcWmgDAI7XzczakIUuD5Tjy6HeatAFdjbSa40XwwGEpGuQUI1yodrO2tzIRxHUmjQSGCA7ww2TZ2daa9Q6pBF4XZWhdLUwDdauSz9WaoEZCR5Qsiyz6b/6KqdRG6aLlu1FBVCo/3nh3knGdC81HXh9Nju+H6AesfbEsuT1ibvjm0/gKw8dwU+P1gAAJUNBw3QG9mmKZQsSJCl8z3dGBdDJuhVaXYx4AZTZB4gYnyz4yle+MtDBnEqg3mtRUxL1BwVWAK3NCdStASrrCnZHPg7PzDVw1bk7GEu0HuBbYFQANTZZHAaFa1YKYQusWiA7/WwLKJ/1U2vlz8raDDi2bDLdRr3t9l34CTQBM10xcLJhoWa6sGx/4POD2pVjBRWqLPVkgMgwrm0PL5rGiSb+hhFKuRYgd2JNTSqAcrbAbG9NmDOWA6YqK9Lcqahp92Cx6e8opQQ/V6Nsq1p0/Q7q/0Qi8Lrl4pm5JiQAl+6dxF8/fBRzDTtTu3upZTPBMxVAZg6bFNfz0YzerweePIkLdo9DliSosozZuoUdY4Xcf1eHCFoKc/NmxsJ7yImaxTG0qzNcXEtkZoDGx8fZv7GxMXznO9/Bj370I/bzH//4x/jOd76D8fHxNTnQrQbyiynqCpL0l3EBtEYiaIt7fUnCWEHDaRED9MzJJgxVQW3IOTi9wLfAKIF7s8VhkFNrtaB2iLnrGc3U+LTnmumg3YO+36x45mST/X/dytYaBOKR9YmSxnbmS2174FF4ylWqFrS+RQgVQK0hMkAn6xYOzjf7P3AD4PtxPhWvjyFdTd4x+Lx5eFlBn33IAHUWJ3TcvXSMtLZ0t88ItIGpr0LEHQQBZusmCqrM2l9nT1cwXtQYA5Tl+SkGpqDJTJRv58iK5Bm57z01xxi+akHFfNMa6D7DWmCRjlSRJZw+RQVQyLCOuhdQZgbov//3/87+/wMf+ADe/OY34zOf+QwUJTx5PM/D7/zO72BsbGz4R7kFQf3ckp6sP4hFcmvFAEUaoEj/UDJiBih08vTh+eFJXkzZIQ0T/MU0HVmqL7edMNBwk8RhkFPrWFGDpsgYi1onzYyFZGcLzNlUxV8W+H6AQwst9nWemBBigIq6gu0VHQ3LxfwqQheJbav2cYEGwD5Hc4gFkOsFa3ZtrxY8G8G3BkkQncW3htcAtYY0SdUN5gKtyiuiLArRFFivwFBq4ZSM5PUt3sAMrv1q2R7qpouyobL216V7JwCAG/aw++r96HwtGypKBSqAsgXTAp3DNHXTxcOHlvCifVMoagqWWjaWWuGIfh50iqDD7521PYxVWmo7sB2v4zwYRQykAfr85z+PG264gRU/AKAoCt73vvfh85///NAObiuDFP3FrhwwQqwBWpsTqNGVQ1bUFIwVNCawO7rchuX66zYJxveTKVMmnALzRn4XAVAMRlQAReG2xBw0MwaidhdAW80N2vZ8HF1us6+zvi9AfL2UdRXTNGmyilF4uqFUCmpqECqBboTDbIHZXj6H8PUEf73xGWAkgu6lqyG0OzRAa/O30k09ycjQ0PoXa6ZLU2C9C6CGmb1V242G5cJyfWiKhIcOLQEI9T9AvMbX215fVo3WhoqhMgbU8rJPJTa7hmn+7ucnAYQDN7qqYLZm5s6dZFlqSuyjtL1SYJv3pfboewENVAC5rovHHntsxfcfe+wxNh4v0BtxEKqamMdCrIvprA19TNNlRV2BJMfTYGdMhRX8s3Mt+AjWrwDi+sk7qmE/2vECNDKyBKbj4dm55pqGx/aC48Vp4RMlPRrnp0DFbDdO3up+qe1sOTdo2/NxPKLGgXw3FtLMlQwF0yS0jAqgQQrkOAZD6ymABnjjOX9oN3Kbi6IZNfBj7pocFwdMA5Th/ebZy6bt5o49yYImnwTftYZSW7NXsdZPA8QYIHNwBmixaUOWJByYa2G57aCoKXjBrmrHMdYtp+8kFk1BVgyVDVfYOcbgiS2jvfYPD8yzNmXFULHUcjrSAbIgyUlbV2W2QVmI3No3ak3OgoHCUN/5znfiXe96F5566im89KUvBQD88Ic/xB/+4R/ine9851APcKuCfIDKupLoQku7A8p7kTHcFlDDikXYiiTBUGUUVAWnTxbx44OLODDXxL86c2rdIjFM7mKqFlQUVBmm67MR0SJ6t+FqpoNjSyYmyzpbuNYTtXYceTFRChkgOo5mRifcJW4aY6lpMzdoY6CrdPRAHkCElu1lnvKr8QxQNSwsO0IXjXzHskgTewW1pwcQELY0gXBqaFjCdNsNEPhYs5DQ1YCKPElCx3tDrcIs5pW8Bsh0/IFHrXuB9zLrboGxKbCUYw2CgB1TmgaMrt+W4w5syFozHRRUGX8ftb8uOn0cqiLDcj1MleMCq1/2IhXsZV3FWFQAOV6Q+biIAZoZL8DzA5yoWfinZxfwC+dMo6ApmG9ZWGo5TPeUBcSgaZyTtq7K2F418NxCC/PNeIMyqoHWAy2td9xxB3bt2oU/+ZM/wbFjxwAAMzMzuPHGG/H+979/qAe4VdGPASpxDJAXBIN9UD1A1T5pkCRJwlhRw2mRDujAXBOGIq/KKTQPeE8JXZUxVtRg1i0sZhgRBUJTs/mWhVrb2ZACiJgNQ5VRMkIRdOwm25/Fcz2/I5xwqe2ELMEmaP9lhe36HZNuTcuFndHIcJnTzJ1GkyYDjsL7fsDO67FCbxdoIDbZC4L4ul0tLNeDh9AIctibm9WCCnmVC0AFYg1Q3jR4oLO4HxaovVLQVqa5U1GTdm44XsAmqAwt+fOPA1EH03650c1fleP8L2p/tWwP0xHTXWs7YUHcQ8u21OJbYPH61kvjxIMYoKKm4IV7J/HlHx/G3/38JH7hnOnw+2r+hPhYA9TZJiUJw3zDZm7d/XR2G4WBjkqWZdx44404cuQIlpaWsLS0hCNHjuDGG2/s0AUJpKNBDJCRrAGiAshy/Vx5L1nRZC0wldGiFUPF7mgS7LmFJhRZgpUzzXxQ8C0wvnioZXRErpuhZuZ4zdwQ3QyNwIe+MjJUOR7nb1n9RaAt2+vo0/tBWARtpRbYUtPuyJhrZHhfCMQAVQyVTSvO1i14A3iN8O3KfkGoQHjToIfUzNUFSAJhDpnnh/quUcwWo5aiKncyKxpjgDK0wLo+k1p7+F5ATZPG4FdmeRlsYi1ZR8czUmkMELWwB3WydqOoF8fz8djxOoC4AGo7HnZGBdCy6cDlJu+SsBANWJQLSodoO2sBxNue/OLzw6Lnx88tMi1otaBiue3kKvAtTgNE0FUZu8bDv2u2bo68F9Cqy7KxsTEx+TUAaAy9rKspU2CR0M0ZnvCSB9HHfAFW1BVsq+goajIcL8DxZRN+gHUxfzO7RlonuP57vwU3CALUTBfjRQ01M99FPCzwxnrkKzNZIgfh/kXkfMSMSBIYxb3U7j8dspnw9FwDAFgx0bS8zK0RKpwqBQU7xgosdLFmOj3dfpNA04UAMF7u3wKTJIldj7X26ls5ru/DC8Id/yjWt3Suhsxw/H260WVpu3QzQHVrdWaCSWhxDFD3GhozQB6SXpXXKBVS2IlJLg9skGO3PR9uEOCxYzV4foCZ8QJ2jRfC9TwIWCHftDxYTu82ORXsVUOFrsgoaJQUkO3cp8cVNAVnbCtj37YSXD/A95+eA0AJ8fmc1W0mIu+cFJyJCqDjUbt7yxVAJ06cwDve8Q6cdtppUFUViqJ0/BPojwZb0NUV/WuAa4HlMLvKA6KPS1pcgBU0BQVVxhnbQiH0cwuhqHg9Nql8MKEEYKpCcRj9F07LDTUGFUOF4wbMZXs9MdeINSUFVYEkdYqg+4lAT9ZjF2n6vfBGPbqLRx4EQYDn5sMR+D2TYeSKFwSot91MIkmybagYGsYKnaGLeT1MTkTFakGTUdbUvi2w8HXD67FpZTveXmAMUJA9C209Qeecqkjgm2DUxsgiOid2gH57LQJRYxH0yikwKhBsL5llM7mp024XacIklwg/yHXouD48L8C/HF4CELM/puOhoCvYM1WM4zZava91aoGVoglTGvPPeu43WQEU/q1XRizQA0+eZI+RkG+t51l79hyShL1RpNKJiI0f5TVsIGnJb/7mb+LgwYO45ZZbMDMzsyk8WkYN/RggmgKznHyBd1lB7ZayobDPr6gpMKJIjMeP1/HcfAuX7p1YF5qep1NlSWKTBHXL7csSmI4Hxw0wXpBR0hScqJnYM1laV3HpfIevTDQSGi2gtuf3NTU82TCj39cwWdJwcGFruUE7XoCjS+EI/N5tJRxdbsPxAkb/a31YGNowjBVUFDQF01UDs3UL800Lpu3ncuqlYpNiMPoxQEDovwJYaERsQJbfSYMbhcAGAUa8BSYBSQxQhhsaXbPjJS2cMFrFJFUaWvwUWLcPEMcAJb3H3S7GSaDQ0aY92HVIjE48/j4BIGxbjZc0jBU0VAsaltthHEWvApFp1ozwnC3oCtB20LSyiqBjDRAAXHnONL7w4HN45PAy5hsWtlUMBJBynY9pIvJ920qQpJD5adnempn5DgMDFUDf+9738Pd///e45JJLhnw4pw7iFpS6YvdC3weivJch3wODIGAnJeUcASHlXTZU7I526M/ON+ED67JLbXNTYJIU+kkA4c6/X9yBGRWJiiyhUlCx0LJRMx3GpKwHKFqhYsTOwmNFDRKAALFJYhrm2Vi2ypywl7eQG7Tt+TgWiZZ3Vgso6yqW2g6LGehlxuz5AdvtVwoairrCCuS5RpiZZucw7DwZMUBjhViv1Q/kvdKyVj+UsNS0ce+PDuMlZ22Df8YoFkCcCJovgIgBymRLET7HVFnHUssJzQSH3M5tWbwIOqUA8pI1lLzzvJQiQp8o6tFj/cxaGx409Xhs2YQqS7hw90T4fK6PsysGc4tfbjtYatk9mTXSwI0Vww1zkcJeM2ZFUgssCMLPd+dYAefuquKx43V876k5/MoluwEEuboNVER2i8grBQ1TJR3zTRsLzcHd2tcDA7XA9uzZM9Kz/aMO04nN/caLaiKDVmYtsOHn6IQFQ/j/VaNzYmq8qDIR27PzLfheMPQCLAm8q6gkScyQsW66fadO2twOQ1NkuJ7PKOP1wgK1wIoqo4RlWULZILOz3i0AimaoFFRsi27utS3kBm27Pktg317RUS7EQZP9LAKWuc9yrKiiqCnYGWUXsdDFHDQ7CdbHChp0VcrEYFMB1LTdVet2vvHIcXzj0eP4Pz85OpLhquQDpHSLoBVqgfV+A3w//jyoVdlchZtyGmjTZKgrp8CK3BRY0svyE0xp9e9UKV4bFweYhrVcDz87FgaPnjszhqKuhEylJKEStbJYHEYPoXUQBEzXGGbXyShEmrR2xqxIvl1IxRS1wcgUEYGUa4CECqDuKBFdkVle2VzDGmkWe6AC6M4778QHP/hBPPvss0M+nFMDdDJLiD1GusFE0DnMrjK/fuQBJCFe2AlFXcXuiQJkCdHOxFnXFhjtJqgAqpkOLCfoWXDXTLdjEqGkqzhes9b15kJGZWMFraM9Qr4atT4i0NjpVWNjpFvJDZofgd9WMZjQu5ZBG0LvLXlV6aqMXRNhAXSiZsLz/VyL7Dyn1+rnAk2gz3EYN3ISzDcsd030fauFzYugue/r0Xnter2vR17vtq0cnstNe3gxIgRiFkoJDJDB6ZUSW2BdLfck6JrCtJjLTWcAp+S4ALqUa38VDYUx/NRmW26nT0TWzXiTMF4KW2AljRigfBqgiqEyb6SXPW87ZAl4crbB2tP5NEBRUdXlpM3HGZ1sWPB9jCxhMlAB9Gu/9mu4//77cfbZZ6NarWJqaqrjn0BvkKDT0OTUBZgKoDyBd1lB+h9Dk1f4MxQ0BUVdxcx4OKFwcLG1PgUQ0alRIUPpxOH4bLprrucHaFpux99RMVQ0TJftdNYaQRCwFle10CmqJQ+ZfiLQOJtKwfboprGV3KBbthsXQJxZZZYgyMVW7AGkq6FNwumRX9XxmglI2bxpCHNNPgYjW9uMblitIdzIqaBbK33fasFrgJJaYGEBlP77vHv8VDm2ghh2AURtqYIupzpBp06BuZ0t9zTQ9Vuz+jOV3aiZLh6PCqDY/8fFZFFj7yUxZHXTSdVW0YCFrsgoaeGUaSEqzFoZnfpbnO2Jocpo2x4mSzou2TMBIBZD5zkfncjDq7v1rKsydo7FLWoPvUf8NxIDtbLvvPPOIR/GqYXlVuzCnGbDX9TjLJthr5EkKC2oK1+/EBVlZ2wr4chSG4cX2+uiAeKdoAFgR3QBtR0PbduD6yXrREwndOetFOJTWVNkeEHYBqNJjrWE6wdxDEZB79CUUB5Yv0BFisGoFDR2zIutreMGfXC+BT8IWw5lQ8V4gXfJ7l28LLMCKG4vnrk91KnVTRdt28+UT0Xg241ZCyBirNoZTC37gUwB20MMVx0m0qbAWAvM9xOLCgJdy4osMYa7aQ+/lU/6uHJCiGc/Bohay9RyT0O1oOJEzcqkVePh+QF+drQG0/UxUdRwZhQS6gZBx5rEhj1Md4V3EoH0gWVDgaKErfWSRjYpfqYw4Ng0MvRYm2/YKOoKrjxnGv98cAl/9/OT+MVzpnM5Xsci6M57iKbI2Ml5AfmR6H8Ul7CBjunaa68dyos/8MAD+MQnPoEf//jHOHbsGL761a/iV3/1V1Mff//99+OVr3zliu8/9thjeMELXgAA+OxnP4svfOELePTRRwEAl112GT760Y/ixS9+8VCOeRhYoiR2LTkGAwCKWv68l6ygFliSf4ahKihqcTL8oYXWuozBEwNEO7eJYthKcr0ACz3iMEzHCzOmlkyMFePioaSpOFG3sGeqBLVP1tNq0bJctuud7PKVGcsYqMhHM7AWWMuBtUXcoJ9daAIA0+6w9yUDM7AQMTYlXWHTYtPVAqqGirrlYinnKPwCJzhXMk5zUQusZa++BUb6NHONPL5Wi1gE3cmOxBogaoElv3dsM6NIjEFp2Wsggo4YoMQCiI3BJw+RMBF0QpAqD9rA1CItYpppYjccz8fD0fTXJXsnIEsSHM+HGm0ACNurcQGU1sad55LgqWAvRBvk8BwC+p3G9F4VdQXT1QJOROGnl5+9Df+/+5/C4cU2Di+1cUa0scgCu2vNJvAM7WzNgj+iflfAKowQn376adx88814y1vegtnZWQDAfffdh5/+9KeZn6PZbOLiiy/GJz/5yVyv/cQTT+DYsWPs3znnnMN+dv/99+Mtb3kLvvvd7+LBBx/E3r178a//9b/GkSNHcr3GWoIxQLqSenPmnaCHvUkkBsjQkunfcS4S49C6tcA6Ryo1RWEswVKPOAzT9XGybuL3/ufDuOmrj7BjZW2wdTBFPBHFYKhyKG7kWbWOROke72M85qpi53iowQoAloW2meH5AQ4thBqDXWMFQIpbC03L61vgLbfj2BY98hmjzCEgLJC6067TEAQBY2DGChq0jKPz1ULcchgWA2S5/kjeGFgLrOuuSk7Qnt97TWpzE1YsT8v2Bs7TSgOxOLwzMoF0KY7rI0jgq/jonTQNEMAFokbRNFlhez5+fiJ0f77k9AkA4XtQ1tUO3SUxQLUe067kAl01VLYBIImEmXGD3GZ6KRVTJR0lXUXL9lDSVbx4Xyhb+cdnF3Ixkuw9TJBx7Iu85Baadhj7MoKFPjBgAfR3f/d3uPDCC/EP//AP+MpXvoJGI3R4/clPfoIPfehDmZ/n6quvxm233YY3vOENuV5/x44d2LVrF/vHmy9+8YtfxO/8zu/gkksuwQte8AJ89rOfhe/7+M53vpPrNdYSlPlUSkgxJlABZHs+vCEbSdHrFxMYICA026LAyYWmvS5CTbqYSlEBpHJhoss9IiFalosDcy24foAjS208cmQ5/P3I2ZSYlbXELE0VFTWoaud7OhbZDLSc9BuA6/moRTf58ZKOkq6wv73WdjY9A2S7Po5FIsudYwUoksQm3Zq223cB53OQSF6lKzKmI6H8PDcK3w98u7IaeapkAQtEHUIrh16/7Xhwcjjvrhf4MXi+ODB4BqhHE4wKE12NI21aVn8z0Dzw/YAVWuWEAogYID7zq/MYs2mAxrkWXh5DP9cLWKuVNpNtx8W2st5xzk2PxROfZJDZDWqPlw2VFRu0TmZlEZnxrS4zx3/yoqNpsH88sJBZS+f5AdNEJbFiu8YMGKqMAKHv1ii2eoEBC6APfvCDuO222/Dtb38buh73M1/5ylfiwQcfHNrBpeHSSy/FzMwMrrrqKnz3u9/t+dhWqwXHcXqKsy3LQq1W6/i3liBxblFPzgEDwERuQYC+ScF5EefCyImvX9QUNtroeAEbi11LxC2weIScjYj2iMNYbocp8ITvPHaC/X/ZUHGybmUyblsNZvkcMEnqaGuSl0jTSh+fbtle3EIraihqCmvlhf4gI0gT5IDt+jgRvUfTVQOqIrECOwxE7aMBog2DEQvMdVVmQvmTDUqd7v8+dbYrtcztUSqAVqvbodiW8P/j6ZxRQqwB6mqBRQafrt9bl2hxDNBkkdzQs+e+ZQG/JnZPsgJgrSLbS2ZIWJGmSL0ZoFKsVcvDAFmux1jd7RUDQRDA87EiqJnywOqmC8tNPoepBVw2VMaAkvA4bcy/G8QAkf3E9ooB3w/fmxedMYWSrmChaePnxxuZ/j7+vdATrqGCrrJR+JM1a2QnWQcqgB555BG8/vWvX/H96elpzM/Pr/qg0jAzM4O77roL9957L77yla9g//79uOqqq/DAAw+k/s4HP/hB7N69G69+9atTH3P77bdjfHyc/duzZ89aHD5DTOnHO9pulLiqupWR3s8KKoCKKQxUQZM7RMXrYcZHFxQ/UcBPSCTddGzXh+l4OLzUYt/7wdPzTA9SiTQiy2s8DUYmiGMFFYbW6SsTtwDSxb5UQCmShGoxdDqeKsXjsaPso5EFluex0e/pyABuR2R02cjg9M23wBgDpMphOw3h+5c1dJFvV1YNNZMJIhBrQdr26tpWS63O3XB9BAsgeh+70+CptduPkWSmpoqMqUrEnLmDmQmmgW95lhMKINpIuSlGiBZjgJSeDBBtYNq2l6tYna2Z8PwAshROwlmuH0avdB3rtorOzFKXW8lMCU1BVgyFadboebIyQDQtVolaZxNFHZWCxiZoKb6CJjX7gb/Wun2AgKhFHTG0JxvmyLbABhJBT0xM4NixYzjzzDM7vv/QQw9h9+7dQzmwJOzfvx/79+9nX19++eU4dOgQ7rjjDlx55ZUrHv/xj38cf/EXf4H7778fhUIh9XlvuukmvO9972Nf12q1NS2CaAy+1IMBUhUZmiLB8QJGXw4L5Apa1JNzyAqqgqoRuxhn1VesBnQT5C+mbdyERNJN0nQ9WJ6PgwthAaSrMizXxw+enserz90JRQ6t3RdbNnuutcBcnUbYtRWeGBNsB+ml3jjZ7xdDhkNTZFb81dru0BnA9YbleMx8cKqsw1BlFnbbtLy+tPtylO1W0uLrRVdkzESthbCoyWaGyLcrFUXOHGlBx2u6q9Oy0Egzod4evQKINEDdE1JxAdRbd9LZAos7BMTkDQNtLjw5aZKWZ4CSPGjanFC7FwNE12/b9piZYBYciVq+k6Ww5bXcDrMKS10j4wVNQaWgom66WGwlt7uXWAEUF+y0TmbRiLpce5i0d7oqY0fVwIG5JqoFjU3NtTNOU9J6LEkrtWJAeK6EDO0yTjbsrcUAvfWtb8UHPvABHD9+HJIkwfd9fP/738cNN9yA3/iN3xj2MfbES1/6Ujz55JMrvn/HHXfgox/9KL71rW/hoosu6vkchmGwVPv1SLcnBiYtB4xAvdVhFyANjgFKKsBkWcJYMe43rzVNHwQBZ4QYLxDbOwqglTcd0/Gw2LSx2HIgAfiVi08DAPzfx2fZY8q6itmataZtJBpTrXI9esJkicsTStF7EINUNcIJMkWWOrQBw9w5bwRm6xa7KU4UNZSNOO4jtDno/dlQkVDhPJYkScIZ0a51tm7B9bMxQHy7UpWlTEGoAKcBcgYLxiR077BpInOU0BGFwX2f+QD5vX2AeH1NQY3b6VmCjbOCNoVpImZDi9mqpNds8wxQj9ehRPhWlAeWtZ1+OBL90xpmui62V/QV4+q81nGp7SQW17wGju4XpHvKYpTLewXx7cKpsg5JCvU8VDCaTnLB2A2WpSav9GACQhuC2AvIGlkGaKAC6CMf+Qj27t2L3bt3o9Fo4LzzzsOVV16JK664AjfffPOwj7EnHnroIczMzHR87xOf+AT+83/+z7jvvvvwohe9aF2PJwuYCNpIF0EDcZU/7DA5WnSLupzaghsr6GxX0FhjBigUVYbgjSG3Vzk36CQGyPZxeDFkf3aNF3D1BTOQADxyZDk0yEN4wTfttW2D0ZhqpaCs8JWJF1APtpu8CMw34mgG+n0aF98KbtDPnAxH4LeVdciKhLKussIQCCdset0Y6XqpFjpbxnumilBlCZ4foNZ2OyJR0kDC1LFCXGxmAe2cHS9Y1fU43yXKb5ijV9xSAaR0GyEqXAHU4/f5FpgscXEwZn/Pp6ygsW5DTb4B0+YxABI3TxZXpPVmgMLztBElwmcVCR+L1p/tFR2eH0CCxKwUeKhKLBSvmXai2SKtXRVDYwxQnBTQfyqRNrCKLKHIWQaMF8Mw1oblsoLRcrxMmiI+SiQpSy0cUogMXfsEvW4kBmqBaZqGL37xi/jwhz+Mhx56CL7v49JLL+0YR8+CRqOBp556in194MABPPzww5iamsLevXtx00034ciRI/jCF74AIDRg3LdvH84//3zYto177rkH9957L+699172HB//+Mdxyy234H/8j/+Bffv24fjx4wCASqWCSqUyyJ87dBADU9HV1BYYEF/Eww6TowuipCW3wIDQZyJmgNZ2l8oXNwWOIuZHRCkOg99B1c1YAL1vWxnTVQMXnT6Ofzm8jO8+Pou3vHgvFFlCEIQ+O9vXqA3GgkyL+go6mMTMbduDndLKmuOM+eg9n4kKoKW2zdygDTmjC9sIIQgCPDcfF6lAeNMxopiBlu1FifA+lJS/jxjTitHpsk1M0omaFYUu9r85MbaNKzazgN8519uDX48LXS2wVmSs2GsjtN6gm3x6C6x3FAZjgDQZkhy+13MNGw1z9TlqBCpCDS3Zx4ffSCWzxz57XK/3forLMnMjnVm/jGXPDzDLcu8MmI6Hoq4kapUAML1fre32LICqpZgBKnEi6H61BXP+V+WOgl9VZOysGvj5bJ217s2ovSn35MU6jSST3j5ZljBRjlnTUbR7AAYsgAhnn302zjrrLADIFCjYjR/96Ecdxoakw7n22mtx991349ixYzh48CD7uW3buOGGG3DkyBEUi0Wcf/75+PrXv47Xvva17DGf+tSnYNs23vSmN3W81oc+9CH8wR/8Qe5jXAuQ8LFkJI+hE4qsBTZsDVCkt+nx+gVNYQVY1sA9Hj8/UcfOscKKqYck8IGfBW7hmq5ycRhBOHZJPhhBEKBuuTgaJYyT0+pV5+4MC6AnZvHr/2oPpCh4cDYyRcxqZJYVvK9MtbCypUmLm+sHTHvVjTgHLM6mIifsxZazqd2gLdfHseXYA0gC2GdYLYReJP1aI1QAVQtaBwOkq+EukwqgdoZiYp5rNyb5l6RBVWQUNBmm469KyzLfFdJLNgD9bjjrCYo4WOEDRFlgGX2AiAEi9qxuUYtn9ddgzACtzAGj1yYNYxIzyEdh9AIxlQ3bheNnY4AcL86921410LI9bKvoqWsPCz823cQWG00Njhc0tgEo6jlaYNQuTGC7Jss6FCnWwtmRsWI/UFGp9tBQUQ5cODk5mhXQwEvq5z73OfzJn/wJ09+cc845+L3f+z28+93vzvwcr3jFK3ruJO6+++6Or2+88UbceOONPZ9zMwS0Nrhgul70K53kw9aAxAxQ8uIBhMUX0yDlpPyDIEArx9goMUCh7whfAIUXUMNyYTluRxyG6fiwXI/12vdFBdDlZ21DUVNwbNnEz47VcP5p4ygbKo4tt/HMyQb27xrL3PbIAs+PC6CJgg6tq6dYiYoielySbf08l01Fu+xdY6HAd7nthMLbTeoF5Hg+a0fuGAtH4OmmM1bQopiBdDdox/PZDbU7Z81QFHaOzDUs2H4oqC70YMp4tq2QowACQj2Z6diom26m+IEkdPtStR1v5GICqE3VPSGncS2wXj0wXqAsS/EEXdMcHhPQJDPXlBaWJElsKKIfA9QLoW4ntCyoZTRDdDyfscLTFQO27zHNWxLIz6phuisiXfh1dLwY+1Z1iqD7tcDi0NLuzcFYQcN4SWMFuJkxezJugaX7KNHmr53TQ2k9MZAG6JZbbsF1112Ha665Bl/+8pfx5S9/Gddccw2uv/76ddcAbUbQxVvuM4bLCqCMwrTMr08FUA8RtqbIjGbN24Ijk6ysx8wEdV229NMVnX292CUQNB0PbcvHoUgDdGbkPFrQFLzsedsAAN+JxNCyJGG6UsDBhTYOzjdz/S39YDoeK2gnKiujFYiBAqI8sKQxVy4GQ2VRDwb7bBZb6UaQow7b9Vk7gEbgqQDifZ7SCqBljjGpFjqNC/lR+JM0Ct9noV3oeK9zFkAGuVcPnuLeXQC1evhDbRTohts9XUXFgtvHCJEKVipOxvnYkyGtY7QpM3pEWZCuJWmK0srIABU0hU1kLjTsVLdmHo4XsPNsoqhBlWRUjXQmfBvvBt1VYBGTpEgSyrrCtcDiqKR+bynfLuyWXMiyFDKz0duQ1VeIP0fSNgLU/rdcPzXnbKMxUAH06U9/Gp/97Gdx++2343Wvex1e97rX4fbbb8ddd92Fz3zmM8M+xi2FIIjH2isFNZMI2sxIS2YF7QjKhtozB6fCeU3kKcD8INwlZl3saAJMkzs9dDRVZsLB5WanGaLpejhWa8P1AxQ1hbWMAOBVL9gJAPjek3MdEykTRQ1PzzWZF8wwQFNFkgSMG9oKBgiIBbT1dnKi9CJrocU3eDpeYHO7Qduez3Q3U+WoAIpurPT31c30pG16byikl4euykxXdKJuwcswCUauumNFLTWIOA10PTTtwZPNyRyPrrv2COaBxRqgZAbIyzoFFrEDNEHXyw09L2jTUejBYhuRaaCZsIEjBqjbtqIbkiSxgYTFlp1pItdyPDa5VY5G35PcqgnTPfLAiEmqRAV7NwOUJSuSD79OWu/HS1ocveRki9Zgm1ZFSmWAOgYd1iGSaBAMVAB5npc4XXXZZZfBdUfzDx0VNO24mBkr9Ca+6aTMmveSFbFRoNKTxufNtvIs+H4Qjp5m/RUzhU7V5DhLaNnszMRq2x6OLEbtr22ljkXw/NPGsHPMQNvx8MNnYmPOsqFCl2U8eaI+tKkw5itT0KCpyb4ysQZiZexDEMTRDGOcyFdVJDZBtpndoOtth7UIp8rhQkvn3HgpdglOO7+oYCnpKzU7iizh9MnICyjSgvUqgDy/873O2wqtFHgGaLDrkf4eEtdSC2yUQM7v3RYBlAXmeL3T4LsZICp0h8l29ZsCA2IGKMnbhoq0gt5fj0Qs43zTQTvDZvB4zYQfhEWursqYKus92UYKP64lTHwSk1TWOydMi3xUUp+FljSnhpb8XlUNla2zVh+PJ4LFMUBpBWjZiMO+a2tsRjsoBiqA3v72t+PTn/70iu/fddddeNvb3rbqg9rKoBNBkaVEC3ceTOg2xF1iEARs8aj2KcAqHW6j2V+DFUAZf8nijNP4i4mPw6i1O2+SddPF0aVY/2O5Ho4ttxEEAWRJwqv27wDQ6QkEhLSs6fh48kQ9E53dD9TeCfvzcmJLkzQQDWvllEfHTbmksQJKk2W2g9rMbtDPzIUtx5KuwFDljuTuCdYaSS+wqXgKk+BXLldnbg8nO+tWOAbfraHg0bZdthseL+orGI5+oOuhV8HWDxSETG2P0Fk6+3PZro8Dc801HSu2uTF4HvR+hZlV6eejyXl6yZLEGKDmEANRaRNX0JNZDSBu2SW1X1j2oN7/FkhZXvNR5Eq/NiutS1NlI2SGS70HQYgBqpkObN/rWCPmuQEJXY3/UN5QsZ9GlCQPhRTBuCRJ7BhsJ1v4tsWzfCmP0VWZ3cNG0fEcWKUI+lvf+hZe+tKXAgB++MMf4tChQ/iN3/iNDlflP/7jP179UW4h8DlcmtJ798EYoIzeDFnQsj22e+tXgBFt284ojCPQApn1d3hBXfdiRjvlmhm3gVzPR8NymdvqmdvLaFkeZElC0/JQKah41Qt24i/+6RAePrSEuYbVMQI/XTVwbKmNp2YbOHdmdaLouUbIPCTFYBBYAWS6K252vMnjZCn2+ZBliQkna5EQejPiWRqBHysAktSx457gcpbSbui0Aw4LoJXv7faKgWrkpLvQtHsK9ucaNgJE7crSAAwQa4ENztpQsbujYuAJ1MPJtRxP1XY8LDQt7KgaqWPVq0XsBN35/lBLKQBSW5ZAzK4YSmgyuBYMUIewN60FFjEm7YS2FXOez2CFcPpkyADNUQHk+j0tFIiZpiiIfnYLlGnnB+H54fo+9IibWGyutMgAOgNIMxdAKdmPQLzOWm4+I8ReU2C6Em546qaLZqTzG+YAyjAw0BX06KOP4oUvfCEA4OmnnwYQ5oBNT0/j0UcfZY8bZEpiq4Ns/Yua0lHRJ6HMzK6G1wKj3rksJWfo8GBmW042YRzBDwDPR+ZdauwpsdJUa3uZm5CIFi3T9WG7Hg7RBNi2MizXx/aqjtmahUpBxa7xAs4/bQw/PVrDd5+Yxb+9LI42kSUJO8YKOLLUQlFTcNZ0eeBzlaaKqgUtMRQQQIcItPvGQfoYNWIE+QWCqPFl0xkKW7XecDwfh6ObAfMA4t4jWnQbVnpQJhUMZT0OguRBo/B100XddDFXt7HcchJ33cTWjRVC/U9eDRAxpm2rv/lcEjzPZzthcvo23ZztZT+A6YQZeGtVABHD0d220bj1qldLlm7I1HJhgaK2l+qGnhfEYhe09BZMr3gHYp2ztMBOnwwHLE7ULDhuwArENNDGbFsl9AXrxzQWtVAj1LQ8LDY79XDxBqDz/FfkcMrNdn00+kzpNjm2TEo55SnzLAyP7fl0ADpbYGlLp8SZYJJubksUQP0S2AXSEYs6kyl9HkwEnXOR7IWYgVL6TkCUOK+JPDtePxJJZrWN7xip7DokPg+M2kCm42Gh6WAh0lOcsa2EpZaDYjQl4Xg+NEXGq16wAz89WsP/fXwWb3rh6StM3SaKOg7MNVE2VHaDzou5RjzCnubzMV6Mbpz2yhvnyVqcTaXKcoeIeud47AZtO8HIGeb1g+36OBF5AO0cK0CRpI4CiMWEWF7iqDKADjFpkmt5GLpo4Jm5JhZbDmzPx3MLTVxQGF/xXp3k2DpFkXMvxlQAtezBrsfFls02ElTcZg2zJHhBEGbgrWFLlDRA3QU9v145PV4/jrUJH8/HSfQrHrKCtcBUJdXNvsANkaw4RmqBaf1vgbsnSQMURjr0akf7fsB0gZQD1m+dpziMphWKp/mBB9KMVQorJ0wLUQHU6tNeYr5vajoDFKfLZzsf6T3Q1fQCCIivmcYqdHNriYE0QISnnnoK3/zmN9Fuh4vcMEe1typIA1TS06lbQqkj72U4r090qKHKfXOQ+BZcnpPXD4LIuDDb4y3+Yur62XY2IRG3gUzHY+Pvu8YKzAxtumJgoqSx9/jlz9sOXZVxeLGNn59oJPx9Ia385Gx9YLNJilaoGskMBRBqe4BwF9TNAM01yZk4HIHnb9rkBbTUcpgb9GaC7fo43jECL3UU3VOV2GU3lQEy4yT4pPM1nAQLz5Hjyya2VwycqJmJqdbEto0VNSiSlDkJnkATiYMKl09G50pBi8X9YQssX3vZihigtQLdgLWuDRL/fvUaa6brtEgFUJnE7t7QtGyMAeqxjhIDlBiF4RKD1J8B2lktQFMk+EE4Ct/rb7A9n60Jk6XQF6xvARRtxoCVZoh8EGp38UJFS7+Q1maGibkCmyoLcvkApWWxEdg1M+CmYa0xUAE0Pz+Pq666Cs9//vPx2te+FseOHQMAvPvd78b73//+oR7gVgPlGhW13i7QQNwCs4foA8SPj/Z7fdI8WK6PIMe69dixGh74+SycjHR3r4mCac4lleIwmpbLIjDO3F6G7frQNQllQ8XMRBGmG05qlHQVV5xFnkAnEl97sqSjZbmZU5C7EcdgaKnJ4jzT0b0IzHHOxN1aARrtX2rZzA16M8H2fJysh5/Ttore4QEEgPmrNO10kTf5AJX15J2+rsReQMdrZtjakmU8O99c8ZwnuYm9NL1WL1DREsZX5PpVALwLtcaubdPtP8XDg4YL1nKsmFpgRteNW5Lidk7PFphDBVD4N9L5b7l+pry2LKACqNijBUY3davrWD0/bmMRS9ULiiIxV+PFPjqz0AQx/Jwnihp0NRvTyLSOXVYZS6wFvHK9zpoVyd4rPZlF5Z/L9rLpTakA1xI2rTyIAWraw/OAGiYGKoCuv/56aJqGgwcPolQqse//2q/9Gu67776hHdxWBCVbFxNO6G6wMXR3eD5AVID1y8ABOA1SRm8Iwh/e9zj+xz8ewmPH6pkeTxMFWoKgbroaLwwUh1EzXRxdjkfgLdeDoYZTRlMlHSVdZRf9q14QToM98POT6f4/kjRwcUEtGt7EsBvxjXOl2Hc+ReQIxOO3NdOF6Ww+N2jT8ZhGalvZQEHvvBnQou94YUxIUpFPMQDFlNw8TZEwE03pHI9G4SfLOhaaNovgIPCRI2l6rV5gGqABGSD+s6aReitnTpLnB3CDAK0BdUhZQMVNUouc3rdeLAjT10TFxRTnB7PcTv6c86LNNEDpXmZU3HS36/hjL2QogFRZZoLmhZbds+BwvYDbFKkd01q9wAYezE7Pr3h90VYwliwrso83ES+CTnX+1/MyQNQmTRdB03ED+acd1wsDFUDf+ta38LGPfQynn356x/fPOeccPPfcc0M5sK0KEkGW9d4xGEA+u/PMr29yBVif1y8zBijfgk8U8EKX620arB79ZJYHZjpwXA8t24PpeExcuy9igMaLGiRJQlFXsKNqsELvotMncNb2Mpq2hw/9r5+m+P8EA/nsuJ7PdmjjRS21pcgzQN2vs8C10LqjGaYrBlv0Ftubzw360EILbiR8rBbUjhF4IMw2onNwOcUkktqZ3TEYBEmScOb2cBM2Wzfh+aENwlhBw3PzrY78tbl63G4cJBOOpvnCcMfBC6CqocbP5fq5cpI8P4CMcKeeNZk8L+gcTYqJ0LIUQG7MOAAhS0BrWc0cTjI4FSElPd2JmDFAXQw63z4sZtAAKdyY+HzThuWks3Ztx2VrzHhRZ393P1CBVTfdDqE4C0ItrtQA0XO3+0yI0mawl+wiZoCyyS0YA6QovTVAnHnoCNY/gxVAzWazg/khzM3NwTDWJnF7q6BOLbAMDFBn4u9wzh66oRS1dP+M7tfPW4DR7qyVsa3U7RzLg8SifhAung3LRcv2cJgiMLaX4QZBh6fRdNWABAmu50ORJfz+vzkP01UDR5ba+M//52cr9BMy5IG0Cbbrs/dzotSrBUZOuO6KmxafA7Zy6kZmo+LLXeLIzYADkQfQjmroh1LqKoAURWZMyHJKICoxQJVCuth1z2QJqhxqNPi097bj4uBCk938Oti2PhYUSaDPop3TGJTATO0MlU1GeX7sy5UFjuejoClwvIAxLcOGk6IBAuLR+F6vTVOdRY5dqXBi2GG0QqjN1msSjgq47ps6aZSyCJSBsAXGCiBuFD4Jx2sWgiDUS1WLKvTMBVA87MGvT7U2H4SarAHqxwC17bglmXbPoQLI8wPYGdbt2Lutdyu5gzUdwQpooALoyiuvxBe+8AX2tSRJ8H0fn/jEJzrS3QVWghiYJFFbN4p8ATSkta7DQr5PBcQnDudZs2hBNxOmnpLAF0DdO5SirqASicEXmy6aVmiA6HhRBEa0MPE7+omSjvGSyt7rbRUDt15zPqqGiidO1PGx+x7vEBoqsoS2k1+bMN+02cI6UVq5QBGmOBFo941joRnHYHQXUKoiYSJijxZbzqZyg/b9gNkUUCsvqaVCbuiNdrIXEG0YKkY6w1bQVbaDJkdoAJgqGTi6aLLChyYwx4rp7cpeoGM1ncHCHRdZAaQwbxwgbktngeV6UGUZXuAzpmXYYC2whOKAMUApf38QBOy4+PYPMQH1ITFAbcZqpBdAPKvBM0Cd0Tv9X0uVJeyM9HizlDmXUgAdWQg3ZlNlHbIkQcsotJ+uhNdI3XTYe2u7cRDweElL1QD1E8STZqlXHEeBM4Q0MxTWvG6zF3gR9JaZAvvEJz6B//bf/huuvvpq2LaNG2+8ERdccAEeeOABfOxjHxv2MW4p1Lmplj5DWLEPz1q0wLT+LTA+cC/r6/NOqVlzjpgPUIKgriMOox3Sz2Q0dsa2Elw/gKHIHVSzIkuYGS92CJv3TJXw+//mPOiqjB89t4j/+t2n2KKoylKmi74bpCmqGCoKavKUEhAXQGGidGdbkNcQrXDelWWmn6i10yelRhG25+NYLR6BB5IXS3IJXraSW2B0vlYL6QJOXZHZDvo4p/MqaKER33ORIHqZy1zLOwEGAFWKC3AGKz4WuZH+gqYwhiKPoPknh2s4vNhCECRPNw0DvTRAWo/JqvB34wgcvjjhx6FXWwAFQcCu13IPJ+d4simZAdL6jHATdEXGzBi1WS24frob9OElMkE0ICMbwwQA26vxFBgNexBjKEnhGtO9vhTZlG5vfQ0Vi73YMp0zoe0nqgY6x+B7Yay4BafAzjvvPPzkJz/Bi1/8YrzmNa9Bs9nEG97wBjz00EM4++yzh32MWwrEwJQzZBGtRQuMXr+YZQyfMUDZT16ejs0aocE7x3YfkyxLGC929sdZBMa2MiwndGXt1nRMlXUUNLnjYn7BzBg+8EsvgCyFERlfeDDUqymytCKDJwvm2Fi1CkWSUw3PirrKflZrx27QfA5YNSFItcMNepOZITqejxPRCPyO6soReAKLw0hIhLddn91oqz0YIF2VmZvuseVOofu2ioGTdRuHF1tYNikHTMudBA/EGqAAg4U7LjW5kWZZYtdX3crGAC23Hdzy14/i4998HAqkNZsEoxZYYgGkUB5Y8rnYTtHXjLHPebAJOh68LxkxDEkggXP3+hkbr/Ye4SbIsoS928ICaLntwHLT9VckxA/zv5LbiEkghqkWOUG7fsB8qygGpvt+EWdF9t5okhSh3EOQLUkSDC02LewHKiK7JwW7MTbiLbCBrUR37dqFW2+9dZjHckqAKP0wib33xVfgKNxhnTyNHAwU7TAcL8j8+vzF0+/CJDA6VZUS23JT5SgPzHRguwGOcCPw5ADdvTiUDRXTVQNHl8yOneiLz5zC//PK5+FP/+9T+Kt/PozJso5Xn7sDthf67Bhydm3IyY4g1N698IqhYrHlhCLQIIAcaZSYYLKkrRA5Ap1BiYOO6m8EXC/AbMTGbK90psDzoEDUJGaA2DEJyQwZwVBldgMhbRhBkSWUdQVPnmiw558or2wnZEHonSXB5fLb8mCxzSV7yxJKenhONDIWMseX2vD8AA3LQ81yMZ6xcMqLLFNgaRogmuiUgA6n+848vNVVQHz0Qy9Wg9bPbubU7DF1mobpqo6SrqBle1hoOImZc74fsKJ/qhxmA2bNmyO7D9cPUG+Hk2ALjbhgTkoNKHVM6SY/r8vplXoVi0BorNi2vUyatHjN7n0TIfY+r5fceiFzAfSTn/wk85NedNFFAx3MqQDateVhgID+fd6soAIliw8R//pZaFEAHYaCZsbxeaYBSrmYeDdoy41NEPdtL8PxfGYi1o3pagGHF9srLNhfc94uLLQc3PPD5/Bnf/8MqoaCi06fgOsFyJMucLJjqqh/L3yx5aAW3eg1JaS7abc9maIhIjfopU3mBu34Pnt/psorPYAIE8X01gi1jAp6b9dyXZFx/sw4gEP4p2cXcGCuwUJSgXARfvpkaIRZ0hUU1eRcsX6QpJC1qUWxG3lBLbiKHl77LCbACn2r+vkSLXJF11zDwo4xI/TAysgyZAWJ7ZOnwMJjTDNC5NvZClfwTnBu0KsVQROjocpSz5ytzhYYpwHinK6zngWlSGd2cKGNpbbTMV1IcHyfscITxcgEsd8uM0KloLECa7Flw/V9LNCARIptA58UkLbO8oMo/cKvw/fLyVYAOXEUSS/EBZA/ki38zMv9JZdcAkmSVlyopKPgv+cNKe9lK4LG4KspviY8eF3LoE7F3cjTAitwi0vDzPaZ8q6kVsYWGC1ISQsuEO+O6qaL2brFeuP7tpWw2HJSL8LJkoaxooa66TAxMeHNl52OxaaNrz9yDH/6f5/CH77hwtxTVnPcxFG/wMMxutFzIlCyzNcVOdXpmATESy0bbhBS4/omKIBCw7jwXJgs66kCzIlS7AbdzQxQAVTqUwDJsoQLdo/jxfsm8Y/PLuKuB57BR19/IVuTJEkCJQCPF0OmbdBMorKhomaGYvy8xSiJnckygWwBKFqjnzCb3g8gbLU8b2cFlusNtQAKgtgSIkm/onOTVUlodww0xN8f40wkV8tmU/RDP5PB1CkwbsOVlQGiyJWDC23MNyy07ZVFq+PFup2Jkg5DT2a0k6DKEsaKWlQAuR1+QpUEk1Qgm0SC7huKLPUdyaeCsV+4KhCv2YU+6x7p5tqON7QYlGEi85Vz4MABPPPMMzhw4ADuvfdenHnmmfjUpz6Fhx9+GA8//DA+9alP4eyzz8a99967lse7qeF4Pju5wrHe3heHLEvsIs5Kk/dDkxioDD5Esiyx4qJpZ6Pb+VwaM6N2idmqpyzkfGjm8aj9tXMsbKtoqpQaaKgqMmbGC4k9bUmS8O9/4SycNl6A6wc4tNjO7bMzx3n49C2AWAvAY2Jf3pdGleVkBihq7Sy2HHjeYH5FG4GD0QTYRFGDpkgrPIAIvVyyF5txbEw/MWnZUPCGF54OXZHx6NEafvD0fMfPaSc8HsVgZN2Zd4Mlwucc53Zcj2UyjUeeLiWD9BHZnotiEYBQ7O1yGqlhwfMDqhV7T4GlMkDJ7SViAppDEEETQ2H0ETHTNRkWQCt9gJKyB9PAC+3nmzYcz19xQ3dcnxUt40U1k8cQQZElpodbbttw/aBDNJ+0NnZGFSU/b9OK3yulzznPfIUydBuoAO7PfIfvQWj3sHbu5YMi8yd0xhlnsP//t//23+JP//RP8drXvpZ976KLLsKePXtwyy234Fd/9VeHepBbBXwRQ4xAPxQ0BZbrZxKmZUGTM8XKsgsuaApMx2cXUj/UuQLI6jOdQCDaPM2dlzw4mpbLBKOUAG+oSs+dzVRZR0FVYDreCqG0IkvYOVbA0WUzNOLLuUPhHV/77d7HOLEvvSdzXDaVluLMTXlgjSiuY7N4AT03F7acdo0XECBgAstuUHGblAcWM0Bq35ZVQVMwUdLwxhfuxl/80yF8/vsH8KJ9k+wmuMwZKqa911lAmpOGHbcys2CeMwWtFsN2J9k7tOxs8QNLHAN0ZLGNAMOfBONv6r0KoLRCnGdX+Hd4YogMEK2FlAGYhgLnBM2/v72yB9OgcTqzOfIC8jrbjy3HZb5gY0Utsws0EG7IWByGGbKhi61O0Xw3Skb/KWEqOvqFlgLx+5WlBUYFcL8wWX7jM4hubq0x0DbokUcewZlnnrni+2eeeSZ+9rOfrfqgtjJed/Fp+Ff7JlHIuDsoMgZmSBqgqEApGekW8p2vny1wr/v5gTwtsN7BhFQALbUdPDsXGyBajoeS3jtssFrQMFXR2cLUDQpqXM7ps+NxO7QsY9VsB8wFojIGqZAezbCtrLOb/0LL3jRu0AcXOQ+gIJ3dmyzH70s3q7DcJrZS6WtcaKhhy+UNLzwd2ysGZusWvvLPR9jPl7gR+EFiMAjM2C2nrwl91mVdga4qUQEUP1eWomCZY4AOL7ahSPLQsrUIfGtLTRDe0ueYdq3wLTC+PRRrgAbzUOp4DY4B6lUAUfHreEGXD1C2IE8efOYceQF1R2wcWzIRIGxnVQr9WctuTJa4PDAv4Hyjkl3QqRi3e2Q10sa1oPbf8Gb1FQK4FlgfBkiRJTZMQzFQo4SBVoJzzz0Xt912G0wzHjm1LAu33XYbzj333KEd3FbDZFnH7W+4EL/7iudlvjjivuxwU5QrhpopDJJ2MVn6wkDneHD2Fpjf8Vrd2FGNdTDkLrxvWxmO72O8kCyA5rFrrADXT56kYz47Zr4CyPFiX5mxQv+x6vFirPeg92SeFzmmFAihG3R0jK38LNVGgbyadlQNKEp6kUohk03Lg+0mT4GVjXQPIIKmyJAQCq3f9bJ9AIC/+ufDTIjN/JaM/oL1XmAtsJxMBrF9lUKo/QtF0NE5kTFbbJkzTFxo2XA9P7M2Lyv4a6A3A5R8vJ0j5vH36Rxu2e6KzzkvaJOla3LP84IxQCs0QLFQO2sBxGfOzdYsBMFKL6Cji5wHkCTnNtuk3MO6GbKhxJikMUB8wdKPATJ65IARCpyvUL+8NtqsGBlYLhq/T8v720gMtBJ85jOfwd/+7d9iz549ePWrX41Xv/rVOP300/Htb38bn/nMZ4Z9jFsSWadQmN35EHZ6nh+wHdpYn5FI9vo5vCEAdExHWBnH4G22m0grgMKbpOMFeCZqrZy5vYwg6D0GS5gs65iuFnCiZq44HsYAtfP57NiuxxaoLGPVvAaCGCDKAask5IARVCXWBmwWN2jX85kh4XS1AE2RUgXuPAPU7cZNN/w0gTgPXQ1Hjh3Px8uftx3nnzYG2/Vx9w8OAIhTtSuF/nqtXqBIhzCMNPvvzXN6MU0JLRNY2LHtpe7gedS6dtBzTSsxYHc1oPNLkZNHxOlzTCvEic3tZmcmh8gAERtd6NMC4zVAHVlgpDlUsjlBA2GLau9U6AXUdkI9VzdjeSQK391W0aFIUm6mMY7DcGC5PmMtK4aSyDAzEbTXXwRdUDPYnrDsyf7MPWPtM1xL8aZhuOfqMDBQAfTiF78YBw4cwEc+8hFcdNFFuPDCC/HRj34UBw4cwItf/OJhH+PWg9TfQpxQyqHM7we+iKkWst0EijkZIL4FljXCgwqPYspuolKICwQ/CBfX6aoBWZIy7eY1Rcb+XVVMljWcrFsdi+EUXwDlcPddaseFzFRJ71vQTpYipoOLB2HhmAk5YOzYZTnWBrTXvwDy/SC367HrB8wle1tZhy4rqTeD7REDFAQhRc5rxohhK+v9GSBdlaGqMhwvYAJ3CcADT87hp0eX4xaYMVgMBmHMIGfbfCJoNtJc0BjbN5YzXb5bQzFbsyKzyOGxQI4bu6MnMcS0bqV5+dA60c2u0Dlsuf6q23akpezXAktlgGzSKfUO8uzGVFlnm5GFprNiU3o0KoCmyjrUjDljPLZXObsPJw5arhaTPcJY2Kub7gNEmswsDBArgDJ49th9Jnd5lNngQL5Q7fXAwEaIpVIJv/Vbv9XzMb/8y7+MP/uzP8PMzMygL7MlIUvZDbiIlmw7vb1CDi00YagKc8RNQsdIZI8MHR5FrgDLMvbboQHK2AKji6mYspvQFBnjJQ1mZDK2b1vo/6OrcuZU77KhYv+uMTx6ZBknGxZrq9HOdLntMAv6LK1BusEXNQXFDAwFa4FxYt8FXkOUclOWZYnlXC1vgBv0fNPGbM3E+bvHM/9O23ZZcTdZ1kLLhbQQRl1BQZNhOj5qptMx5l/jGKB+lhGaIsOIjNwA4OzpCv71eTvxzZ+dwF1//wy7adII+qCoFrm2VY7dLGW+lfU4AoN2xmbG5yIT1WohzLk7XjNhez4sx0epfyc4E0hjpspSokBYj87TviLorgkr3qtrqe1mvs6SQBu5Qp84H6YBcjs1QMSC9yugVjyfpmBbRcdS28Fiy+4QC/t+gNnl8JwPXaDzF0Bk91Ezw81YnUxSC1ri38mPwae1lui8L6j9ryEWrdFn3Xa5gjJtuIEHPzk5ahLG4TpodeGBBx5Au91ey5fYlJBlKfMulFT2Zh9a8kTN6huqGF8M2R1Ks9qts9cYoABigrqUXB9FllgLCQgNEC3XR5G7mWTBeFHDuTNjUBWJCQxpZ7rUcuB62b0qyOV4rKBClZJH2HlMMg1E3AJgFHchWeRImN5AN2jL9VC33I7w2H6YrYeJ2LIUTnD1CmGUJAlVIy5C+UKAWj793h9CWVc6bszvuHwfyrqCZ042medSpaCsjgEqxK2cPCLoBU7PRDdmJoLOoLkAwukgANi/swoAOLo0/EkwvgWWdL+MRdDJx5tWXGiqHEd/rDIQleJ2DK33GDtjgPzVRWEQ+FH4hYYN046nXB3fx1wzNkHM4wJNoFb/ctuB4/psLR0rJrfY+QIojVlhLbCcDFCv05E/37Iw8DQ40Mpo97CeWNMCSCAZCqS+1TiBJbL3cFU2ndC+vJ8gssHo0OwTCsQUmT3s1nnw02KeH8DuEzIapkf3N9WiQgUAztxWguX6GCtkE3J3P8+5u8bgBj5qbYcVJrbno25mt+knBigcYe9veEZal5btwYp2bNTSGIuiEdJA0yfLkRv0egoJTceDldPEjPK4Jko6IAV9WTqyhKhbTsf7HyfB92+BAWFxwQeqjhc1/PqL93Y8ZrJkDBSEyo61EOt28tzESYTNC1rHuJiALDcGej/oRnkkysQzneFN17AWWNcUF0GPrtE0DVCbtZfkFVOmVPBRHMygiN3ss02BBUHndJvZoVPK/rq6IrPNyFzDgu3HmWCOF2CeuUCrKGpK7rVpx1isdTxRN5kf01gxeX1gafduelQSY8syhW/zLbX0z4cvgPpNZwJxC6yV4PW10RAF0AZAkpHZh4Q3u0o7eUwnFOS1OX1JEmgBzbIbSHr9LDvebsfqfuJpfmHq5WjLF0D7tpfhBz4qGYXc3dgxVsDzd1ZZG4OmFEKDs4wMEGdimMWJl6bN2o4Hy/U6pjwmUnLACHwchhusNGBbSzQtD44fpDr/JmG2FkdgSOivE6BCoN7uFBYT45GUhJ0E+hz4AvHfXDiD0yeL7OdFXR4oCLX7WPO2wMjUsVKINUi0M87eAgvfj+ftDGM+jiy1oUBKjGUYFPQ5q2kMUJ8WGBuDV2V0N9FYInxC8G0exEaISs8CxuDYCZPbiHV4FeVhgDgvoJONcBSe3i/X400QdRR7pNSnYbyos2vl0EJo91HUFBgpI+y0OQ6QPrpOY/DFPoJx/vm6nbO7QZozRZYysVwdrKkogAQUGbkZILMHLdmyvbAI8tNTioF4AQ0nAvIVQFmEcXQsPPpFePC7iV66JOqPA8DeqRIkSKmTU1mwe6KIc3aUsdy22YjuYsvJ3OqJXZy1vhbzQDxtBgDLLbej3TNR1DMxQItNe13doIMgQMsOzRfzvCaxY5MlDRJ6+zQBsUlerYsBopZttahlYoDGI/M53opBVWT8+5efBSD8zFWlf7uy32sA+cMdl9orGSAKpwzHjnv/vs25Pp89XY4m3gLULAeNKEtsGOhogSX8nD5Lzw8Sb2amExcn3UtclQtEHUYBVND6TYHFJw0fXtrPeDUNuipjJjImna1b8Lig0YYV58ONF7VcLtAEvtV/OBqprxTCINSkQo1fd9JsUhpcC6xv9qPGh6v2YIBYC1FCFidJMvxs26MnghYF0AZAQvaMmCy0ZN10oMrhBEwvPQArgPqIB3mQk6fZY9KAR3fB0+jDAPGp0kaC8RqBAlF3VA0WrJk2NZYF4VhrGWdNV9gFutx2OlooaeB3e1kZIF1VWL+81nZYm6igyX1F1OQG3YySmtfLC8j2fLh++G+QAmgimo7r9/7weWB0YzSduJgfK/TPzQNCvdGeySLqltNRELzwjEl84k0X4fpXPx9KSuRIVsSsTb73hHeips+6wpx8+zNA/ATYtpLBRPyzNbOjOFotnA4GKGEKjDNCTDrithNPB3UXJzQIkBR7kgd8oHOvAkiS4nOPZ4DYqH5OPyhFlrA7YhNP1i34QTzAQdezrsgoGwq0HmtZGlRZwng0lHE0ivwJTUDTLDJinVGaTQoVi0Vd7TvxVjI4UXWP04klwcvZOgl8HphggDg88MADuOaaa3DaaadBkiR87Wtf6/n4+++/H5Ikrfj3+OOPdzzu3nvvxXnnnQfDMHDeeefhq1/96hr+FfmhyNnDGKkASSuAgiDAUstB2VA6diRJIJF0MUM/mFA0+muQeHQzQP0yzDqzg9IP6ryZMQDAxXsmWARGFg+KXpBlCWduL7Px06WW3fP9IzhewJx9w4mPjInPRqx1ma2HC1y1oPUVUU+U4iJrPd2gbTe6yQexNiQLqD04XtSgqskp8Dxij6Q4J2050sxIUqghyHq97BovolLQWPuM8IJdY2w8edAgVAAY51qZeVqRJOgeK8YTf3Q+OF7Qd7pvKSq4i5qCgq6wVsxszQonwdaiAEr4Od2MHT9Zi9YZNNr5s2ExQO0OBqj3Y4kF4i0uTFak5V8/9kyVoMgSXD+IJkfD5z26FHsASTlsTnhIksSmUmmkPi0JnlDo49PWyjgxB3Ai6B6+QkBc9KlK8jnSDb4FtmkZoBe+8IVYXFwEAHz4wx9Gq9Xq+zv/7//7/2Jqair1581mExdffDE++clPZj0MAMATTzyBY8eOsX/nnHMO+9mDDz6IX/u1X8M73vEO/Mu//Ave8Y534M1vfjP+4R/+IddrrCVkKbsIutQhgl75c9Px45wrKT2lGeCmwPrsnHjEBVg2s7bunUi/XBm2m1B6CxIv2TuBP4xctC3XQzVDmGwWqIrMvGiy+uzYns8Ej1NlDVrG46AbQN10cSLSyYwVVKh9RNS6qnBBievnBh3a/Yc5Xt0mhb1ArsfVggpD7d8Co0W/YcVtQZqaKmnKiliFXihoCvZOFtGwnBWLuOcHqe2ErOgId8yovWnbLtPGTHCxKbyJZ78JzsWohVbUw2uX2qLHaia8Abya0kAuzYqSnB1FDJCbwgCZKVNgQGcczDAKoKLWP96BFUA2XwBRAZW/SBkraEzPt9h0WMjuYc4FWpHlgcN2yRn9eMQohS2w9EKNFUAp5yJpgEp6difoXunyQMygZZ2iG8updVtPZP6UHnvsMTSbYQzBrbfeikaj0fd3brrpJkxMTKT+/Oqrr8Ztt92GN7zhDVkPAwCwY8cO7Nq1i/1TOCX6nXfeide85jW46aab8IIXvAA33XQTrrrqKtx55525XmOtYKgyxopaprYJEC+Slpvc52/ZLizXZ6JDs0fBQaZYpVwtsLgFl6V6j3dnFKzXTwPEJTP3OCZNjkdQXd/HeHFIxicApsfIf8PN5LNjux5rgW0rG5kZBVoI6qbLnIH77fCAcKdF02pLLXvdNEC264e7WVnKHMXiej4WuPZgOUObcpIFonpMg7XAkuCztRh57BwvYKKorzAO9Pxg1awhH+7Yr2ghEFsoS0C5ELNZ5F4NIDWrjkDBmBRiPDMetmIOL7aBoLOVvBrwDFDSzY1E0K4fJN4k25zJYHdRT0X8arUgjNXQ+xfGxPLw9hHMdyxrki0HTY19uRZaFtvgUcEyWQoZvkFaYEDsBk0bw7Lem/2kvyFto9nmWmD9NozxVFlvy5XOTWuGFliBb4H1ffi6IrNS65JLLsE73/lOvPzlL0cQBLjjjjtQqVQSH/v7v//7QzvAJFx66aUwTRPnnXcebr75Zrzyla9kP3vwwQdx/fXXdzz+l37pl3oWQJZlwbIs9nWtVhv6MRNURcaZ28uZH1/iNDhJ1XPb8RAgZJV0Re45ddXhCZG1BZchcZgQBHHUxkRRx3HH7Jsib3KCul7XkqJIUOTwJoYgPTZjEOzk/DeyuEEvNG22CGyrGJlbYCwR3ooZgWpB66tF4N2gl9fRDdqOUrRVRe5rxElw/YDdrKtGtkTsKc4jiRhMGhsv6vlDJQ1VwelTRTx6ZBnjRY0t0iEDtLquP4U7tm0vcwHEF7uaInfovUq6iuW2wzYnaaD3o6QrMDQZuydDBujIYhuKLA0lKgeIHZ7TvJKYE7QXJAq3zR4OwWN8HMwqWEy6dsoZzFzpOPgCiK7xQTSEsRdQHfPROmC7cfQLtcQHaYEBsecXodzHubxfgjvdD7JsRPix+l6i+njTmk0ETUG4bcfLbDOyXshcAN1999340Ic+hP/zf/4PJEnC3/zN30BVV/66JElrVgDNzMzgrrvuwmWXXQbLsvDnf/7nuOqqq3D//ffjyiuvBAAcP34cO3fu7Pi9nTt34vjx46nPe/vtt+PWW29dk2NeLWKzq+SqvNZ2oEYLvKrIaFrpNyoSQZeM/tQxgRVAGaZeeK+gybKG4zWTmbyl3Tiz0qmhdiO8EWuqvKpAy27s5Hx2srhBH12MTRBLWnJOTxLGOA0EiQErhgqjz2IpyxK2VSi0NRtLNQw0bTey9Jdge2H4Yz/dRNt2GZsxXtIyOcVOlOP3hQpLMoksG0puQzkgDNCdKLWx1HJY8egHwapywAiVqACqt7MVhXONuJ3Rrf8r6QqW207fjcJSK2bESprKcqkWWuFkYL2P1i4r4iiMlBYYC0Pt7QSdxK7QjbCZ00SyG8Rq5CmA+GuG2LLSAJsoXZWZX89cw4Lr+mjZLmv7TpZ0aAPEYBCIXSJUClkZoOTPn96ritHfMiQegw96M0Apgbdp4P2uRi3LMHMBtH//fnzpS18CAMiyjO985zvYsWPHmh1Y2jHs37+ffX355Zfj0KFDuOOOO1gBBGDFgtRvkbrpppvwvve9j31dq9WwZ8+eIR754ChyLajuqtz3Ayy3XfzkyDK++JWf4PpXPx97pkKTwCSGhEYiS7qa2QCs8/V7P5Z2GxK6x4WBtHtYVg2QpoRjnC3LQ8lQBqKv07CLfHZaDjw/9NnRe1DYBxfDVvC2igFFye7qPcEFQtKCXDHSc8B47ODcoPNklq0Gcw0Tn/zuU/jF50/j4j3jcLwA/bJnZ+tWxEhS8Gf/v21bOZ4Co/YE5SCVdDWT2Vo3dFXG3qkSfnJ4GZ4fhDcRCasSQBMqBRUnGzbTsvT7/NnEoKGtOK9og9Ewe8dD8MngYRumgPGihuW2g5NNC+WiCsfzB77xEpgPkCKt8PEBYq8l109hgHrk+rFEeC4OZhAQa1zq4TBOoAKcb+HGzvODnVe0YTpZD33DmrbHgo3Hi9qq2OntXQxQPw8s6hCktahbjC3rf17wDFAWI0QK9e0H0s1Zjp/LT2w9MNDV4vv+uhc/aXjpS1+KJ598kn29a9euFWzP7OzsClaIh2EYGBsb6/g3Kih1CNM6f9aOXHq/+8Qs5ho2fnhgHo6ffpKRaLPf+Gja6/cTsFGLTVflOMKjD3PEe0r0milQZAlqRPVnLRqygvLT2o6Hpt3fDZpGVLeVw9TnrIJHZqJnuXE4ZzFbkUBTP0utmKVaS3h+gH94ZhEPHVrCVx8+AtcDnAyTRrwLtK71nwADYosDy/VZq22ZRUdkn1jsxnTVCLOboucC0ls7eUDMQytjICppopL0XnR9tRy35657ud2Zi1YpKEwIfaJmwnG9oUyCxRqgZAaIjt/zfAQJMmgqTpIYWj4RPsu0ZRJcL17fKv2qcaQwQNEGYiAGSJGZ/mq2ZsJHgKYVZ9+NFbO1fdOwo9JZAFX7MEBUbCVF5LjcVHC12J8BoueyPR9ejzWwk7Xv+7SM+Q7Qfyp4vTHwXeTpp5/Ge97zHrz61a/Ga17zGrz3ve/F008/Pcxjy4SHHnqoI2z18ssvx7e//e2Ox3zrW9/CFVdcsd6HNhSw0cSEqjxkElw8czJkJGZrFlwvSF1cGow6zt4CY+ZYGTRAROMbnEdP3wKILia1d64PEF6gAWIx8bAwwSV0zzd6u0EHQcBu8lNlPUy9zvheThTjFkCN84XJ8lmQF9BS22Ys1VrCdn0cj0ZxT9Yt+MhmhsibIKqylMlsbqKgsZvtciucBOMZoEHDSzVFxp7JEiw3FFdLwKqCUAmVQuxnk0XSQAVQ2VBWtODKbAffe0KmowCSJRQ0hbViTiyboQfYEFqj/cbg4zT45DYJKy56MUB2PgsBHi3ub8xSAFEhRqxpEARs0zVIO1SSJJyxLWo/Nm04bhipQ+z6REldFQO0syvMerxPTA5z6k9wWebfq2qGNZNn7XqJ6m2Otc8yCG+osQljP7H/emOgO8k3v/lNvO51r8Mll1yCl73sZQiCAD/4wQ9w/vnn43//7/+N17zmNZmep9Fo4KmnnmJfHzhwAA8//DCmpqawd+9e3HTTTThy5Ai+8IUvAAgnvPbt24fzzz8ftm3jnnvuwb333ot7772XPcd1112HK6+8Eh/72MfwK7/yK/jrv/5r/O3f/i2+973vDfKnbjiKPUYT27aHkw2LXXx0Y04rgIihKerZjOU6Xt/LwADZcdZYR4RGL1MtzpW1HyulqzJ0VUIpw8KXB5oqY6KoYbZuYbFp93SDDj2A4n5/njBW3vCPbmjjhd4u0ISd4+HNbrEVmjW6vg99DW28bM9no/ot20Pb7u0yTqAYjNDdOpvpoK4pqBhhwjmZUdJCWc7hWZWE7RUd26sG5hp2rhDiXmDp1hkZoEWmZ1o50VYpxBqOXhuFGtcCU2QJhhozQEeGGIpKhUnaFJjGojDSfIDSJ6yo1Wl7ft+InDRQEKosZRtjpyKH1kSbG9/Pa4RImBk3YKgyLNfHYtvpmOor69kY3TSMFzXm8g2EzE1PDRBJFCLvHpkrSOi9UmQpkzM176zf6lFMsxaY2ntwhSBJEsq6gprprvDn2mgMdCf54Ac/iOuvvx5/+Id/uOL7H/jABzIXQD/60Y86JrhIh3Pttdfi7rvvxrFjx3Dw4EH2c9u2ccMNN+DIkSMoFos4//zz8fWvfx2vfe1r2WOuuOIKfOlLX8LNN9+MW265BWeffTb+8i//Ei95yUsG+VM3HNTjdf1ghRndUtvG4UiQC4SjmOEOJ/nkpQsibCvka4EBsaAuDWzKjEt+NvvZqkc7M12R++4lCqocOSoPT/8DhG2RiVJYAPVzg3Y8n8VgTJS0nAVQ7INCotWxktozB4xADFDb9tC03HVhgGYb8WTkYtPONGpN0zDjUYBj1lZltRAWQHUz9O+hhbKUo1hPghqxQHP1JRirdIEmkKC0lTEQNSkIlVDmNBw9C6Do/SgXwnFmg3MlPrzYhoT+12cWxCZ3KS0wpgFK9gGyekxYTRQ1SAhbIcstJ5OAvBsk9g0tBPqvAwWOQef/Gx7jYBupgqZiW0XH0SUTS00bXrQxmK4YgJQtHysNmipjvKgx64TxPjE5nVmNnT9j4dcJnkxJIGdpxwt6RhjRe6grOfzkDBU102Vu78PQ4g0DA50Bjz32GP7n//yfK77/rne9K5ffzite8YqeWoa777674+sbb7wRN954Y9/nfdOb3oQ3velNmY9jlNFRgHBmdG6UXn5wITaktD0fDdNL3V3R96s5GBR+J1e3etOX1N81oh09EFLPvRZ2fgy+38WkKjJKmjLUCTAgHDMnn51+Y+aO52MuamlMlPIJHuk15hs2Yw4milomDREVW5Yb+uxkzSwbFLzZIxCKkrOYIZ7kXKDzvDdjBQ1HYWLZdDsYoKxBqL2wrWJgx5iBpuUOpQVWLcZtqyzW/otUABVW/i0sKbuPSRwFGVd1lblZ74kKoKPLbajScEJRHV4EncgAxVlg3Ze15wesMC8nMA6KEm6MmraHupVNQN6NJrupr8waSwKxPDTaTVogCb2jd3o+pxqOwh9dMjHftNn7vq2shwGhq7BaUKM8sLmGHUoJ+pg99opK4ovFrKd9QVXgeG5Pk0/m9t3HuoQH3Q9a9mgVQAN9UtPT03j44YdXfP/hhx8eGXH0VoGhxswIv8C1olTxZ+eaHY9fbNlsFJ6Hy9nl50lRl2WJsRytPqO6dc5nqGTEyv+eLTBeA9TnmtBVORwbH8IoMw9Zlhg9v9x2eop9W47LdvRTZS2XGHsqYoDocyhqIZuVZTHQFJkxSMttO1Nm2WrQslymXQFCBqidgQFiLtBFDcUchSpNDTZMF54XM0CVwupaYEDYAjh9shTlcK1+4R0zYi1XlhYYCd4rurLihk/ajLCYSn+ODgYo8vw6bbzIduzLbQcta/VZS3Repbmbd06Bdb4WLzROm7Ai/VQ9Y+5eN6h1bGTU3hU6vG3ilrvaJ3qnF3RVZuHM8w2L6d6mytEI/CpOWEmSmDFo6BvVe9KqyA2bdJ+KpMksZEiCJxTYWH0PETRtWjMyS0DYdQCAhrU6C4RhYyAG6N//+3+P3/qt38IzzzyDK664ApIk4Xvf+x4+9rGP4f3vf/+wj/GUhiSFgse243XQkqbtwXR8PBMVQNNVAyfrFuYaYTZQ9yg37zNSLeQrIIqaAsvt37dvRAxRQVXYCd9PPG1y4Yn9rqWpsp5J+DgIaPy0ZrrMzC0JJ5bDEERVljBW1HPR3XwiPACMFdW+OWCEsE2n40TNwlJ7dWPEWXBwodlBqS+0QtM31/NTiz7eBXqsoKGQIxGbiruGGd4YifGoGNpQWJvpqoGJkjaU+BS6ftq2m6ngiINQV7YzqJ3Wb1igQQxQFKUhSRLGihp2VAs4stTGyYaFqYoOy/VXFRLMt8CS0MsIkS+A0o6haqg4AQt1s7fmKQ3LrbgAytIaJV2LHWlk+Jb7oKdCOApPXkA26K2aLOngA0oHBW3GKhmCltk6m3D+tJgms38SPIEZK/Zge0lQrsv9ZQuEvG3j9cJAd5NbbrkF1WoVf/RHf4SbbroJAHDaaafhD/7gD/De9753qAcoEJ6UbcfrcPtsWi6OL5vhgqcpeNEZk/ibR4/jZN2C4/mwXK/j4qFUdlWWmK4o++srQAaztqYZhxRWaWHv1wJjI5X9dynaKhxW+4G8gEIzxPTigkIPw92enGnKiUAiaMJYQeubA0YI23QRA9TqzVINA8/Od2b9zTfCtpvjBUgj4HgX6PFSbwfbbkxE0SZN22PtXSAsEodQ/wAYLKAyCWNcunUWBogFoZZWaoBIBN3ruRzPZ+wbPzVYKajYOWbgyFIbx2sWzpquwHK9VRVAVFin3cTp+0kaINo49AqcpXHshu0OxgBFhWCWIFR6HBDbiJgcezFoJpymyNg5HqfCUxEyUQqnSVdr0UGmp2Vd7asxLHWJoHnEzv/Z2oX0WCB+n5IQi6CzT8B2tMBGiAEa6JOSJAnXX389Dh8+jOXlZSwvL+Pw4cO47rrrOk6q73//+x0REwKDgRa0JlcALbUdHF4Mb1JnT5dx2kR4QZ6omYmj8HwQat4dNb1+P7v9RkcLrHeIK4Gyy/QMGqC1xM5qXACRF00SSHMVhh7mu6kqssQWSyBygc6oFwjbdOGus96HpVotwhH4kNan9W2+GVks9GCeOlygi3q+AqgUu0G3nDgVvmpoqxJBrwXI16Tf6DoQXnf0no0XV7JZVZ4BSnkufnR4vKixNZafBDu61IYfrD4Vnk/6TkKvFhiJsHu5ujM3dDMbe9YNei/0jAUM720TINYAaSki7yzQFBm7J2IPJmL4Jkr52r5pOGMqjEraVtb7brDKvNSg6+2scyLorNdQ3ALrIYImDVAOrRO1elsZdXPrhVX3E6rVaurPrr76ajz88MM466yzVvsypzRIiNyOTkrb9dG0YgH0OTurOG08TocOsDIVntpThirnFh72C9wjUIusoCnswrT7MkCxJ8dG3ubIU2U5coN2/SBxF3w08sbZFmX+5H0vK4bKmDRKS8+K6SrFYfRmqVYLx4sLoLOnK3hytoG5OvkPpb/ubC12gR43som7CcRuNS0Xi00uPDRHbMt6Ibb27++OfjKKwVBlKdF/i2mAEqZ4CCSiLkSiWIKhytgVMRFHltoIAmnV5wVjgFI+O7ohBwFWTCIycayavpkZK8YWAoMwQItcCyzLaVHs1gBxLsar2XCduS0sUuqWCyt6z8K27+r1ib90/i6cqJk4d6bal03qld8VT+Vm1wDF95osY/DZr++YARqtFtjaGYkAa+5We6qALipigNqR/udApP85Z0eFuRnTKHw3A1Rvx8VJ3gufaNb+Y/CxCVolowbI4nZkw9BnDAoyIKtHQaVJYY2+H+DEcshoTpVDn5s8LTAgTkYGIl+YHL9Px0hxGGt1fdmuj9lomuuC3eMAQg2Q4/c2QzxWi12gNS2f5w7zSLI9zEevXYo8VQZtVawV+BZYv2k8FoQaTYB1a4DofLAcP7U1QDf9MBg2/n1Dk3F6FIp6eLEFVZHYRmdQ0MYprejkGU/b61wPsuhrJpgben4mwPMD1k401GyFMU2BOVGLKJ5gGlwDBHTqEWmtDSc1V18AlQwFLzlrG8YysKh8VFF3YRGz/tnXVmYcmaEFZuS4vqn12d4KLTCB9UV3Vd5yXFiuh+fmYwZIksLRTsqZ6vZxoOTqYg5BHHt9PdYp9Fq0+J4z+ZtYCREePIgB6pW9tR7YXo4FqgtNG07CSI7t+TjZIA8gHQUt/815nHNkrRbyRXpQu2OpTWaIa1QAeT6b5jpnR4VR3YtNu2eEAe8CreQMhJzi8sDmmlQAKatOb18LTOQId2RTcZEHUPeNiBiRXi2wOAm+02RPV2TsiUJRF1sO006tpjC2I6+xtM+O/363Do3yqHrpa1gcTMYJOh6W63Esc7ZrjzdCDDgNUNYWWurzakpHcGlRU1DSs0W/9IMWiYuDoH92XTwGv5JBpA1zUc+ml6LHsudLOR/jFlgem4uY6Ryh+kcUQJsB7CR3fPh+aFJ1ZLEN1w9QLajYUQnHL0lHMd+0O/RCAD+inr+lkNXUkBanUkcLzGdGYUkYpJ+8FiADMgBYaNiJDJDDeeNMFAezvB/jMnmqBS1XkUBtusVWaL6WdIzDgO36zIht55jBRn6XW07PNmi3C/QgBVDDcrHcJhNEZc1E76sBC3d0/b5arAWOAUpiB2K/rPRrK06C7ywIVUXGtorOCrK5hg3bW13gJO8DlASegbJTWmC9MqIYA2S7uc/ftu0xGUB2EXQnA8TnWK2GWNQjLyDCtooOSRrOkIaiSFCUMGSin3ankLEFlnXNJ3PIXucja4HluI/wGqBTpgUmMBzQSdmORh0XWw4OL4ZalHN2VOH4AQxVwXQ0yr3QsGF17U5ZC0zPPhHAXp+8Jtzeuza6ORZ0hVH7ATrHY7vB6NSNLoA4n52amWyG6HoBa2mMl/SBQg87CqCc+hZyg25aHtqOl8hSDQPLbZsJO4uawhavxT5miMwFuhTa+ef520jg3bI8xlaGjMdotb+A2MsGAJtWS8M85wKdxHLyGwW6OXdjicsB625fVw2NtUZP1EzY3uqE0PEUWPL1KHFOx3bX8dJEp9GDXaFWZ8PKPwZvcgVnQc3GZDMGyAs6psCyRO/0gqbIbL0FIhNEZXUu0Oy5ZRmqJEUB0P2mwOLzJ80JuqBn/1tLUcGYxCgR6PxK83pKAq+bO2UKoFHr3W9W8DRn2wlzmZ5biPQ/OyuwHB+GqrBJJhqF59sV5OJcVJXcUzU00WXavelLKoBKuooyd3H0ukmMSgGkyhJbnJdS3KCX2zGzNlXOx94QxrkCqJ/NfTemylpHm26tGKBn58LWalFToEcsAxDFYdhBKjVOuqHxQn52jF7DCwKcrHEtsAxxB+sNQ421OP3CHUnQXdaTPV14XyvSt3RjmcsS677pF3WFedIc7xOGnAWxCDr9vKTzvpsBatv99TXjXAss73G2LJf9TlYtI88AIeCF2qvTABmcFxBAmsB8bd80qNHmQZHRNyanyE25dSe403pc1NTsImidnypLvs7pM0jKe0sDm5zs43e13sj9aQVBgOeeew7tdjvTYwVWD6YBcjw0LQ+W47ME+OfvqMByfeiaxLxsTtStMB2aW2BoN1DMkQRPyNoCI/v0kh56YVBR0ytXhhakYed75YWqyKwNU2s7icUFsW5lXUFRz+dzQ+ALoLGcXjm6qjCWaqntrEkchu8HOBRNF+4cM+AhtgggbVRai2W+wwU63+fJC8JJTF02hucBNGwQc0NsVRoWWpQEn9wC45Oy6ynPRWxc2VBWFMyhKV8shAawKpNMmuzqpe+gm3x35iCxM700QJPluADKe5zLbYcVWcWMTDavAfKDgPMdy5ZkngZdkTETMbJAaIKo5Wz7piH0UQqFy/02SKTZ8bkJN0IrZ7sQABvjJ81UEljeW45rPI9ubj0xUAF0zjnn4PDhw30fW6/XxQj8EMBrgBqWi6bT2QKzPQ9TZQMzE/EkGNCZCk8TAeHCke/CjwXN6bQoECcIU4sgnl5LL4D4Hd1GYwe5QbfDSbBuHIlMELdVDChRHEFeTHQwQPlcjskNGgjZKGcNqGTb89moP91YSXs037ThpozCd7tA552GUWSJnTckpi7p+T2r1gt0TTSt3poGFoNRSN54SJKEUnTuN8zkFthyO24JdouoDVVhHmBHojVhGAyQ3qMwp591j8FTQdRrRJ3y8Fq2ywTXWWC7PkzHY+fmaZPF3AyQzwVF6xmc53tBliWcvq3AviYTxGFYNkiSBF0Ni6CsImhg5ZQuMUDlhNZpGoodwyu9NUB5MhnHOgqg0SFGcq8usizjnHPOwfz8/Focj0AC+BZYw3RwZCH0+tle0TFRChOWK4aK0yfDiZBjy20EQVcBZMUC5YEZoIRRSx50wVWii4h2CI0eDtJ0MQ2ipxk2mNg3xWeHdtjbK3puE0TCeMTglHUllzgR6HKDbrtrwgDZno8TUStr55gBCXEhdLIetliSFrDQBZr0UdpA7NgYK4DC16+MMANErStKt04DTXBVjfQcMmoxp4UNU5utrK98DkOVsTeaBDu63IaM3oxrPxC710vfkcYAUVZgL33NVFQAOV7QM26hG6brYbFlM3H+GZOlTK18KsQdL0AAMEftrAnpvXDGVJlxSBPFwTSBaSDzwn4MkKbERVL3+0mfR0FXMo/Bd06V9WmB5UgUIB2h4wVMyD4KGGh5+fjHP47/+B//Ix599NFhH49AAliwaCQCPLzEsz+h/qekKzhre7gQ1kwXttuZCk8ePUU9vwaI9Dy2s3LSgOBymiO6OdDF1GtyiCjpUWCAdnJmiEk+O0cjZm1QE0QAzK9psqRDyZgDRuBDW+ttt6e4fFDYrs8S3aerBSiKhF0s98iCHyQzQKELdHi+TRQH00fRLrGjXTuiOkJiq5p2bzHvEtPvaKnFLrFJjYQQYyBus5WNlTcyQ5UxM17oCEXtngDNAxpt7yXmVZkGqGsMvkNfk/z740WNFQ3LTSezTMJ0YtuPbWUd1WK2XLduBqidYVItK8aLOk6fLEKWwrUjb9u3Fwqa0jcIlUCv2+xiEGn9L+cozDrT5ZMfwxigHCxvmdO6pRX6G4GBnKDf/va3o9Vq4eKLL4au6ygWix0/X1hYGMrBCYQgitxy/GghiA0QSQBd0BRMVwuoFlTUTReLTadjJ9hk+pz8u+qSEU+BpV0U/KJL1X4c4dG/BVYccsL7INg5vtJnh24Enh+wMe/JAU0QAeBFeyfxpst248xtlcw5YDzi0Fanp1nZoAhH4MO/c1uUd7ZjUockhSxPrZUsXuVdoMcK2kDTMLw+CiDWZDQpIOZs26cFRu2r8UJ6u5MYoLbtwg+A7reOkuCrhrqiIAxDUVUWijpbtzAzUewZWtsLxO5pPcTnOpsCSy6AegUbK4qMsqGgYXmoWW6q43o3LCfe+O2ZKmVmTmMGiNLg+0+qZYWuynjvVc9DSQ8ZzzzOyH2fW1FgqNmu74Imo2GtXGepJUZxK1lQ5KbK0opT1ibN8fdqkSbUcn0st0aHARqoALrzzjuHfBgCvVDmGCDbjQXQ5+yswnJ97BjTocgSdFXGjqqBuulirtmZ3s1aYEZ+J2j+9dN2uyS4U2SJXURZbNWZBmgEWmC7OKdlO3KDpk1dhwdQSUdRy6+lAsLd869eshvLbXegyTdqRy23nTVhgMK/M2wzbCvrkT2AgYmihsWWE6XCr3zdDhdoVRro5kvtPUKlMMItMGKAnHRn2yAIWPEyVlJTJ3qIASKPlO6bO4mjy4WVU2D0+xSKeqJmhhOgAxdA/acy6UZvdp0Hbc4gr9caUzE0NCwPddOG58fXWC/U2g6OUQE0WUTWP40YID8Inat5I8TVMkCaImO8qGPnWAHHa+ZAG6I0hGxftsfGUUVdLbDo8ygb2Y+Lj9ZIquuDIB6uMXLmnpUNFZZroxm5gG+k8z9hoALo2muvHfZxCPRA2Yj7snXTZX4rz9tRwXLbxniUpK1H3hRPn2xirmHBceOFkDcpzD0Gn8EdtMkF79FOl2mHbD/xhOcvplFogW2vGJCk0IF1oR1OPBURj5mS/mCiqOW++AmqLEGRZDieP1ABNEMFUCtiqQa80aXhRM1kLcuJaJrL0MIJucWWg6W2ndjS5F2gkyIfsoDOY0JlhBmgarQpaPeIdFhuO4wdGiuka4Bog5HWTiMbiWohuY1W0BVWGB9fDsOQHTcA9BUP7YtYBJ3+vutMA9TJUJgZGCAgZIiP18K/K4ubeRAEqFsujiyG59jpk8XM5wUvxjdtv+sYV88AaYoE2/UhYzgj8IRtnMliP7BhE05ryUsSqsUcDJDGt8BWfjZ82zNPCwwI72MLTaARxWHIG5r+GGLgT+zpp5/GzTffjLe85S2YnZ0FANx333346U9/OrSDEwjBezOQEPe08QLKehggSheArsqMxZitm3A4TxA2EWCsnCTpBypk0nYFQHzx8YsfyxBzkxd2fmxz0IJimDA0hflVLHa5QVuOx6acxkvawIJHWZYgy2GRNUhuEE1khW06P3McxnzDYn4yvfBspLOYKGqQo3aFrsSut0stJ5HRowJooji4HwqNSBPGitrIMkBZnG2plairMkp6MnsDxBscM8EjxfMDdu2OGVri5sVQFcxE7dsjS214QQDLG4wdZGPwPa5H+mzTwlD7CYzpvVtuO6mj/53P66NtuzgUrX2nT5YyF9j8JsN0PbbmDMN5XlfCsXfT8aAq+cJBhwmSGvAt8RbHDvNeU/2fK9Z3DXvNjtvG+U0w1woDfWJ/93d/hwsvvBD/8A//gK985StoNBoAgJ/85Cf40Ic+NNQDFOAYGM/HsUiIe87OTgE0EF7UNAp/bNlEgNgVlhiaPBdD/Pr9W2DNhKgNZqvuJBdOHRfTCGiANCWOE1nu8tmZrVtw/QASQpZjNbs9XZUhy+lxA72wM/IeqbUd2G7vdHYehxfb7AaSBtfz2Sj1zrEC/MCPxJgym5ALvYBWhqLS5NZ4SYM24DjwRNdOtZKgeRkVkNN5y0l3R+/IAZPk1L+Frsl2QjHFGy2OFZOneQxVxumTcSo8MPgovOtT0GUPBkiN3YJ5xBNWvX1nSOtlObHerBdMx8PRRTP0O4uK8aznlyzH7sykoQyPcQiZXUrY6jUdH4osb5hreVILjCbAFFli63cWdEZrrPw5z/rlfQ87mM4RsQIa6Cz44Ac/iNtuuw3f/va3oesxz/rKV74SDz744NAOTiAEneCO15kAzwuggXBHQp4gx5fDUXk70g3Rbm2skJ0OJXQyQMmLfYNrgdHur9fOFogXUAmAscFhqACgyjLzKVlqOx0+O0ejwmCyrEORB5sAI1BMxCDtnemqDkWSEABYbCYbNnbD9wO0LA/zTWuFToCH7fmsvUoj8CHNL2Oa9wLyVhZezAW6qA18c5ksx7R/uHDnt2xYL4wxBsjFsSUzcfS8Iwm+h+C9YsTmgN0bBRqjD1mk5GvXUDtDUc0oEHkQMBF0Lw1QyhQYc1nuE1RKxaPjB1hs9teyma6Hg0th8b53qgRZynft0OaqswBa/YZLksJztO24oQniBtGVrADi3sek9TjPc6Wt9XGWmpRbS0pt40GCcNcKA31ijzzyCF7/+tev+P709LTwB1oDsIRex8eTJ0K27ZydVZiuh2oxvklIkoQzt5UB0I0qQMt2OxbnajE/A1TkCiAv5YZLVHZBi8V7JcYApRRAThy8qIxAr0OVJeZTUutigA5FBdD0KkwQCboiR26v+W/uuqqwBPGldnJqfTcs14fteWiYLhZ7tMFs18dsVADtqBYgQY5ofomZRM6RF1CXid1cVABVC+rA7UF674FwdDcMrBzNAohYDM8PcHzZxCOHl1ewGawA4lyuk1Ax4hZGt55okQtCTbtnq0pYuNMxLTaTdVr94PsBY6B6aoCi4qi7+KYJq0KfAphYVsvx0LI9NimXhpblsQ3Ivu0l+EGQ00E9PJ62E7fAhjWyXjFUeD5g6PknOocFdn+w48kt2uj002OlPVdoG7Dy58QsqnL+azPr5OR6YqBVfGJiAseOHVvx/Yceegi7d+9e9UEJdIJpaRwPCy0bsgSctb0Mx/NXCEdnxgvs8UstB03bZbsBTZEG2p1TIdMr2JRpgDSOAeIMFJNbYLwt/cZDliWWSbXc5bMTu0CvPvNHV5XcHkCEMLMsatO1sjFApuPBdgMUNAWzNTN1vNV2fZyMbuLbKwbTNUiSxBiG0Aso6Nj9u57PTBAHcYEmTFW4AshQRzIJnkAsRtv2MDNeQNvx8MjhJRycb7IiZoELQu2llyBNTDuhnUbvaynKZev1HNuj92++FRZAeaOI+GK6l0Ym1gAlj8H3G2ggv6em5UGVJcw3e7fB6qaLo9H1t29bGZKEXOxDgRVALtt0FYc0dWpoChQ5DozeCMRay5hBjDWZ+VhUKgxdP1gRdgtwSfCKlNtJu8qxpmmDA+uNgVaYt771rfjABz6A48ePQ5Ik+L6P73//+7jhhhvwG7/xG8M+xlMepa6La+9UiVGb3YtNQVeZYHW+acG0faYjID1HXvC7pTQTqzqZ13FTZuRvYjnJFT+J9kZppz9djRkgOr4gCHAssuCPU58HvzkrshSNiuf/mzWls02XpQCyXB8BAowXNSy2bPZZdYOfdNtWCUfg6aZ7RlQANW0PLdvtuPnxLtATA7pAA8D2clwAlXRlKELVtQKxcC3bgyRJ2F4xUFBVPHashidO1GC5XhyEaqg9NTXVHkGRS604BqNXPleJv+4bFhuFzwNe1NzrMyQfoG4BPhUX/RjACWZ46aBsqFho2KkbK9fz0bRd5gG0b3sZAZDr5ss0S47HRveHVQBpihS1JzdOw1jqiCrqZIAKWr4WGH8/SXLDjwug/E7alQy6ufXGQCvMRz7yEezduxe7d+9Go9HAeeedhyuvvBJXXHEFbr755mEf4ymPgt75MZEAWldWXni6InPtChuO5zMavaAOlq1EHkNAeqwFE0FzHiBlbnotaTfK7yZGRerB++zQjtb1A+aOPFEKTRBXI3hUIgZp0M9ikmvTJXnydKPN6HAFtuuzG3PS48jraCpyu6a/c6KkM0ZvoWl3FEDdLtCDjq5PlPgCSN0wUWkWTHCp5oRKQcW2ioHn5lt49MgyY9MqhtLTqoBaA6bjrRCHLrUjBsjovZM3NJmZZJ5shJ9P3swlhxNO672MEFkLrEsDxEIy+7TAoveubroo6QqalpsaKmu6Pmptm4ns90Ut/jysRoFpZHyuBTac4tpQlIE3lsMCL5GgAoiMaQ0tn++bocZsfFIbldqcg7D2pJtr2+nWEeuNgXg7TdPwxS9+ER/+8Ifx0EMPwfd9XHrppTjnnHOGfXwCCIsaRZJY1ZwkgGaP5dKhZ6NU+JoZLqKGJg88VlzUwptnmoiWFUB6rAGq9HGQ7riYRoQB2jUeisiZG3Qk+CU9x+QqTBAJYUE5mAYIiENb62ZyaGs36pbLFuiSruL4sonTJ1e66R5eDK0TZClkOEpccK6hypiq6GgutLHUcjoWxw4X6OJgLtD0GmVdQdP2UNSVnjfhjQZ5q7SjVlP8PinYNVbEyYbJnMMrRm9WjBggK2FnvMxpgHoWQIqCHREDRJlttusD2e1kWFErSf0YoDQRdLaMqImI6WtYLmQp1P8tNGzsqBZWPNZ0PGbNMFXWUS2oaNlurjYrFWym47ENw7B8x3RVRlFXVjUUsVqwpADXY5NbDdJk5jR8lKKuQtvx0LRXFqWdm9acImiubTwqDNCqGpdnn302S3sflRvYVkR4Usqsqj9nRyiA3jm2chzUUGXsGg9XveM1E14QMC1CMedugEdBk7Hc7jTb4kHMUJHXAPVxkO6kUwc6rKGD5YG1HTiuB9cPBb/zTTJBVFcwcnkRMkDKQBogIPYCWm47HVYCSfD9AE3TYzetqqFivmljue1gims5BUGAgwsU9mogCDotEzRFxraygUOsAIoL4WPMBFFno8GDQI4S4Zu2h5Ke3Ql3I0CLuReZefI3VEWWsGusyAwMKykOzgRqp7UTRNBLXBBqPwZoV2SBQZ5MeUfhqaBRZann7j5VBB0VF6U+xcVEV+ZbSVcw17Rhu/6Ktmfb9nBoIdb/2G5oIJpHxEyfjel4Q9cA6aqMoqpsqI1HLDWIw6rjKbD8k5QFTUY7Eqh3g1+zpZzXJ4n02ymSiI3AwEvM5z73OVxwwQUoFAooFAq44IIL8Gd/9mfDPDYBDhQVoSkS9m0rwfWCFQJoINydnT4Rp8IjiHeRBW0wfxaAH7VMZoDinjPvA8RRswknPC2Y+ggxQLQL9fwAy6YD2/PRsNw40yligFaDsYIWhigO+FnwbTrH9XsuJjQBRjcWVZHDorhLeGp7PtM57RwrIECnEFZTZUxHItuFpt3xmR5fphgMbdV+KGTTUB5hF2gAzIQUSA/7pWuiovf+WyrGSg0HgfR7/RggXZGxO2IvZ2lSL6cGiAoaVe6t76ARed4mIgiCWAPUx2uMWrgNLp+wldIGa1ouOy/P3F6C5fowNCXXMAc9tmV7TLeU18U4DYosYf9MdUWO3XqCpAYmt9HkA4Xzrq1UMCYZnrIpMKV3kZwEXuy/qX2AbrnlFlx33XW45ppr8OUvfxlf/vKXcc011+D6668XGqA1At10z9pegSKHCvwkGleWJZyxLSyATtbDiZ2lITBAVMy07eQzt8GJoOklyh3ivJW/w0TQav6JgrVCSVfYDWkhcoM+EnmQ6JEz8mr7/boaOysPAkqUpzZdrxud6XhoWh4OLbaYDquiqzhRszoYAtv1GXNALBhfAFHMCkBmiLHIdrYe54CpsrQqPxS6kZRHnAEiDxhgZQYTgeWAFXuzN5WOrL/Omw4V3uU+ppCyLOH0qSJUWYLnB6iZTk/PpyTQeUTrSxqITeQtMUKhfYh+7ApNWpJTPb033dq0MEvNYROY+7aVYbkeqjnd7GmdbJjuiu8NAxtt4lriopK6p8AG2fRSfloyAxTLFvLeS0gDZDo+M9zcaAzUAvv0pz+Nz372s3jLW97Cvve6170OF110Ed7znvfgtttuG9oBCoSgC/acHZVUATTh9MkidEWG7fmR3TztBtSBW02MAUqZIKKLpajHC3U/A8XVXExrBVWRMVHS0LDcaMrKZyO401UDkjTczJ9BsItrgdme1zMOw3J9fP2RY/j6I8fwnlc9D//6vF0oGypm6yaW2rHuwvHitPvpagGKJMHgNDiaIjHmaa5pscKroCnMPHG8qEUu14N/lleduwOHFls4d9fYyLpAE8qGGk3FrbxR+EHAvLHGi+k5YEAcrBoEsXiVQEVUlgib8YKGbRUdJ2oWFpvOinTwfmAtMEVCr/09PwZP+id+YqgfQzpW0Nj6dHChheftqKCkqzhZt7Bve5k9v+X6K1pgru+zMfqsIAaIn34chfDlYYFG8PkEd96ZP+/lyETjPVpgIWuf73lpyMF0vNzs5FphoJXc8zy86EUvWvH9yy67DK47OlH3WwlEH56zs5oqgCYUdZXt1ucbNtsNFLXBnXU7TA0TbrhUGJU4DRBrgaW0afiLaVQ0QKossVTypVboBn04MmHbXgldmDd6OmnHWAESojZd010xjcOjbbt44kQdAPC3j4WZfeEOX2LmhUC4eNII/PayDlUJR/UJkiRhdxS3MFe34HkBmzI6WY8y0ooa2z0Oil++8DT8wTXnY89UaaRbYEDM3CS5QDctl+3Gx4rJIaaEIneTqnWZAlIRVTX6a8YMTeEiS0ILjDxaC4e1wCT06m8QMxgWQOH3aAJMltD3+pBlCS88YwIA8IOn5wCE72XT9thmDQhb58eXTbSd0C9o92QxzNDLeY5RQUafkypLA+vvRhH8OhszQIONwYe/k94CiwdXBhdBh75kgzmVDxsDrTBvf/vb8elPf3rF9++66y687W1vW/VBCazEb195Fl71gh14+fO2r3CA7oahylwBZDE1f0EffHfOAvdcP1HBTzbsRT3eqVILLK1NQ7tGXZV77jjXEyo3Zr7MGKCQ4dhWMVZtgjgMFDSFFcSLbbvnuHPNdJiG4rFjNTbmXjVUzDVstsiFBVD4s21lo8MDiLA38gJaaDmhz0xUwPIu0KttLchyyJ7IMka6BQbEIv9HjiyvsHkgW4CipsDQejNANHkDAHWziwFqZxNSA+F1T6Pwc42wTZlnp521BUbnv+sHjNml80hT5Eyu7i87ezsA4AdPzyMIAiiyBN8POtpgpuvh4EIY/UNGnHpOATT9DhC3hdQBbt6jjFJHAdQ5Bl/U1Nx/K72/SROmTAQ9gEcXrVl+ADStTcYAve9972P/JEnCn/3Zn+GCCy7Au9/9brz73e/GBRdcgM9+9rOQc6xaDzzwAK655hqcdtppkCQJX/va1zL/7ve//32oqopLLrlkxc/uvPNO7N+/H8ViEXv27MH1118P0zQzP/co4sVnbsNb/tVeaIqUKoAm6KqMndFCONuwGJVZ4kwK84IusrRYCwrfI1t/oFMLkESnmtxuYlTWI1WR2XRUve2ibXusxTNV0lZtgjgMaLLcGdqa0k/3/QDHlqyOyb0Hnwmjakq6gpbtMp+ZmukwM8PJStjK6p7m2jVWYLv7xVboNeN6PpsyHCuqq9ZDEOuTN+9pI/CSs6YAAF/+8WH8wf/+GSsuATBBb7UQtoT7FS9UTDW6jEbp62pB63sjMzSFtUdPNkJLg35TgjzIB6jvFBhnhEgrgcm7ume4ll/2vG1QZQlHltps+rBsqDjZsBhrFSbAh9fevm0lWI4PvQfznQa6oZNOcZD2zSgjKb+L9F+DsP7x1NxK/7ZO1j7f85Z0julM8X1ab2ReYR566CH275FHHsFll12G6elpPP3003j66acxPT2NF77whfjpT3+a+cWbzSYuvvhifPKTn8x10MvLy/iN3/gNXHXVVSt+9sUvfhEf/OAH8aEPfQiPPfYYPve5z+Ev//IvcdNNN+V6jVGDLIVFgh8gVQBN0FUZu8ZDvcbxZTMugPT8uwFCmWuBJVk40G6hbMT9eYPzoOhe2IF40dRzBvatNYg9q5mhjoJMECfLqzdBHAZUhWOpesRhWK6PZ+cbHd/7/lNhy0GSJOiKEnr4BAEOLTbhB2ExWtIVZnrIw9AUVhwuRCGWrh+L7Cf+/+29eZhcZZn//T3P2Wrr6jWddCfdWYAYIISocQkEWUUjZl7HGZVESZRxdHxBI3FmkgiYxBHCuMxvHP2BA2oyXILMNahcODISlgASUAzQEhnekA0SY0L2Xqq6tnOe949znlOnqqu7Ty3ddarq/lxXXdDVVZXTp85yP/f9vb93UCvbD0Vm1kwl679lfdSE89kls/GJRTOgyQwvHTyNL/70ZWf/igt8RFegKdK4nThigTHkKqeZJneC1yZ97CwSICwwrDLlWwNJGK4snRdSTgZonC4wuUAJzJXN9XIut4Q0nNcVBWBlgQBL+D6UyDhlwIFEtgNMCKAjWvEjUkRZNjsSyD8Z50rgntUovo/sNb94DZAIqAo1rzi6zSL9hQDROGDdRwYSmaJHtUwEnkXQ27dvr/g/vnTpUixdurTo933+85/HihUrIMvyiKzR888/j4svvhgrVqwAAMyaNQvLly/HCy+8MOrnJZNJJJOu1dvAQNHbNNFIzHok0saYAmggbyr8QMIRAQbL6KwJuVva8w5cznm25deVAZIkCUHVMrYbShTKAInVhOyrFVmOF5DBnVlFLUG1bBPESqDKDK12IDKQGN0NOpE2cNARkIbwxsk4/vfIAE7HU2gNWaZyp2MpDAxn8Cf7dZ1NAZg8m5HI/3fbwzreGkii3543FXe5QFti3/I9kmTJKlH4XQStyAxXzOvE++Z24l8e2419x2O449f/Hy5/2xTMmRIBYJWuvAzOFQuMeNLS2DEmYTCZcTIs0aA6bvlalRmm215ARwcS4NLIeV1j4dYAjdkF5vIBEjkgUQLTPGZzVZnhHb2teOVwP3bsPYHl7+6FIjNkTBP9wylEgypiCcPR383qCCNtmo5nUjEE8jRAfvIdqwTZEph7FEZ25Eex16tgXklNdgWLTuduCcNQxbYOJTOI2xq5apu9+3yNNZItW7Zg37592LBhQ8HfL1myBC+++KIT8Ozfvx+PPPIIrrnmmlE/c/PmzWhubnYePT09E7Lt5SBLEhism5quyGPWwRWZoccWrB7tTzgXp5A2divtWDglsMxIEyu3+E50tAhEt0UhV1FRAvNbBmha1Np3QgMkXKBbQlrZJoiVQohdBxKZgmJFwPpexAr6nTNbcXZnBCYHfmuXwQKqjIRh4NhgAkf6RQu8dQMttMpWZckZuHk6bo0Kect2gZaZhOag5ulmPxbuDFCpgv3JQpaszE5PaxDf+usL8fFFPWASsH33cfz42QMArMDGi2eNCDjjqYyjseu3M2uqLDmtzuMhLDAGExkkUqYjWvVCusgusIw5UgTttaOTMQkLeqKQmYQ3T8Xxp9NWGSykKTg2mLQC62TKKT/Pbg+D89IMDJ0AyF6kqUp9aYDEvcDkWZGyWJCGPR43hT6vkC+V+Hy9hAwQkG0cGEplfGGGWFIbfCKRwPe+9z1s374dx44dg5mnQXjppZcqsnH57NmzB+vWrcNvfvMbKErhTb/22mtx/PhxLFmyBJxzZDIZfOELX8C6detG/dz169djzZo1zs8DAwO+C4KYfbEdThnoaNLGXQ32tFujDjImxzG7hBPRSvcBEhfgRHrkZHd32r4pLwAKqTJOAogXEL05Imifrcg67fLh6XgK/Yms23LUzgD5AadMN4Yb9HAqgz/bgU1PawgRXcXeY0N4bt9JLJ3fBQAIKgpOxbOzlgp5AAk0JdcLKGOYjkdLiz0Co9wSmMIkyJLIQvjooCgAY9aN1CodMlz33plYNLMV/+fx152AMqLL0D0cM2HHU8haYKiytY8B2GNBvAWWUyIBNOkKBpMZnIkX1wrvBEBsbI2MEwAZ3AmAkkUuZhQmIaKruHBGM146eAbP7TuJjy8KIaJbx+OJoRTePBkH51Z3YVNAQSpmlnT+if0fd7JUdaYBKqC1dDSZWvEGjW4D2/wqVcIlgi7l/HQH+oW0pJNNSQHQ9ddfj8ceewx//dd/jXe/+92TcqEyDAMrVqzApk2bMHfu3FFf99RTT+G2227DnXfeife85z3Yu3cvVq9eja6uLtx6660F36PrOnS9dGO6yUCSrEd6HAG0IBpQ0WF7ggifmJAul9wFNtZgU3GyaQobMb8pqOWuvty4NUB+utlNs2/yaYPjoD2HKBpQoCms6gJowTSXG3QqYzplEzeDyYzj0tzTFsK5XQr+4/k38MqfzmBgOI2ofWM5FUvhuG1mOCWi5wy/daPJLNcLyOA4bK/cW0MqFJlVTAPkl/08FkKn5C4TnNsVxXc/8Xb8aMcBPP36MbxtWtRTJkvcGIZdGjsxxDikete96KqMjiYdg8kMTsWSGC4w0Xs0nBLYON+hprhF0NzZbsD7YoZJEjjnuOisDrx08Ax27DuBjy/qgSozZAyOoUQGh+xja3ZHGCnDhKpIJXUZBuxjWexXP80erARin5vcFQDZ30ckUPz+ys4WGyl3cLv3l4KoEMST/hiHUVIA9Ktf/QqPPPIILr744kpvz6gMDg5i586dePnll3HjjTcCAEzTuhkrioJt27bhiiuuwK233orrrrsOn/3sZwEAF1xwAWKxGD73uc/h5ptvLqpLzU8wWxchhu+Nh1iti5U9MDI7UwxjuTpn586MdB0dy0Ax21Lpr4tRJKDaXVIG9hyzRMQi8+GXG7OYB+a4QZsmdJY9LkyT463+pOMkPKM1iJCmYE5HGPtPxPC7Ayfx/vOmQZUZDG7iuPAAiuhQZangBU6SJHQ3Cy+gFDImd46v5pAGRWJluUADtgaIsYIBmN8QWiWRsREENRk3Xn42/t/LzsLRgYQnXZRzY3ANinQmwReRAbJcxjUcOBHDyVgKyYzVCu/luM1mgDy2wY8igvYSXAgvqvfObsOdTwH7j8dwtD+Bac0BWzeYwZ9dHWCpjImAUtwIDEF+Bk4rsXzjV4SNgtDkZVzi93xJgheCOZqi3N9lrUtK24ERfeRxXk1KuspMnz4dTU1Nld6WMYlGo9i1axf6+vqcx9/93d/hbW97G/r6+vCe97wHABCPx0cEObIsg3PuC9V5qTDJCoICHn0wNJmNmK4c0UufVxNy2fXnR+7OHDBlZGpZqP6HC2SOhAZI90lQIVBkyRnJsP+4FQB1RHTIUuHAoBqITMzpWAoZwygwmDLbAdYR0Z3v4aKz2gEAO+zOGwDoCAeccktbWIPGRr/h9rTZAdBQEmkz6x3UHFQtbUWZdxbFFkFXu9POC4zBLoEVvq6IUpCXDFBEy2aAxPl12jUJ3uuAWU1hTnB8YiiJTMZ7J1hWAzR2GUtzNEBZH6BcPd/4/5bI9EUCKuZPbwaQa4p4KpZ0HNjFENRoUC0pcxPIC5pKaeH2O1mzR8PJ/gBAUwnX/LEc/J02+BIXKE2uAKiQoe5kU9Jf8Z3vfAdr167Fm2++WdY/PjQ05AQzAHDgwAH09fXh4MGDACxtzsqVK60NZQzz58/PeXR2djqDWMPhMABg2bJluOuuu/DAAw/gwIEDeOyxx3DrrbfiL/7iLyDL/tBvlAKTJDBI4wqgBZrCHD2HoJwMkNtsKz+QGWvysGOgmC7UUmk67/MTCpMcn5037RJYW9iac1VuiadSiO82Y3IMJAoFQNkRAr120AIAF51tGdD94dAZZzaSybmTKWoLa9C10QOZ3rYQJMn6dweH045APBoo3wUasNvzFWlEKdWPMMnaT6NdxznnkABPrsOiVJFw3RjEEGMvYzAE7jLl8cEk0kUMRU159AESJnhuHyAhxNeLyADJkuVmLkwRd9gBkKYwRDQFh1wdYBnOnexBsRTKANVZ/JMzvkJIEmQmlSQaD2nZxS7PO3TEMVJqple4QceSNSyCXrRoERKJBObMmYNQKARVzY0yT5065elzdu7cicsvv9z5WQiRV61aha1bt+LIkSNOMOSVW265BZIk4ZZbbsHhw4cxZcoULFu2DLfddltRn+M3GJMgMXgeBKgp2VZ4wLowlZI+FoTGSIuKG2kh23XxvmHbQNHdUukIJytw46wkCmOOz45YhbeL8RA+yQCFdRVhXUYsaeB0zMrGuEmkswLlnlarhCBJ1v/3toVw8FQcL7xxElfMm+qI5MOaDE1hTrmzEEFNQUtQxel4GqdiKSdzFK2AC7QgoPh7EKpAliTIkEZdyRq2Lkv2EDSLG8Owy2jUPQjV6+gGTWbojma9gEzwIjJA7jZ4DxkgVwlsuMgMkGJngAyTY/Gcdvzg6X14/a0hHBtMOFYMQ8kMmATMaAniZDxV0s0cGJkB8tPswUrhaC2TmRxJQil/Z3DMDFD2ey4F90R4P5TASgqAli9fjsOHD+P222/H1KlTSxaUXXbZZWOWpbZu3Trm+zdu3IiNGzfmPKcoCjZs2DBqm3wto8nM8yBATWaY7gqAAmp5U8zHOimcE67AtHm3g3S+VkJ0FPils0qgypJj+CdoCWmQfWCCKFBlCS1BDbHkMPoTmREZoEQ6gyMDWQH08aEkJAnobg7i4rPacfBUHDv2WgFQdgp8AAY3nRXgaP9uW1jD6XgaZ4YzOGkHQM2h8l2gBVOiekWndU8UzBZBZzKjBECcQ5G8zZ2K6CNvDCIAChUxz4kxCb12K/xbAwmYRbhBu0tgY2aAXCUwFAyAxt9WXWGI6AoGhzNoj+g4rzuKV/88gOf3ncT/s3A6Dpy0RmBMbw0BklUmL/WYyM8A6XWZAbK+k+G04UgS9BIHEwfHFEHb1+wSg1EnAEoZ8MNA+JICoOeeew7PP/88LrzwwkpvDzEGbWEN0YC3AEiSJMxst8oVnFsniJcZPaPhTovmpy4Hx5g87DhIZ0a2VKacAMhfy31JyvrdCPxigiiwslQqDp8ZRv9wakSZYyDh6gBrDUKSLB2ZYVqdNz/9/SG8fOg04qlMTgAkYewsl2bPm9p3PIbT8ZQzPqM5qFUsO5avXfMzqiIhlhwnA+SpBCac1k3nxiDcpMP6+HPA3PS2hSBLlgXGQDLj6HPGw6sI2m2EmD8LTFe8mZpKkoTOpgCOD/UDAC46qwOv/nkAz9kB0Bsn7A4wWwBdygwwQX5p1m++Y5UgpGbby4V7uF7CJHggW05LGSMtT5JlLlqFkaVfMkAlXbHmzZuH4eHhSm8LMQ6zOsJoDnkXtbWGNLTZpZxyywqOMM4wR6T8ncnDBTRAWQfpAqZameyUer+RfxNuCakI+sQEEbAyMaJMNzCcyXGDNk2O44NJpzzV1RK0R1woiKcymNkewvSWINIGx+/fOO0EQJ1No3sAZf9dhk7bhPHEYNIpfzYHFd/ooyaT7uYQGAOODSZGZLNN09JheOkCi7omZYvzZGC4tAAorCtoF4aVsWTBOXyFECWw8drERRbUcM8Cy2mD97atzSEVmsyQzBiOOF8M7H3DzgDN6ggjmTGL3gdu8jOT2jgZrlok6PKRcjellGJ8mztao7LX7Ki71OsDDVBJV/Q77rgDX/nKV/DUU0/h5MmTGBgYyHkQ/kBXZad9O6AVPxTPjQhkOM/69whECSyojbxwiu6xQlPkxWoiUGI6dSIRs9QA6yYW1hUE1dJF5JVGkiS0N2UDIPcqP5kx8aZ9A2kNqc5k99aQilgqA0mSst1ge084rexTmnQocmEPIIEmM0yxg8MDJ2I5LtDltsDXItOaA1gwowVhXcGf+4eRcWXiDHvKuZfzrsnJAGW7wAbs4LLJwyR4N7rdCg8AJ2NpxFOGpw5YtxP0WGS7wLI3SNEGrxdRrmvSFUQDKoYSGXREdLxtahM4LKfyAyes43d2u+UB1FzCCAxBI2SA3GWrmEuSUErGOne4au7vUmWWwISEY7Sh2pNNSVesD37wg3j++edx5ZVXorOzE62trWhtbUVLSwtaW1srvY1EiVit8FYAFCziwlQIty5kKM/TR6RcAwUmD4ddGaD8jgLhKeGnwEIgOmkASwDNfNQBJuiMZIe2JlyGd+4OsJ62ENIZEwHbII9zq+vrYrsb7MWDp51p3O0Rq4w1ls6JMQndLda/K1bpLUFrJe+3/TNZtIU1LJjRjO6WIN4azI6eMUzuufFABEBu3cWga6CqVxE0IDzArOP35FASacP0pANyl8DGQpQ6TQ7HZHXYsbTwXnaRJAlTowEkDOu9F59tBeVPv37cGY0xqyMMCRzBMXRp45GfASpVG+Nn3FpLETgHCviyeSGYUwLLDVJSTuduaYudZifTaRY1p26iKOmomojBqETl0RSGGa2WILI5qJUVAMnM8sBJGaZT9hCIFUewQM054jrgRzuZAj5rgweyTsuAbYLIuW86wAQiSDsTTyOd4Y4btNUBZt1AeltDSBkmpgZ0NAezBo9zOsKYGrWMMkW3WFtIc7JFY9HTZh1TYnXYGlIhV2AMRi0T0hSc1xVFSJNx4EQcSXtmntduGeEEnUgbSNvnhTjPIgGlqHNXlbMWGMeHksiY1s1mPBGx0+I8zvfv/ptEScTx9FKLc1l2l8EWn9WBH+94A68dHQRgBX7NAQWnhnlZ14j8DJBaRjesX8najZjOArWQJtMLYt5hOmPCyFMqZwOg8jNAaaP6GaCSAqBLL7200ttBTAC6wnDZvA6ENQXndEbKHi4ZUK0AKJ431kKMuSjUBh/OaZ/PHvCcc6TsFUAl/GMqTTSoQlcYkhkTHWHdcif2WQDU6RqHkeEmMiaHxiSrA6zfKmv1tIWQMTlCmtWl1RbRcPh0AhFdwcVndeDnLx92Pq8l5E3oPbMtnPNzS4VcoGsdRWY4a0oEYV3BnreGMJhMo1cJjv9GZLvATA7E0xlwzp0bWZOuFnXuagrDNNux+9iANbLESyt8qsgMEJDtgHMHQMXgLoNNiwZw9pQI9trmo7PaQ0jbWbRyhhDn36zz2+LrAbdNicgcBtTSRh+JDBAHRmQOHe+2Eq/Z7kynV3H+RFJSAPTMM8+M+fv3ve99JW0MUVk0maFJ03Dp26YgkTZKngQvCGoyBhLZLgOB+DmkjVyp5jhIuwIg94lV6sk0kai2G/SxwSTaIv4yQRRMczJAKRgGR8Y0oYGNmAEmgTur//awjkOnhsHtMpgIgFpCKpjEPE2Pbg6pCGsyYnapJxpUoanlu0DXA5Ikoas5iKAqY8+xIc8rZbf30sBwGkPJjJNhawoWpwFSZYYZrcILKAGObHAzFiLzNJ7rtLtEKj5X3MyKbVUXZbATMbsb7Oz2bADUEUYybSKky2VZLFhCdMkp19XCmJViERKFRNp0SRJKkz24v8Nhl4DeMLmzD0stgblHc4hArZqUFABddtllI55zrxoNo/qRHWHpNYKajJNDSUgMkMo878WJkT/YVGSEgmNqgHLb4JMuzYofPV8UWzR8bDDpOxNEgRBqJzJW2jud4TAVjpNDKWdExbSoDi5lg8xmu51/OG3gnM4IOiI6TgwlMS0agMG5p+9CkxnaIhpits6oOaD68jusJi0hDQt7Wjx3GzEmIaAyu4Rh4IxtwGlNTS9+386yvYDODKeRTBtIj+JV5MZrBkiSsgFFNgCy/hsq4Thwl8EuPqsD9z5vTRiY3RFG0jDRFSzfFkFTGDKp0oK0WiDbuWXklMBKWfSqMnO+X3cHoTuLWLInkyJDlSWkDY6B4ZHzISebkq7op0+fznkcO3YMv/71r/Gud70L27Ztq/Q2EmUQ1mUkMyYYUH4GyGW37kb8HNRG1pydwXpGbglMaAckCb4xF3SjMAlXnz8NF0xvxsKeFl+ZIArc4ydOxVNImyZShomDp6zurKaA4gzSFBqKgCqjLaxhKGF1g11sd4N1NQcgSd6mPKsKQ0c4O2alKaD40sqg2qgy8zzDC8hmgQYSGZy2LQyCmgy1hLEgnU26UxY5HUuPWLQUItsGP/5xLhYD4qZYjkGeuwzW3RLEeV1RKEzC+V3NljFniSMw3LjLXvV4rIqFZsLVBRZQSzd8zI7WyB43bquNcoJIoXcbSmSq3gpf0pHV3Nw84rn3v//90HUdN910E1588cWyN4yoDAFVRsbkYJK3dtyxcMZapKz2V5H1EydJWBupH3EMFNO5BorOJHhWnkHjRKHKDIvntOOiOR1IZgxfmSAKFNsN+mg6YemADI5E2sDBk2IGWAgpgyNoj7gQdER0HD4TB+cc176rF4xJuOrcqdawVw+pbU1m6GjKGkU2B1XfZcdqkZAm42TMaio4FXdNgi+h3KApMjoiOg6eiuN0POXcFMciZXgrgVmfzzCcNpz3iC6wUsTK+WWwWz98HoYSGXRGdRwbTFQkYNEUGYCVVSu1hdvPuK+z4rsOqaV7JwVUhqEknDI3kA12mTS+UH4swpqCM/E0YrYZYjVdmSp61ZoyZQp2795dyY8kykRXrPZkNo67qxecToO8eWAiAxQOjLywiJVJvoGi0AyocvnbNRHITIKqMGRMS1zsJxNEgSJnh7b2x9NOu7PoAOtptVrg84dIRoMKAopVBosEFFx/8WxMjepQmLcASJVZjlFkS4gCoEogMh3xZMY1CV4pSXxvtcJbWbqTsVRBB/d8hIeRl+9SZIlEKVtkB0IlnifuMlhEVzCtOYBUxoQqs4o0Sbh1hqWU6fyOe4J73FXqK7XzV2R4hnMyQKJEWt4oEXE98sNA1JIyQK+88krOz5xzHDlyBHfccQeNx/AZmsIcd9ZKlcCEiZUYbCqEchF9pEt1cBQDxaRLcDn27OnqoSsMsUQGBue+9CpSXUNb+4etAMjkPKcDLG2ODIBCmoKWkIaTQyln5Zg2OBSFebr5yUxCl8soMhpUfScQr0WE1ieeNp0SWEiTPTlJ56MpWQ+wE0NJZAwTqYw5ZvZD6IS8BsEAkLL1no6nV4l+Pe4ymB7JmvrpilwRmwy3aDd/Nlg9EHR1gcVT1t8aKiBJ8Eq2BJbrLwbYi9Yyrtmi0SKeylTdDLGko3XhwoWQJGmEu+h73/te/PjHP67IhhGVQRMZIFSiBJb1KhEHrmFmhy3m32jd7wGAoYQ7AHJb55e1WRNGQGFO14Mfb/CMSc7IA8sM0fpeRAdYb1sIkiQV7LKb0qTjqD0CA7BM8Cy9ibebrfCXslygKQNUCYQGKJ7MQBxuYU32NE0+H01mTpfg8cEk0nYr/FgBkChn6Z4yQEIDxJExTOc8GWuQ7ljkl8GszzbRGdUq0l3oDqJCdVgCc7s3x9N2CayAJMEr2eGq2QyQELqrZV6zm2xvuJir07FalHS0HjhwIOdnxhimTJmCQKB2hhg2CppsreoZQ9kalpCro0v4Y7nFlU0FAiDZLqukMiaGktm2x+zJJPnWll5XZat134cmiIIprnEYw2kDsaSBY4NWYNPdHLA6wAqsoKNBFbrMkEgblk7M4EUNpT2/O4o5U8KY0xG2jzF/foe1hBCHxtMGMvYCI6wrJWVuNZmhqyXbCm9wjqRhABh9lqBw5lUVLyJoUQIzkHB1B5Wj13GXwXRFRto0EA1o47/RA5rr2PZjNrdcHA2QYQJW8rCgJMErQTWr9xRks/ZSWfeSbAbIqM0S2MyZM/HEE0/giSeewLFjx2DmuUVSFsg/KDKzrd/L/yxx4CZcpoZx23OCSaOLC4OqbK1MkiMzQKpSXj15IlFkBpNzqD40QRRMc5khpjIch07HYHIrWA3rMlJG4XEMYU1Gc0hFfzxtC+XNHC+a8QjrCtZ+cB5agipShllSmYbIRWRQE6kMhlPWSRHSlJLOXcYk9NpZurcGkjC5Oa7zrgiAvHjuiDJZysg1tCvH02tkGUwqywDRjTsD5EffsXIRGsVkJqu1jGjeB2fnE3DJHQTZElhlMkDxVPUHopZ0JGzatAlXX301nnjiCZw4cWJEWzzhL8K6UpEblNtsSwRAbs+J0dp1RTp1yC2oszNA2jiTp6uJyiRwbrXEe1kVV4OpUWuV3z+chmGaTgdYT2sIaTv4KRQASZKEKU26o8vi4EVpIzSFAZxb2iEfWgTUItkMkImBhBBBl6YBAoCZ7UEwyQpSBoczSI7jvOueBj8e4jXJtOFoAFVZKqrtPx9nNljGQNowochSxTx73EGPH53ny0Vor9IZ08muezE1HfXz1Gy2X0hd3KNSytEAiUpBPGWMGJA92ZSUAfrBD36ArVu34rrrrqv09hATQFhTEEuV77rptlsXgbtogdfGyORYgVMyxz/IaYP3aRcYYGWAhAO0XzMcYubTmXgKGZNnZ4C1WTPA2iPaqAFmNKhCU6ySA3hxra2qzCDLVmmzJaz4NoitJbIZIMM5VyIllsAAaw5fW1jDiaEUTsfTI4YY55PNABUjguYVywwAdhlMYVYWSJEr5tkjAimvXle1huhss7RY1sW5KVB6qS+U1/ErS3nX7DJ2odiu4XT1fYBK+jNSqRQuuuiiSm8LMUG0RzR0N4fK/pywy2tCHLhOBkgZ6QItKGSgmG2DL29K/USiyBJkWfKlCaJADESN2TfNbAdYEBmDF+zME4iSQ/9wGrLMivKb0WQGlUlIZAxfDrOtRbI3hqybb1gvTQQNWIFMR8QKkE/FUhhOGSMaV9yIAMirFxRgnccJVza33HNZHJOn4ymENG9diV4QQZ1ffcfKpZD8QJSayvk89wzHZMYtgi79exYDUYdTZtUzQCUdCZ/97Gdx//33V3pbiAkirCtoDpV+Mrg/B7DrzPaBG7MnVutjzJ0Rq4l4cqSnRCVWjROFyhgUSfKlCaKgNaQ6wdmxwUTODDBIfMx0vyg5xFMGVCYVNd/H6i5kSKbHnzJOeCPiCoAG7YaBpkDpGaD8Vvi0MbYOKGN4b4MXr8mY3DFBrISeTxyTjFlO55VCHKOq4t+McznoSq6doMykssTeTgnMda1Pur3bSv7kbGCWSNeoCDqRSODuu+/G448/jgULFkBVcw/Uf/mXf6nIxhH+IlcEbT034MoAjbawEquJ4bQlemNMyrbBK/71AbIyQMyXJogCVZHRElRxfCiF4aThtLZPbwmCSdK4gtZoQEVIt9rfi1lty3bAxFh9DpesBkIbkUgbGLIXFpGAWnIDgzsAOjmUQtqwRqWM9n05GaAijBDTLhG0VqY/jKA5pKI1pDkLrkogspSq7N+mi3KQJEsvJYJRvcxgNKi5NUDWc2LRWm6mLxq0A/2Ugbz+qUmnZCPEhQsXAgD++Mc/5vzOrytlonyE0C6VyQrj3HNnxssADdviaQYpJ23u10NGsadI+7ltVmESWkIajg+lcLg/AcO0hM/RgIqkYYzb8dIUUNCkK1awV2QqzhLo+m9IbK0iSmCJtOGMIGgqo4FBkxmmNVsi+WO2F1A6YwJ64deni8gAie/cCoAqm81t0hV0NukFfcVKRVg8VKJM51cCKssJgMrxTwq6nKVHlMDKDK6aRQYoYyBT5QiopCNs+/btld4OogbI8QGyVwVipTrW5OHsnBpL9a/ApQFS/HtBkiRrErdfO8AA66bTapc3Xzl0BoDVAZYxOXRFHresxZiEziYdKQ/TwvMJqlaH0njTwwlviJJP/3B2REA0qJZsYKrIDNNbrQDIygxmp7fnwzkvTgOkiACIu266pY9ecCNJEuZOi5b9OW5Eh6OfNYflEtRkZ4SKXuIkeEFIFXKHbLY/pwusnABIaIDShnPMVQv/Lm0J3+HUhY1s+2LM1QY/XgYokTacdGqiQqnaiaa3PVyUNmaykZmEdlvo+trRAQCWADqVMdESGr0DzM2M1lBJjqy6KiOgFjfxnBidiB0ACQE0k7Kz9EpldrvV/HAqlkIyzZ2bWD6GySEOAS8ZPXFOiAG8gKWv8Wtw4c4A+XMLy8etxQso5WXjsh2/7gxQ1r2/rDb4oNAAmRQAEbWDe+Ce6AIbdJfARrluugOg/HSq31PSlRRiThSi00eUMMQMMK9dIKUGMKrMoKuybzvkao1IXttySFPKnls1rTmAgMqQSJs4HU85N7F83OJozcP3KYKkjGk64lhNYWW1R08kbhG0n6835eC2DNBVuWIlMG7HKLlZ+9K3U5R6DZMjnqxuAOTTw5XwIyGXBkgEQMIHKDRGBkiIGROuidTD6cp5hzQ6wgtI0NsWAjif8O4sTWG2ASZdRipBRMsPgMrft7oqY4qrFd5tReHGXRpTi2iDTxtmtgTm48WMuAYFVNm3QVq5BEdkgMoIgJxs/0gNkFbm+CK343x/IlXy51SCOj0UiIlArApMnl0NxOzxFgF1dB+gQtohxwla8a8TdK0gxmEIprcEIbPCDtCVJBpQcHZnhAKgCpHv3GsFQOWdG5rM0CE6wWJJx7U5H3cpwksXWI4GKCUyQKVPH59orpw3FVed24ml86fVbQnM7QU0liazmM9yi6Cz3m1yWbIFmUnO5w8mMmN6U000dOUiPOOeoizGWggNUFAb/aQIu6bI87zVRD3O5ZlsOl0BkCpLaA1pUOXxW+DLxRKJUxW9Uoi5fYKQJkMbZbyMV9QcL6AU0iYvqLsQz8nM2+pec2mAhl1t8H7NADWHVHzxinNw7rSob7exXHIyQGVmutzT5UcuWssbhgpktW3xKk+Ep7sP4RnVNfVbZH5ESj2gjb7iiOQYKFrPub1DiPJwl8CmtwSRMTk0efwOMMJ/uBcZYb20QahuNJk5GcLjQ0lkjMLCU2GCqDBvNzd3G7y4BpSrV5poJAmAhLoNgEI5GSBWVgYo4AqA8hetlfD9ijhz78aeTzfR0BWSKApxYsRsp9qYnQkKq8q4GqBkJuv8mTVC9PdFsxboiOjOxa63LYS0YSIcKE8ESVSHkEsfEdLKH2KsKwxdwgtoIImMwZEuYHkgNEAK89bfkxVB85yOTj8HF1ZwB193nZaD+9gZqyvXC04JzDALtsGXS8Q1ELWaUABEFEV2rpd1Mgw7GaDRjbdCeralMn81QXOkykdTmDPqpMcOgJqoNFWTuFfxIW10d3WvSJKEmXYr/FsDCWRMs6AXkLsE5uW+KTLBGcPEsEvP5+eYm9nlvXoNgNwaoKBa3rGTWwKzNUCZrNi9XMSieDRN2mRR1QDomWeewbJly9Dd3Q1JkvDQQw95fu+OHTugKIrjSO3mzJkzuOGGG9DV1YVAIIBzzz0XjzzySOU2vIEJOhkgK/MjIviwNvoN1zFCdNeTSQNUMVSZoSOiAQBmtYfBYZUkidrDPf4hNEZZuRhmdYQgwTrnBhPpwgGQnRWSmbdxFo4I2uQYTrnG4fg4upAl6y/z8zaWQyg/AKpAF1jGpRlzO0GXi2iFr3YGqKrLxFgshgsvvBCf+cxn8Fd/9Vee39ff34+VK1fiyiuvxFtvvZXzu1Qqhfe///3o7OzEgw8+iBkzZuDQoUNoamqq9OY3JGKVIdrf4y7L/tHI+gcZIwbrhehGXTYKk7By8Sy8ergf75zZipNDKcqs1SjuTrBIGWMw3ER0FS0hFafjaZwcSiFVQHfhlMBkb14+olMs4xqFoZU5fmGikW19U6MEQJXoAgOAhH2NF9fsQAUCICcDVGUNUFUDoKVLl2Lp0qVFv+/zn/88VqxYAVmWR2SNfvzjH+PUqVN47rnnnCGtM2fOrMTmEsiuDIbTJkzTdAKhcGD0G67bVTSrATJzPo8oHUVmOKczglntYRgmh6pIlFmrUdyZ1EigfBE0YGlzOiI6TsfTOBVLFbzppEvVALmcoP0+FFdmEhgkX5fpysGtAQpq5WkA3Q0UQqiczdqXf80WBrMNXQIrhS1btmDfvn3YsGFDwd8//PDDWLx4MW644QZMnToV8+fPx+233w7DGH1HJ5NJDAwM5DyIwrgnuydcvj4RfXTXYXFipg3udJsIQV2AbtQVIaDKyBgmUhkTmswmvAWemBjcbtARXSl5DpgbzQ6AAOBMPF2w7JATAHnpAhNt8GZ2Grzfz2UJEphcvwO73eXTcJkaQGu6vPV9CrmDyBKGtMqJoKudAfL3EZvHnj17sG7dOtx3331QlMJf8P79+/Hggw/CMAw88sgjuOWWW/Cd73wHt91226ifu3nzZjQ3NzuPnp6eifoTap6sqaGBAbsTDMjWdMd6DwDEU9Z7hKDOz5PWa4mAymDY9fpwhW6cxOTjdoOubABkacROx1NIuTKxghwRtJfPFCJokyNhL2ZCPj+XJQkVKSn6FXc2Pd9UsxREGV1k+VNO1r787zkaFBqgTNmfVQ41czQYhoEVK1Zg06ZNmDt37qivM00TnZ2duPvuu/HOd74T1157LW6++Wbcddddo75n/fr16O/vdx6HDh2aiD+hLhAZoETadCbBj5dx0F3dIYOJ3JNposc1NAoBVYbBuT0DzN83ImJ0wq7vrimgVkQErcnMMcs8GUsjbY70AhKzwBSPk77dTtBOBqgCmYGJhDEJSh0vDHI8pMZoSvFKIK/j1ymBVUQErdqf3cAaoGIYHBzEzp078fLLL+PGG28EYAU7nHMoioJt27bhiiuuQFdXF1RVhexyUD333HNx9OhRpFIpaJo24rN1XYeu6yOeJ0YiylmJjIGhRLb2P9ZK1UqnyoinDMRSBkyTOxdc0qpUBkVmgASYJgWVtYy7maApUJkMkCIzTIuKeWBJZEyOlGHmHCe5GqAihqEapquhwd+3Ewmo6wAokJMBqkQAZH3H+RmgSnSYRgP+aIP39xHrIhqNYteuXTnP3XnnnXjyySfx4IMPYvbs2QCAiy++GPfffz9M0wSz052vv/46urq6CgY/RHG4J7sP2SWwgDr+qjFoB0BDeW24legoIACVSTBNgEkUANUyInvHJOsmUSm9Sk+r5QV0fCgJw+BIZ/IzQKILzKsPUNYIMdvQ4O9zWVNYXdtDuDNAosRUDuI6Irr8hHltJTpMRQao2hqgqgZAQ0ND2Lt3r/PzgQMH0NfXh7a2NvT29mL9+vU4fPgw7r33XjDGMH/+/Jz3d3Z2IhAI5Dz/hS98Ad/73vewevVqfPGLX8SePXtw++2340tf+tKk/V31jLuja8AuZ+nK6INQBUFNBmJW91jCddDTzboyKLJVZtTkiR+CSkwcQgQd1OSKuqTP7LACoFjSQDxlOBlYgfABUpi3EpgIgAx3AOTz4KIjojti8HrEHQBVwgg1WwKzBpamKjgKoyXkjy6wqgZAO3fuxOWXX+78vGbNGgDAqlWrsHXrVhw5cgQHDx4s6jN7enqwbds23HTTTViwYAGmT5+O1atXY+3atRXd9kbFMTVMmxhKZDNA43lrCIHeUDLjXDAlyVpxEuWjyBJkWYKqUABUy0TtbsqQqpQ9Cd5NS0hDWJMRSxk4FUuN0ACliiyB6Y4GyHTO57A2eicoMfGIrIoqSwhUQKicdf03kDG50/FbiSxac5A0QLjsssuc0QiF2Lp165jv37hxIzZu3Dji+cWLF+O3v/1tmVtHFCLijLUwMGS3R+oeXEezBopGdqqwzCDXcVfGZKIyBkWSENYUSw9E1CTvmNmC7uYA5k9vrqivjiYztIU1xFLDOBNPOe7Ngtw2+PE/T2SA3CUM8vSqLtOaA/ibJbMRT2UqMu4j4Mr2p1wl00AFri89bSH83xVvr3q3as1ogAh/ILoLEmkTg/ZE+KAij2su5miHUgaG09bF16vegBgfKwPEEBnDkJLwP61hHf/nEwtxpD9RkaGTAk1haI/oOHR62PYCGk0DxDwaIVqvEvoQwP8+QI3AqsWz8MrhMxUJLISmK5k2ckpVlTBCVGWGGa0hZxFdLeiIJYrCPdndXQIb74QTHiHDGcMZnqjKzFO6nRgfhUkIKAxBn3fiEOOjKtbCoJKr4xwvoOE0htNGTvbdaYNn3kZF5AdnTKrMjCiiPCyvo8qM+xAZvWTGdBatcgWtBPwgf6AjliiKoDPXy0TMzgDpqjxut4qYCJ9wrSY02d/To2sJSZIwNRpw2kuJ2kVh1oKiogGQzNDZZHkBnY6lRkyFFyWOYn2A3J9frzO2agkrQGEVGaGSHWJtOJk+VZZQqTWr6oOLP10tiaLI+gBl54AFtfEzQLkjNKwASJHrdzBhNehpC1V7E4gKIFbwlTBBFEiShOktQQDAyaEkMoaJtMEhmoUyprsN3sM0+LwMkKpQAOQHmCSBscpMvBclzUTGdLReKqvc9+wHrWL1t4CoKUKuDJCo3wbV8TVAYVf3mDiZNI+rTYJoJFQ7m1JpgeiMVisDdGIohYyBHC8gUQLzuirPL3dRNtcfMLt0WongWSx2U2nTaVxR5MqJFhTG4E1xNnFQAEQURVDLdoEJLU/AQxdY2GWgKDJAqszqdjAhQZSKYussKh0A9baFAQAnY8kR4zDcJTAv5Lfoqwqdy35AkiTIFcrSuK/1YnajWsFSJ2OAVOUIhAIgoijcGSCh5Ql5CIBCjng6a4SoKrRqJIh8ZMYgM2t2VSWZ2hyAzCSYHOgfznVkd7fBe0FllAHyI0yIoCuhARIiaMPMW7SW/9mAnamiAIioJUQ3l8nhjMIIauPPLMpOkTcdQZ3mUW9AEI0EY1YmptJzq3SFodV24D0dS+V4u4j/92q+mD9YVCMNkC9gklSxLjCxaE25rtlqBa/ZsuTNdHMioQCIKAq33f3puB0AedAARfTsEFX3aoJWjQSRi9PJU+GAQlUsM0TAygDFXWaIbh8gz5/nei3p+fwBk6SK6cdEG3wqYzpzwCqZAWITUOYtehuq+q8TNYd78vuZeAoAENbHb4MP664M0ATUkwmiXmCSBFWunN+KQJMZptizsE7HUjljCFJFlsCA3GyRRhogX2BlDytjMBvUbCPETG4GqFLXbFmqfhcwBUBE0YiVgegcEcHNWLg9JURHAc2sIoiRiDJGpVfHCpMwpckKgE7F00gZHBk78HGGoZaaASI9ny+I6ApmtoWhV2CQrhiGmsrTAFXqe9ZVBl2t7nFDdyCiaPIt7yOB8Ycghl0i6OE0ZYAIYjQiuoKOJr3iGRVFZpgatVrhT8WyXkAAkDaL0wABuWaIOp3LvkCSJDSHKjOU1l0CczRiSuXc+0OagnnTolXNHFIARBRNKG/cQpOH8QtuW3WxmqBVI0GMJKjJmNE6MaaW3S1WAHRyKIWMwZ0bW1YEXU4GiE7mesJx/TdMJDPZAdb19DVTAEQUTf7UZy8DON0zxNzT4Ek3QBCTR6/tFn5iKAnDNQ5DiKCLGU8wUgNUwQ0lqo47A5SYABG0H6AAiCgadycYk0ZmhArh9g8SJxNlgAhichGZpVjKMjJNOwGQVQrTitCOuDNAOmWA6o6gWwPkzG+svnC5klAARBSNOwDSFAZVHv+iKd5jcmAokXHeSxkggpg8moOq07RwKpZyBUDFa4BIBF3fBOxrNufAUDJrXltPXzMFQETRhFwlsIAie0qJut9zZtjyD8qfKE0QxMSiygztYasT7Ew82wovSmG66v2czBFBK+O7wRO1hVvq0D9sWZ5odSZ2pzsQUTTuDJCujj8JHrA6UMQE6QERAFXbB50gGgxVYWi3zRDPDKcxnLaysWIwajHWFPklsDq6LxKwvl9xbR+0s/akASIaHrfmJ1DEyk+0zw/YJxP5ABHE5KLKEjqasmaIyRSHafLsNHgP5WyBRkaIdY9zzXZl7evpe6Y7EFE0obwMkNfBeyJzNJhI2+8t36yLIAjvaDJDpwiA4mmk7U4woQEqKgPkem2+NxhRHwRsUbxYtKp1tmitr7+GmBTcAVDAwyR4gagpm1z8TIcfQUwmkiShuzkIADg5lETatIKfjH1SFlOWdr82qI7fCUrUHsIN2lm0FiGSrwXoDkQUTUjPFUHLXgMgLTfjE9AoA0QQk830VssM8fhQEpmMVf5yusCKWOFrlAGqe8T3OpQUnbv1dc2mo5YomrBLAxTUvGeA8v2CQlQCI4hJp7ctDMBqgze5FfyUVAJzZYBCOmWA6pFAXta+GKfwWqC+/hpiUnAHQKVogJz31tlqgiBqganNOmQmweTAmeEUUpnsTLCiMkDuEhidy3VJvut/vWX66uuvISaFSMCVASqiCyycFwCFqARGEJOOrshotQdmno6lEU9lYJSiAXIFS/mLG6I+yA+A6u2aTQEQUTTui11Qkz35AAEjhZKUASKIycdthtg/nHY8XgBrqrtX3OWyersxEhb5Os16u2ZTAEQUjVu7E9S8OUEDIy+SxbjOEgRRGSwvIMsM8XQ8NwBSlNJGYVAAVJ/kf6/11rlbX38NMSm4xcxB1XsXWFiv73oyQdQCqswwpcnqBDsVSyKRzgZAWhFGiGpOGzwFQPVI/vca9DD4upagOxBRNDklsCJ8gMJ5J0+ALpoEMemoLjPEU7EUkmlL/yNJgFLMMFRXtoiyufXJCOsSKoERjY47LRrSZTCvGiBXBohJxa02CYKoHNNbrAzQiaEUkhlrIKrCpKImfQvBtCpLkL22ghI1RX4GqN4C3ar+Nc888wyWLVuG7u5uSJKEhx56yPN7d+zYAUVRsHDhwlFf88ADD0CSJHzkIx8pe1uJLO4AKD+rMxYR12vrbageQdQSPW0hAMCJoSSStgeQwoqb8yS6wNQ6mxBOZBmh26RRGJUjFovhwgsvxPe///2i3tff34+VK1fiyiuvHPU1b775Jv7+7/8el1xySbmbSeThTos2BbwHQGGdAiCC8AMzWqwAKJ4yHJdfmUnwmMwFkNUAaTQJvm7J1/zUmxN0VRVNS5cuxdKlS4t+3+c//3msWLECsiwXzBoZhoFPfvKT2LRpE37zm9/gzJkz5W8s4aDJDCFNxnDKQIvtJ+KF3ABIolUjQVSJ5pCKiK5gKJnB8cEkACsAKqYI5gRAtJipW/IzQPXWuFJzf82WLVuwb98+bNiwYdTXfP3rX8eUKVPwN3/zN54+M5lMYmBgIOdBjI4kSdiw7DyseE8v2iO65/e5jRBVmRWlNyAIonKoMkNb2GqFPzGYAmBpgIo5KakEVv/kB0DFGGXWAjXV07Znzx6sW7cOv/nNb6AohTd9x44d+NGPfoS+vj7Pn7t582Zs2rSpQlvZGFxyzhSENcW6aHokmBcA0UWTIKqDKktoj2g4eCqO40MJAFYHWDGnZMAOgAIqncv1irtT1xK719f3XDPhnGEYWLFiBTZt2oS5c+cWfM3g4CA+9alP4Z577kFHR4fnz16/fj36+/udx6FDhyq12XULk6yLZTHD8UIalcAIwg+oMsMUO3srSmDFdoEtPqsdl82dgg+eP60o7RBROwTVvKx9nV2zayYDNDg4iJ07d+Lll1/GjTfeCAAwTROccyiKgm3btqGtrQ1vvPEGli1b5rzPNO0OB0XB7t27cdZZZ434bF3XoeveSzkEwBggs+JWfqG8DBDVwAiiOmiuAOjogJUBkovsAmsKqLjlw+fijRPxursxEhburL3CissQ1gI1EwBFo1Hs2rUr57k777wTTz75JB588EHMnj0bsiyPeM0tt9yCwcFBfPe730VPT89kbnJdwyQJCpM8ewABeQGQwmjVSBBVgjEJXbYXUCyZ9QEq9pyUJQkSA53LdUp+BqjesvZVDYCGhoawd+9e5+cDBw6gr68PbW1t6O3txfr163H48GHce++9YIxh/vz5Oe/v7OxEIBDIeT7/NS0tLQWfJ8qDSRLkIi987i4wrQ5PJoKoJabbrfACRS6uCwywAikZVM6uVwL5JbAqbstEUNUAaOfOnbj88sudn9esWQMAWLVqFbZu3YojR47g4MGD1do8YgyYZKXMvc4BAywTLQkAB6AVKbgkCKKyzGwP5vxsGSEW9xkSrAwQncv1SW7jSv0FulUNgC677DJwzkf9/datW8d8/8aNG7Fx48YxXzPeZxClwZhk14S9nxCSJCGgyhhOG3WZTiWIWqIzGoDCJGRM6xpcSoePJFllMDqX65P8Eli9pYBqpguM8BdMkiCX0BYpdEDkHksQ1UVXZLTaXkCAtcIvOgMkZTtCifojqOZngKq4MRMABUBESTBn5Vfc+0RNWVNY0XoDgiAqhypLaHcFQJYIurhzUpKsRgjKANUn7tlf9dgGTwEQURKldIEB2ZqyVQKbiC0jCMILqszQ4XJyV0oQuUqwFkMUANUnjElOEFSP12wKgIiSUJgETZGLtkZ3SmCkASKIqqLKDFOacjNAxa7wRQms3m6MRBaRta9HETQFQERJKDLDed3RnDZJL4iask4aIIKoKqosYUpTwPm5lAyQZYdRfOBE1A5iAKqq1F+4UH9/EeFrwvY4DFWpv3oyQdQSkiRheku2Fb4Up1/Jfh9Rvzi6zTrM2lMAREwq1yzoQk9rEO+Y2VrtTSGIhqenNRsAlSJy1VUZTQG10ptF+AinBFaHWfuaGYVB1AfvP38qWsIqWkN00SSIatPTlnWDVuTi727NQRXNQTqX65kgZYAIojIwSQIDIEt06BFEtWkKqGiyR9SoJQRARP0jNEBaHY7CoLsQManIkiWYrLOFBEHUJKosoc32AlKL7OgkGoOg6rIuqTO9Fx3xxKQiSdaDhJMEUX1UhaE9QgEQMTpC4xXSiuv4rQVIA0RMKkySLAdpCoAIoupoMsOlc6fgxFAKi6gxgSjA3yyZjWTGwCXndFR7UyoOBUDEpCI8Q4qZIk8QxMSgygzvmtWGOR0RzOwIjf8GouGYO7UJy9/dW5did8p5EpMKkwCG4oeoEgRReWQmQVMYMiYnN2eiIEK2UG8dYAAFQMQkYw1PBAVABOETxHw+Rp2ZRAGsUSf1Oe6Ejnhi0lFlmgRPEH4hZHf5MLobEAVgkrVwrcdFKx3yxKSjyNQGTxB+QVNlyIxuBkRhhHdbPTau0DFPTDpaHfpJEEStojIJCp2TxCiwOm5coS4wYtJpDWsIKPXnKUEQtYgqM6is/sYcEJVDZvUpgqYAiJh0pkYD1d4EgiBsVIVBkevzBkdUBpmRBoggCIKoM1RZssYcUABEjIJSpxlCCoAIgiAaGE1mUGRGjQnEqKh12rhCARBBEEQDI0mS4wVEEIVQ6zRAJg0QQRBEgxPVFRqGSoxKd0uwLjVAFAARBEE0OLM6wpDqcYlPVISwXp+hAoX8BEEQDQ4FP0QjQgEQQRAEQRANBwVABEEQBEE0HBQAEQRBEATRcFAARBAEQRBEw0EBEEEQBEEQDUdVA6BnnnkGy5YtQ3d3NyRJwkMPPeT5vTt27ICiKFi4cGHO8/fccw8uueQStLa2orW1FVdddRVeeOGFym44QRAEQRA1TVUDoFgshgsvvBDf//73i3pff38/Vq5ciSuvvHLE75566iksX74c27dvx/PPP4/e3l5cffXVOHz4cKU2myAIgiCIGkfinPNqbwRg+VD84he/wEc+8pFxX3vttdfinHPOgSzLeOihh9DX1zfqaw3DQGtrK77//e9j5cqVBV+TTCaRTCadnwcGBtDT04P+/n5Eo9Fi/xSCIAiCIKrAwMAAmpubPd2/a04DtGXLFuzbtw8bNmzw9Pp4PI50Oo22trZRX7N582Y0Nzc7j56enkptLkEQBEEQPqSmAqA9e/Zg3bp1uO+++6Ao3qy5161bh+nTp+Oqq64a9TXr169Hf3+/8zh06FClNpkgCIIgCB9SMwM+DMPAihUrsGnTJsydO9fTe775zW/ipz/9KZ566ikEAoFRX6frOnRdr9SmEgRBEAThc2omABocHMTOnTvx8ssv48YbbwQAmKYJzjkURcG2bdtwxRVXOK//9re/jdtvvx2PP/44FixYUK3NJgiCIAjCh9RMABSNRrFr166c5+688048+eSTePDBBzF79mzn+W9961v4xje+gUcffRSLFi2a7E0lCIIgCMLnVDUAGhoawt69e52fDxw4gL6+PrS1taG3txfr16/H4cOHce+994Ixhvnz5+e8v7OzE4FAIOf5b37zm7j11ltx//33Y9asWTh69CgAIBKJIBKJeNou0Rg3MDBQ7p9IEARBEMQkIe7bnhrceRXZvn07BzDisWrVKs4556tWreKXXnrpqO/fsGEDv/DCC3OemzlzZsHP3LBhg+ftOnToUMHPoAc96EEPetCDHv5/HDp0aNx7vW98gPyEaZr485//jKamJkiSVO3NAZD1Jjp06BB5ExWA9s/o0L4ZG9o/Y0P7Z3Ro34xNNfYP5xyDg4Po7u4GY2M3uteMBmgyYYxhxowZ1d6MgkSjUTrRxoD2z+jQvhkb2j9jQ/tndGjfjM1k75/m5mZPr6spHyCCIAiCIIhKQAEQQRAEQRANBwVANYKu69iwYQMZNo4C7Z/RoX0zNrR/xob2z+jQvhkbv+8fEkETBEEQBNFwUAaIIAiCIIiGgwIggiAIgiAaDgqACIIgCIJoOCgAIgiCIAii4aAAyEds3rwZ73rXu9DU1ITOzk585CMfwe7du3NewznHxo0b0d3djWAwiMsuuwyvvvpqlbZ4crnrrruwYMECx1Rr8eLF+J//+R/n9428b/LZvHkzJEnCl7/8Zee5Rt4/GzduhCRJOY9p06Y5v2/kfSM4fPgwPvWpT6G9vR2hUAgLFy7Eiy++6Py+UffRrFmzRhw7kiThhhtuANC4+0WQyWRwyy23YPbs2QgGg5gzZw6+/vWvwzRN5zW+3UeeB2QRE84HPvABvmXLFv7HP/6R9/X18WuuuYb39vbyoaEh5zV33HEHb2pq4j/72c/4rl27+Cc+8Qne1dXFBwYGqrjlk8PDDz/Mf/WrX/Hdu3fz3bt3869+9atcVVX+xz/+kXPe2PvGzQsvvMBnzZrFFyxYwFevXu0838j7Z8OGDfz888/nR44ccR7Hjh1zft/I+4Zzzk+dOsVnzpzJP/3pT/Pf/e53/MCBA/zxxx/ne/fudV7TqPvo2LFjOcfNY489xgHw7du3c84bd78IvvGNb/D29nb+3//93/zAgQP8v/7rv3gkEuH/+q//6rzGr/uIAiAfc+zYMQ6AP/3005xzzk3T5NOmTeN33HGH85pEIsGbm5v5D37wg2ptZlVpbW3lP/zhD2nf2AwODvJzzjmHP/bYY/zSSy91AqBG3z+FBicLGn3fcM752rVr+ZIlS0b9Pe2jLKtXr+ZnnXUWN02T9gvn/JprruHXX399znMf/ehH+ac+9SnOub+PHSqB+Zj+/n4AQFtbGwDgwIEDOHr0KK6++mrnNbqu49JLL8Vzzz1XlW2sFoZh4IEHHkAsFsPixYtp39jccMMNuOaaa3DVVVflPE/7B9izZw+6u7sxe/ZsXHvttdi/fz8A2jcA8PDDD2PRokX42Mc+hs7OTrz97W/HPffc4/ye9pFFKpXCT37yE1x//fWQJIn2C4AlS5bgiSeewOuvvw4A+MMf/oBnn30WH/rQhwD4+9ihYag+hXOONWvWYMmSJZg/fz4A4OjRowCAqVOn5rx26tSpePPNNyd9G6vBrl27sHjxYiQSCUQiEfziF7/Aeeed55xIjbxvHnjgAbz00kv4/e9/P+J3jX7svOc978G9996LuXPn4q233sI3vvENXHTRRXj11Vcbft8AwP79+3HXXXdhzZo1+OpXv4oXXngBX/rSl6DrOlauXEn7yOahhx7CmTNn8OlPfxoAnVcAsHbtWvT392PevHmQZRmGYeC2227D8uXLAfh7H1EA5FNuvPFGvPLKK3j22WdH/E6SpJyfOecjnqtX3va2t6Gvrw9nzpzBz372M6xatQpPP/208/tG3TeHDh3C6tWrsW3bNgQCgVFf16j7Z+nSpc7/X3DBBVi8eDHOOuss/Md//Afe+973AmjcfQMApmli0aJFuP322wEAb3/72/Hqq6/irrvuwsqVK53XNfI+AoAf/ehHWLp0Kbq7u3Oeb+T98p//+Z/4yU9+gvvvvx/nn38++vr68OUvfxnd3d1YtWqV8zo/7iMqgfmQL37xi3j44Yexfft2zJgxw3ledK2IiFpw7NixEdF1vaJpGs4++2wsWrQImzdvxoUXXojvfve7Db9vXnzxRRw7dgzvfOc7oSgKFEXB008/jX/7t3+DoijOPmjU/ZNPOBzGBRdcgD179jT8sQMAXV1dOO+883KeO/fcc3Hw4EEAdO0BgDfffBOPP/44PvvZzzrP0X4B/uEf/gHr1q3DtddeiwsuuADXXXcdbrrpJmzevBmAv/cRBUA+gnOOG2+8ET//+c/x5JNPYvbs2Tm/nz17NqZNm4bHHnvMeS6VSuHpp5/GRRddNNmb6ws450gmkw2/b6688krs2rULfX19zmPRokX45Cc/ib6+PsyZM6eh908+yWQSr732Grq6uhr+2AGAiy++eITlxuuvv46ZM2cCoGsPAGzZsgWdnZ245pprnOdovwDxeByM5YYSsiw7bfC+3kfVUl8TI/nCF77Am5ub+VNPPZXTdhmPx53X3HHHHby5uZn//Oc/57t27eLLly/3RTvhZLB+/Xr+zDPP8AMHDvBXXnmFf/WrX+WMMb5t2zbOeWPvm0K4u8A4b+z985WvfIU/9dRTfP/+/fy3v/0t//CHP8ybmpr4G2+8wTlv7H3DuWWdoCgKv+222/iePXv4fffdx0OhEP/JT37ivKaR95FhGLy3t5evXbt2xO8aeb9wzvmqVav49OnTnTb4n//857yjo4P/4z/+o/Mav+4jCoB8BICCjy1btjivMU2Tb9iwgU+bNo3rus7f97738V27dlVvoyeR66+/ns+cOZNrmsanTJnCr7zySif44byx900h8gOgRt4/wndEVVXe3d3NP/rRj/JXX33V+X0j7xvBL3/5Sz5//nyu6zqfN28ev/vuu3N+38j76NFHH+UA+O7du0f8rpH3C+ecDwwM8NWrV/Pe3l4eCAT4nDlz+M0338yTyaTzGr/uI4lzzquYgCIIgiAIgph0SANEEARBEETDQQEQQRAEQRANBwVABEEQBEE0HBQAEQRBEATRcFAARBAEQRBEw0EBEEEQBEEQDQcFQARBEARBNBwUABEEQRAE0XBQAEQQBEEQRMNBARBBEARBEA0HBUAEQRAEQTQcFAARBFEX/PrXv8aSJUvQ0tKC9vZ2fPjDH8a+ffuc3z/33HNYuHAhAoEAFi1ahIceegiSJKGvr895zf/+7//iQx/6ECKRCKZOnYrrrrsOJ06cqMJfQxDEREMBEEEQdUEsFsOaNWvw+9//Hk888QQYY/jLv/xLmKaJwcFBLFu2DBdccAFeeukl/NM//RPWrl2b8/4jR47g0ksvxcKFC7Fz5078+te/xltvvYWPf/zjVfqLCIKYSGgaPEEQdcnx48fR2dmJXbt24dlnn8Utt9yCP/3pTwgEAgCAH/7wh/jbv/1bvPzyy1i4cCG+9rWv4Xe/+x0effRR5zP+9Kc/oaenB7t378bcuXOr9acQBDEBUAaIIIi6YN++fVixYgXmzJmDaDSK2bNnAwAOHjyI3bt3Y8GCBU7wAwDvfve7c97/4osvYvv27YhEIs5j3rx5zmcTBFFfKNXeAIIgiEqwbNky9PT04J577kF3dzdM08T8+fORSqXAOYckSTmvz09+m6aJZcuW4Z//+Z9HfHZXV9eEbjtBEJMPBUAEQdQ8J0+exGuvvYZ///d/xyWXXAIAePbZZ53fz5s3D/fddx+SySR0XQcA7Ny5M+cz3vGOd+BnP/sZZs2aBUWhSyNB1DtUAiMIouZpbW1Fe3s77r77buzduxdPPvkk1qxZ4/x+xYoVME0Tn/vc5/Daa6/h0Ucfxbe//W0AcDJDN9xwA06dOoXly5fjhRdewP79+7Ft2zZcf/31MAyjKn8XQRATBwVABEHUPIwxPPDAA3jxxRcxf/583HTTTfjWfy7sTAAAAP5JREFUt77l/D4ajeKXv/wl+vr6sHDhQtx888342te+BgCOLqi7uxs7duyAYRj4wAc+gPnz52P16tVobm4GY3SpJIh6g7rACIJoSO677z585jOfQX9/P4LBYLU3hyCISYYK3QRBNAT33nsv5syZg+nTp+MPf/gD1q5di49//OMU/BBEg0IBEEEQDcHRo0fxta99DUePHkVXVxc+9rGP4bbbbqv2ZhEEUSWoBEYQBEEQRMNByj6CIAiCIBoOCoAIgiAIgmg4KAAiCIIgCKLhoACIIAiCIIiGgwIggiAIgiAaDgqACIIgCIJoOCgAIgiCIAii4aAAiCAIgiCIhuP/B3s3BF3t2d/jAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Creating the line chart\n",
    "line = sns.lineplot(data = df, x = 'age',y = 'number_of_dependants')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0a9f738",
   "metadata": {},
   "source": [
    "There is not a clear trend between number of dependents and age. Yound and older people have different number of dependents."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08e8626b",
   "metadata": {},
   "source": [
    "### Family status and frequency of orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5d72b834",
   "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>family_status</th>\n",
       "      <th>days_since_prior_order</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>divorced/widowed</td>\n",
       "      <td>10.750533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>living with parents and siblings</td>\n",
       "      <td>10.750641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>married</td>\n",
       "      <td>10.821002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>single</td>\n",
       "      <td>10.813100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      family_status  days_since_prior_order\n",
       "0                  divorced/widowed               10.750533\n",
       "1  living with parents and siblings               10.750641\n",
       "2                           married               10.821002\n",
       "3                            single               10.813100"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Average \"days_since_prior_order\" per family status\n",
    "usage_table = pd.pivot_table(stakeholders, values='days_since_prior_order', index='family_status', aggfunc='mean').reset_index()\n",
    "usage_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b4b5af7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ich\\AppData\\Local\\Temp\\ipykernel_9512\\1365063461.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  status_order = usage_table.groupby('family_status')['days_since_prior_order'].mean().sort_values().index\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  grouped_vals = vals.groupby(grouper)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Determining the order of family status based on average days since prior order\n",
    "status_order = usage_table.groupby('family_status')['days_since_prior_order'].mean().sort_values().index\n",
    "\n",
    "#Plotting the bar chart with specified order\n",
    "ax = sns.barplot(x='family_status', y='days_since_prior_order', data=usage_table, order=status_order, color = '#1f77b4')\n",
    "\n",
    "plt.title(\"Average app Usage Frequency by Family status\")\n",
    "plt.xlabel(\"Family status\")\n",
    "plt.ylabel(\"Average days since prior order\")\n",
    "\n",
    "#Rotate x-axis labels for better readability\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right') \n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e75c2ad0",
   "metadata": {},
   "source": [
    "Regardless of their family status, our customers are using our app within a similar frequency, placing orders every 10 to 11 days. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c52d04fc",
   "metadata": {},
   "source": [
    "### Family status and expenditure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6e64849e",
   "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>family_status</th>\n",
       "      <th>prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>divorced/widowed</td>\n",
       "      <td>20664935.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>living with parents and siblings</td>\n",
       "      <td>11572096.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>married</td>\n",
       "      <td>169615014.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>single</td>\n",
       "      <td>39710369.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      family_status       prices\n",
       "0                  divorced/widowed   20664935.8\n",
       "1  living with parents and siblings   11572096.5\n",
       "2                           married  169615014.5\n",
       "3                            single   39710369.2"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total sales per family status\n",
    "usage_table = pd.pivot_table(stakeholders, values='prices', index='family_status', aggfunc='sum').reset_index()\n",
    "usage_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f53294c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ich\\AppData\\Local\\Temp\\ipykernel_9512\\885731133.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  status_order = usage_table.groupby('family_status')['prices'].sum().sort_values(ascending=False).index\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  grouped_vals = vals.groupby(grouper)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Determining the order of family status based on total sales\n",
    "status_order = usage_table.groupby('family_status')['prices'].sum().sort_values(ascending=False).index\n",
    "\n",
    "#Plotting the bar chart with specified order\n",
    "ax = sns.barplot(x='family_status', y='prices', data=usage_table, order=status_order, color = '#1f77b4')\n",
    "\n",
    "plt.title(\"Total sales by Family status\")\n",
    "plt.xlabel(\"Family status\")\n",
    "plt.ylabel(\"Total sales\")\n",
    "\n",
    "#Rotate x-axis labels for better readability\n",
    "ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right') \n",
    "\n",
    "#Changing y-axis to display values in millions\n",
    "ax.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}M\".format(int(x/1e6)))) \n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'bar_family_status_sales.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "#Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e20b159e",
   "metadata": {},
   "source": [
    "Based on family status, most of the sales are coming from customers who are married. This group generated more than 160M dollars in sales. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "864f4066",
   "metadata": {},
   "source": [
    "## 8.5 Demographic insights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c74b458",
   "metadata": {},
   "source": [
    "What different classifications does the demographic information suggest? Is there a relation betwen age and spending power (income)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "3099c21f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting age against income\n",
    "scatt = sns.scatterplot(x = 'age', y = 'income',data = stakeholders)\n",
    "\n",
    "plt.title(\"Age vs spending power\")\n",
    "plt.xlabel(\"Customer age\")\n",
    "plt.ylabel(\"Income\")\n",
    "\n",
    "#Changing y-axis to display values in thousands\n",
    "scatt.get_yaxis().set_major_formatter(plt.FuncFormatter(lambda x, loc: \"{:,}K\".format(int(x/1e3)))) \n",
    "\n",
    "#Add grid\n",
    "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
    "\n",
    "#Save the plot in the specified directory\n",
    "path = r'C:\\Users\\Ich\\Documents\\12-2023 Instacart Basket Analysis'\n",
    "filename = 'age_vs_income.png'\n",
    "filepath = os.path.join(path, '04 Analysis', 'Visualizations', filename)\n",
    "\n",
    "#Save plot before showing\n",
    "plt.savefig(filepath, bbox_inches='tight')\n",
    "\n",
    "#Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9996d486",
   "metadata": {},
   "source": [
    "Users below the age of 40 typically have a maximum income cap of 400K, with the majority earning less than 200K. As individuals progress into their 40s and approach the golden years, there is a potential for income to peak around 600K, although the majority are likely to earn below 300K during this stage of their careers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f59c5fa",
   "metadata": {},
   "source": [
    "# 9. Recommendations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cf01c0c",
   "metadata": {},
   "source": [
    "For a targeted marketing campaign, the following recommendations are suggested.\n",
    "\n",
    "1.- Optimized Ad Scheduling: To enhance the effectiveness of our targeted marketing campaign, consider scheduling ads during periods with lower order volumes. The analysis indicates that Wednesdays and Thursdays between 23:00 and 06:00 experience the fewest orders on our app. \n",
    "\n",
    "2.- Strategic Advertising Timing: Capitalize on peak user activity and spending by focusing product advertising between 09:00 and 16:00. During these hours, our users spend each hour over 17 million dollars. Aligning marketing efforts with this active period can maximize engagement and sales.  \n",
    "\n",
    "3.- Product Pricing: Simplify the product pricing scheme for the marketing and sales teams. Emphasize mid-range products priced between 5 and 15 dollars, as these have proven to be the most popular among our users.\n",
    "\n",
    "4.- Targeted Grocery Department Promotion: Acknowledge the popularity of certain grocery departments, such as produce, dairy eggs, snacks, beverages, and frozen items, where over 2 million products are ordered in each category. Consider intensifying promotions in these areas or strategizing to boost the visibility of other departments.\n",
    "\n",
    "5.- Loyalty Program Implementation: Launch a loyalty program targeting customers who currently contribute to 33.8% of all app sales. On average, these loyal customers place orders once a week, presenting an opportunity to foster continued engagement and loyalty.   \n",
    "\n",
    "6.- Demographic and Regional Insights: Leverage demographic insights to tailor marketing strategies. Recognize that customers across all US regions and family statuses exhibit similar app usage frequencies, placing orders every 10 to 11 days on average. However, note that married customers, particularly those older than 40, exhibit higher spending power, contributing to sales exceeding 160 million dollars in our current database. Consider refining marketing approaches to capitalize on these trends."
   ]
  }
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