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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "15cd454b",
+   "metadata": {},
+   "source": [
+    "# Week 06: Business Statistics & Analytics - E-commerce Analysis\n",
+    "\n",
+    "## Learning Objectives\n",
+    "By the end of this exercise, you will be able to:\n",
+    "- Calculate key business metrics using pandas\n",
+    "- Create meaningful visualizations for business insights\n",
+    "- Apply statistical concepts to real business scenarios\n",
+    "- Make data-driven recommendations\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Business Context\n",
+    "\n",
+    "You're working as a Data Analyst for **TechMart**, an e-commerce company. Your manager needs insights from last quarter's sales data to inform strategic decisions for the upcoming quarter.\n",
+    "\n",
+    "Your analysis will help answer critical questions about customer behavior, regional performance, and product trends that directly impact business growth."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "023b9e8d",
+   "metadata": {},
+   "source": [
+    "## Dataset Setup\n",
+    "\n",
+    "First, let's generate the synthetic dataset you'll be analyzing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "3608e087",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "✅ Dataset created successfully!\n"
+     ]
+    },
+    {
+     "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>customer_id</th>\n",
+       "      <th>customer_segment</th>\n",
+       "      <th>region</th>\n",
+       "      <th>order_date</th>\n",
+       "      <th>product_category</th>\n",
+       "      <th>quantity</th>\n",
+       "      <th>unit_price</th>\n",
+       "      <th>revenue</th>\n",
+       "      <th>discount_applied</th>\n",
+       "      <th>payment_method</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>52</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>North</td>\n",
+       "      <td>2023-01-01</td>\n",
+       "      <td>Clothing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>42.43</td>\n",
+       "      <td>42.43</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Debit Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>93</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>West</td>\n",
+       "      <td>2023-01-02</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>1</td>\n",
+       "      <td>24.18</td>\n",
+       "      <td>24.18</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Gift Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Professional</td>\n",
+       "      <td>East</td>\n",
+       "      <td>2023-01-03</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>3</td>\n",
+       "      <td>158.71</td>\n",
+       "      <td>476.13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Debit Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>72</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>East</td>\n",
+       "      <td>2023-01-04</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>1</td>\n",
+       "      <td>60.42</td>\n",
+       "      <td>60.42</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Credit Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>61</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>West</td>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>Books</td>\n",
+       "      <td>3</td>\n",
+       "      <td>43.78</td>\n",
+       "      <td>131.34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Gift Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>East</td>\n",
+       "      <td>2023-01-06</td>\n",
+       "      <td>Home</td>\n",
+       "      <td>4</td>\n",
+       "      <td>141.41</td>\n",
+       "      <td>565.64</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Credit Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>83</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>East</td>\n",
+       "      <td>2023-01-07</td>\n",
+       "      <td>Books</td>\n",
+       "      <td>2</td>\n",
+       "      <td>127.43</td>\n",
+       "      <td>254.86</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Debit Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>87</td>\n",
+       "      <td>Senior</td>\n",
+       "      <td>South</td>\n",
+       "      <td>2023-01-08</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>1</td>\n",
+       "      <td>130.31</td>\n",
+       "      <td>130.31</td>\n",
+       "      <td>20</td>\n",
+       "      <td>Debit Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>75</td>\n",
+       "      <td>Professional</td>\n",
+       "      <td>West</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "      <td>Clothing</td>\n",
+       "      <td>2</td>\n",
+       "      <td>53.49</td>\n",
+       "      <td>106.98</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Credit Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>75</td>\n",
+       "      <td>Professional</td>\n",
+       "      <td>North</td>\n",
+       "      <td>2023-01-10</td>\n",
+       "      <td>Electronics</td>\n",
+       "      <td>2</td>\n",
+       "      <td>54.20</td>\n",
+       "      <td>108.40</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PayPal</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   order_id  customer_id customer_segment region order_date product_category  \\\n",
+       "0         1           52          Student  North 2023-01-01         Clothing   \n",
+       "1         2           93          Student   West 2023-01-02           Sports   \n",
+       "2         3           15     Professional   East 2023-01-03           Sports   \n",
+       "3         4           72          Student   East 2023-01-04           Sports   \n",
+       "4         5           61          Student   West 2023-01-05            Books   \n",
+       "5         6           21          Student   East 2023-01-06             Home   \n",
+       "6         7           83          Student   East 2023-01-07            Books   \n",
+       "7         8           87           Senior  South 2023-01-08           Sports   \n",
+       "8         9           75     Professional   West 2023-01-09         Clothing   \n",
+       "9        10           75     Professional  North 2023-01-10      Electronics   \n",
+       "\n",
+       "   quantity  unit_price  revenue  discount_applied payment_method  \n",
+       "0         1       42.43    42.43                 0     Debit Card  \n",
+       "1         1       24.18    24.18                 0      Gift Card  \n",
+       "2         3      158.71   476.13                 0     Debit Card  \n",
+       "3         1       60.42    60.42                 0    Credit Card  \n",
+       "4         3       43.78   131.34                 0      Gift Card  \n",
+       "5         4      141.41   565.64                 5    Credit Card  \n",
+       "6         2      127.43   254.86                 0     Debit Card  \n",
+       "7         1      130.31   130.31                20     Debit Card  \n",
+       "8         2       53.49   106.98                 0    Credit Card  \n",
+       "9         2       54.20   108.40                 0         PayPal  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "\n",
+    "# Set random seed for reproducible results\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Generate 500 orders from 2023\n",
+    "n_orders = 500\n",
+    "\n",
+    "# Create order IDs and customer data\n",
+    "order_id = np.arange(1, n_orders + 1)\n",
+    "customer_id = np.random.randint(1, 101, size=n_orders)\n",
+    "customer_segment = np.random.choice([\"Student\", \"Professional\", \"Senior\"], size=n_orders, p=[0.4, 0.4, 0.2])\n",
+    "region = np.random.choice([\"North\", \"South\", \"East\", \"West\"], size=n_orders)\n",
+    "order_date = pd.date_range(start=\"2023-01-01\", periods=n_orders, freq=\"D\")\n",
+    "product_category = np.random.choice([\"Electronics\", \"Clothing\", \"Books\", \"Home\", \"Sports\"], size=n_orders)\n",
+    "quantity = np.random.randint(1, 5, size=n_orders)\n",
+    "unit_price = np.round(np.random.uniform(10, 200, size=n_orders), 2)\n",
+    "revenue = quantity * unit_price\n",
+    "discount_applied = np.random.choice([0, 5, 10, 15, 20, 25, 30], size=n_orders, p=[0.5,0.1,0.1,0.1,0.05,0.05,0.1])\n",
+    "payment_method = np.random.choice([\"Credit Card\", \"PayPal\", \"Gift Card\", \"Debit Card\"], size=n_orders)\n",
+    "\n",
+    "# Build dataframe\n",
+    "df = pd.DataFrame({\n",
+    "    \"order_id\": order_id,\n",
+    "    \"customer_id\": customer_id,\n",
+    "    \"customer_segment\": customer_segment,\n",
+    "    \"region\": region,\n",
+    "    \"order_date\": order_date,\n",
+    "    \"product_category\": product_category,\n",
+    "    \"quantity\": quantity,\n",
+    "    \"unit_price\": unit_price,\n",
+    "    \"revenue\": revenue,\n",
+    "    \"discount_applied\": discount_applied,\n",
+    "    \"payment_method\": payment_method\n",
+    "})\n",
+    "\n",
+    "# Save dataset for reference\n",
+    "df.to_csv(\"ecommerce_data.csv\", index=False)\n",
+    "print(\"✅ Dataset created successfully!\")\n",
+    "\n",
+    "# Display first few rows\n",
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbf765c7",
+   "metadata": {},
+   "source": [
+    "## Data Exploration\n",
+    "\n",
+    "Let's start by understanding our dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9ab80b5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset Info:\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 500 entries, 0 to 499\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   order_id          500 non-null    int64  \n",
+      " 1   customer_id       500 non-null    int64  \n",
+      " 2   customer_segment  500 non-null    object \n",
+      " 3   region            500 non-null    object \n",
+      " 4   order_date        500 non-null    object \n",
+      " 5   product_category  500 non-null    object \n",
+      " 6   quantity          500 non-null    int64  \n",
+      " 7   unit_price        500 non-null    float64\n",
+      " 8   revenue           500 non-null    float64\n",
+      " 9   discount_applied  500 non-null    int64  \n",
+      " 10  payment_method    500 non-null    object \n",
+      "dtypes: float64(2), int64(4), object(5)\n",
+      "memory usage: 43.1+ KB\n",
+      "None\n",
+      "\n",
+      "Dataset Shape: (500, 11)\n",
+      "\n",
+      "First few rows:\n"
+     ]
+    },
+    {
+     "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>customer_id</th>\n",
+       "      <th>customer_segment</th>\n",
+       "      <th>region</th>\n",
+       "      <th>order_date</th>\n",
+       "      <th>product_category</th>\n",
+       "      <th>quantity</th>\n",
+       "      <th>unit_price</th>\n",
+       "      <th>revenue</th>\n",
+       "      <th>discount_applied</th>\n",
+       "      <th>payment_method</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>52</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>North</td>\n",
+       "      <td>2023-01-01</td>\n",
+       "      <td>Clothing</td>\n",
+       "      <td>1</td>\n",
+       "      <td>42.43</td>\n",
+       "      <td>42.43</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Debit Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>93</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>West</td>\n",
+       "      <td>2023-01-02</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>1</td>\n",
+       "      <td>24.18</td>\n",
+       "      <td>24.18</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Gift Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>15</td>\n",
+       "      <td>Professional</td>\n",
+       "      <td>East</td>\n",
+       "      <td>2023-01-03</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>3</td>\n",
+       "      <td>158.71</td>\n",
+       "      <td>476.13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Debit Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>72</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>East</td>\n",
+       "      <td>2023-01-04</td>\n",
+       "      <td>Sports</td>\n",
+       "      <td>1</td>\n",
+       "      <td>60.42</td>\n",
+       "      <td>60.42</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Credit Card</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>61</td>\n",
+       "      <td>Student</td>\n",
+       "      <td>West</td>\n",
+       "      <td>2023-01-05</td>\n",
+       "      <td>Books</td>\n",
+       "      <td>3</td>\n",
+       "      <td>43.78</td>\n",
+       "      <td>131.34</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Gift Card</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   order_id  customer_id customer_segment region  order_date product_category  \\\n",
+       "0         1           52          Student  North  2023-01-01         Clothing   \n",
+       "1         2           93          Student   West  2023-01-02           Sports   \n",
+       "2         3           15     Professional   East  2023-01-03           Sports   \n",
+       "3         4           72          Student   East  2023-01-04           Sports   \n",
+       "4         5           61          Student   West  2023-01-05            Books   \n",
+       "\n",
+       "   quantity  unit_price  revenue  discount_applied payment_method  \n",
+       "0         1       42.43    42.43                 0     Debit Card  \n",
+       "1         1       24.18    24.18                 0      Gift Card  \n",
+       "2         3      158.71   476.13                 0     Debit Card  \n",
+       "3         1       60.42    60.42                 0    Credit Card  \n",
+       "4         3       43.78   131.34                 0      Gift Card  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the data\n",
+    "data = pd.read_csv(\"ecommerce_data.csv\")\n",
+    "\n",
+    "# Basic information about the dataset\n",
+    "print(\"Dataset Info:\")\n",
+    "print(data.info())\n",
+    "print(\"\\nDataset Shape:\", data.shape)\n",
+    "print(\"\\nFirst few rows:\")\n",
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "45510b1d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Monthly Revenue:\n",
+      "month\n",
+      "2023-01    6626.66\n",
+      "2023-02    6796.03\n",
+      "2023-03    8960.10\n",
+      "2023-04    7713.47\n",
+      "2023-05    7266.80\n",
+      "2023-06    7982.47\n",
+      "2023-07    7981.65\n",
+      "2023-08    6664.64\n",
+      "2023-09    9708.06\n",
+      "2023-10    7287.27\n",
+      "2023-11    8795.85\n",
+      "2023-12    8119.44\n",
+      "2024-01    7268.59\n",
+      "2024-02    8388.91\n",
+      "2024-03    6835.73\n",
+      "2024-04    7430.29\n",
+      "2024-05    4314.71\n",
+      "Freq: M, Name: revenue, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Convert order_date to datetime and add helpful columns\n",
+    "data[\"order_date\"] = pd.to_datetime(data[\"order_date\"])\n",
+    "data[\"month\"] = data[\"order_date\"].dt.to_period(\"M\")\n",
+    "data[\"day_of_week\"] = data[\"order_date\"].dt.day_name()\n",
+    "\n",
+    "# Example: Revenue by month\n",
+    "monthly_revenue = data.groupby(\"month\")[\"revenue\"].sum()\n",
+    "print(\"Monthly Revenue:\")\n",
+    "print(monthly_revenue)\n",
+    "\n",
+    "# Plot monthly revenue\n",
+    "monthly_revenue.plot(kind=\"bar\", figsize=(10,6), title=\"Monthly Revenue\")\n",
+    "plt.ylabel(\"Revenue ($)\")\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62a50bf3",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Business Questions\n",
+    "\n",
+    "Now answer each of the following business questions. Use the starter code above as a reference, and expand your analysis to solve all questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb669ad",
+   "metadata": {},
+   "source": [
+    "### 1. Revenue Overview\n",
+    "What was the company's total revenue last quarter? Break it down by month.\n",
+    "\n",
+    "Q1 revenue was $22,493, average about $7,498 per month. January came in at $7268.59, February is the peak at $8,388, up about 15 percent from January. March came in at $6,836, down about 18 percent from February and about 6 percent below January.\n",
+    "\n",
+    "February pops up and going into March it gives some of it back. I would check March for lighter promos/discounts or a campaign gap, then repeat what worked in February."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "12d89265",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Last Quarter Report:\n",
+      "month\n",
+      "2024-01    7268.59\n",
+      "2024-02    8388.91\n",
+      "2024-03    6835.73\n",
+      "Freq: M, Name: revenue, dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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E6KmnnrK2hYWF6cILL/TYV+PGja1QX6x27dravHlzmT8XAFUfYQtAtZadna3+/fsrKytLX3/9tRo2bOixvUGDBpKOf4bTyf744w/VqVOn1FmvgoICXXvttdq8ebOWLVtW6oNV33rrLe3cuVNz5swpddOJ3Nxc/fLLL4qMjCz3tu0xMTGSpM2bNys2NrbMMcV/nBW/Cn/yH3DFyrqpwD/+8Q/NnTtXY8aMUVxcnMLDw2UYhgYNGlTqDItUdtgsj9vtlmEYWrJkSZlnw06+wciZ7PtscDqdHqEhISFBbdq00Z133qnPPvtMkqyfwdtvv+1xg4NiJ94QomHDhurZs6c++OADPfjgg1q/fr3S0tI0ffp0a0zx/p588slyf58n/1zKO5NYHIwr+vsu/t633HJLuWHvggsuKLN+Opdeeqkkac2aNR5hKzAw0OPsUvE8IiMjy729fvH7/bKystS7d2+FhYVp8uTJatGihYKCgrRp0ybdf//9pZ6fDRo0UIMGDbR48WJ99913Zb738mxr2LChoqOjtWbNGjVv3lymaSouLk4RERH6v//7P/3666/6+uuv1a1bt1I/h9P9XgFUP4QtANXWsWPHdOWVV+rHH3/UF198YYWYEzVq1EgRERFlfjBxSkpKqT+O3W63brvtNq1cuVIffPCBevfuXepxaWlpKiwsVPfu3Utte+utt/TWW2/p448/1tVXX13mvPv37y+n06m333673JtkvPXWWwoICLAuVSs+83HyDSjKulPef/7zHw0ZMkQzZ860aseOHTujDygu74/9Fi1ayDRNRUdHq1WrVhXen7c0aNBAY8eO1aRJk7R+/Xp17drVOgsSGRlZ7mennejGG2/U3XffrZ07d+r9999XSEiIrrzySmt78f7CwsIqtL+KqOjvu/gOfC6X66x972LFd/s7fPjwace2aNFCX3zxhbp3737KgP3VV1/pzz//1EcffaRevXpZ9T179pQ5PigoSAsXLlSfPn10+eWXa/Xq1WrXrp21vVmzZnK73dq9e7fHWaviSwCLRUREKCQkpFRdOn4JssPhUJMmTaxaz549tWbNGkVHR1sfcn7hhRcqPDxcS5cu1aZNmyp9QxMA1Qvv2QJQLblcLt14441KTk7WggULFBcXV+7YgQMHauHChR63Jl+5cqV+/PFHXX/99R5j//GPf+j999/XSy+9pGuvvbbM/Q0aNEgff/xxqX+SNGDAAH388ce6+OKLy51P48aNNWLECH3xxRd6+eWXS22fPXu2vvzyS915552qW7eupON/yNerV8/jLmmS9NJLL5V6vNPpLPVK+vPPP1+hW2sXq1GjhqTSf+xfe+21cjqdmjRpUqnvYZqm/vzzzwp/j3PlH//4h0JCQjRt2jRJx892hYWF6YknnvC4S16xAwcOeHw9cOBAOZ1O/fvf/9aCBQt0xRVXWD8f6fhHBrRo0UJPPfVUmcHk5P1VREV/306nUwMHDtSHH36o//73v2flexcrvhzz5EvlynLDDTfI5XLpscceK7WtqKjIeh4Vn/k58blTUFBQ5vO4WHh4uJYtW6bIyEhddtllHu+f69+/vyR53KpdUqn3UjqdTvXr10+ffvqpx9nojIwMzZ8/Xz169PC4fLNnz5765Zdf9P7771uXFTocDnXr1k1PP/20CgsLS71fC4Bv4swWgGrp3nvv1WeffaYrr7xSBw8e1DvvvOOx/cQ3qT/44INasGCB4uPj9X//9386fPiwnnzySbVv397j5g3PPvusXnrpJcXFxSkkJKTUPq+55hrVqFFDbdq0UZs2bcqcV3R0dLlntE709NNPa8eOHbr77ru1dOlSXX755ZKkZcuW6dNPP1WfPn305JNPejzm9ttv17Rp03T77berc+fOWrNmjX788cdS+77iiiv09ttvKzw8XDExMUpOTtYXX3xhBbeK6NSpkyTpoYce0qBBg+Tv768rr7xSLVq00JQpUzRhwgT98ssvuvrqqxUaGqo9e/bo448/1siRI8/oM8dONmXKFEmyPu/q7bff1tq1ayVJDz/8cKX2WbduXQ0bNkwvvfSStm/frrZt2+rll1/Wrbfeqo4dO2rQoEGKiIhQWlqaFi1apO7du+uFF16wHh8ZGan4+Hg9/fTTys3N1Y033uixf4fDoddee039+/dXu3btNGzYMDVq1Ei///67Vq1apbCwMCu4nImK/r6nTZumVatW6eKLL9Ydd9yhmJgYHTx4UJs2bdIXX3yhgwcPnvZ7/f7779bzvaCgQD/88IPmzJmjevXqeVxCWJ7evXvrzjvv1NSpU5Wamqp+/frJ399fu3bt0oIFCzRr1ixdd9116tatm2rXrq0hQ4bonnvukWEYevvtt097mV29evW0YsUK9ejRQ3379tXatWvVqFEjxcbG6qabbtJLL72k7OxsdevWTStXrtRPP/1Uah9Tpkyx9nH33XfLz89Pc+bMUX5+fqnPwioOUjt37tQTTzxh1Xv16qUlS5YoMDCwUu/XBFANeeEOiABgu+LbLJf372T//e9/zX79+pkhISFmrVq1zMGDB5vp6ekeY4pva13ev5Nvg34yVfDW78UKCgrMZ5991uzUqZMZEhJifZ8hQ4Z43N66WF5enjlixAgzPDzcDA0NNW+44QZz//79pW5xfejQIXPYsGFmvXr1zJo1a5oJCQnmjh07zGbNmplDhgyxxhXf+n3Dhg1lzu+xxx4zGzVqZDocjlL9f/jhh2aPHj3MGjVqmDVq1DDbtGljJiUlmTt37rTGnHjr7oo6k9/pyYpv/V6W3bt3m06n06P/VatWmQkJCWZ4eLgZFBRktmjRwhw6dKj53XfflXr8q6++akoyQ0NDzaNHj5b5Pb7//nvz2muvNevWrWsGBgaazZo1M2+44QZz5cqV1pjiW78fOHDA47HFv4sTf8YV/X2bpmlmZGSYSUlJZpMmTUx/f3+zfv365qWXXmq+8sorp/mplb71u8PhMCMjI82bbrrJ/OmnnzzGnupnbJqm+corr5idOnUyg4ODzdDQULN9+/bm+PHjzX379lljvvnmG7Nr165mcHCw2bBhQ3P8+PHmsmXLSt3qv6znz08//WQ2aNDAbNu2rfUzPHr0qHnPPfeYdevWNWvUqGFeeeWV5t69e8v8OW3atMlMSEgwa9asaYaEhJjx8fHmunXryuwlMjLSlGRmZGRYtbVr15qSzJ49e5YaX97zfciQIWazZs3K/ZkBqNoM0+RdmQBQFeTk5Kh3797avXu31qxZU+7NFgAAwF8DYQsAqpD09HR169ZNx44dU3Jyspo1a+btKQEAgHIQtgAAAADABtyNEAAAAABsQNgCAAAAABsQtgAAAADABoQtAAAAALABH2pcAW63W/v27VNoaKgMw/D2dAAAAAB4iWmays3NVcOGDeVwnPrcFWGrAvbt26cmTZp4exoAAAAA/iL27t2rxo0bn3IMYasCQkNDJR3/gYaFhXl5NgAAAAC8JScnR02aNLEywqkQtiqg+NLBsLAwwhYAAACACr29iBtkAAAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2MDP2xMAAFQdzR9Y5O0pwMt+mZbo7SkAQJXBmS0AAAAAsAFhCwAAAABsQNgCAAAAABsQtgAAAADABoQtAAAAALABYQsAAAAAbEDYAgAAAAAbELYAAAAAwAaELQAAAACwAWELAAAAAGxA2AIAAAAAGxC2AAAAAMAGhC0AAAAAsAFhCwAAAABsQNgCAAAAABt4NWy5XC498sgjio6OVnBwsFq0aKHHHntMpmlaY0zT1MSJE9WgQQMFBwerb9++2rVrl8d+Dh48qMGDByssLEy1atXSiBEjdPjwYY8xmzdvVs+ePRUUFKQmTZpoxowZ56RHAAAAAL7Jq2Fr+vTpevnll/XCCy9o+/btmj59umbMmKHnn3/eGjNjxgw999xzmj17tr799lvVqFFDCQkJOnbsmDVm8ODB2rp1q1asWKGFCxdqzZo1GjlypLU9JydH/fr1U7NmzbRx40Y9+eSTevTRR/XKK6+c034BAAAA+A7DPPE00jl2xRVXKCoqSq+//rpVGzhwoIKDg/XOO+/INE01bNhQ9957r/75z39KkrKzsxUVFaV58+Zp0KBB2r59u2JiYrRhwwZ17txZkrR06VINGDBAv/32mxo2bKiXX35ZDz30kNLT0xUQECBJeuCBB/TJJ59ox44dp51nTk6OwsPDlZ2drbCwMBt+EgBQNTR/YJG3pwAv+2VaorenAABedSbZwKtntrp166aVK1fqxx9/lCT98MMPWrt2rfr37y9J2rNnj9LT09W3b1/rMeHh4br44ouVnJwsSUpOTlatWrWsoCVJffv2lcPh0LfffmuN6dWrlxW0JCkhIUE7d+7UoUOHbO8TAAAAgO/x8+Y3f+CBB5STk6M2bdrI6XTK5XLp8ccf1+DBgyVJ6enpkqSoqCiPx0VFRVnb0tPTFRkZ6bHdz89PderU8RgTHR1dah/F22rXru2xLT8/X/n5+dbXOTk5kqTCwkIVFhZKkhwOhzVnt9ttjS2uFxUVebz3zOl0yuFwlFsv3u+JPUhSUVFRher+/v5yu91yuVxWzTAM+fn5lVsvb+70RE/0RE/l9SRJ/g5TDqNk30VuyWUaCnCYMipQL3RJbhkKdHpeWFHgkkxJgU6PsvJdkiEpoFTdkEOm/E+om6ZU4DbkNEz5OU5fd5tSodugpzPoqfj5yXqiJ3qiJ1/t6eTtp+LVsPXBBx/o3Xff1fz589WuXTulpqZqzJgxatiwoYYMGeK1eU2dOlWTJk0qVV++fLlCQkIkSU2bNlWHDh20efNmpaWlWWNat26tNm3aKCUlRQcOHLDqsbGxatasmdasWaPc3FyrHhcXp8jISC1fvtzjyRcfH6/g4GAtXrzYYw4DBgzQ0aNHtWrVKqvm5+enxMREZWZmWmf8JCk0NFR9+vTR3r17lZqaatUjIiLUrVs37dq1Szt37rTq9ERP9ERPp+tJkgY2dysuquR/Tkv2Glr6m1PDW7vVtlZJ/d+7HVq/39C49i41CCmZ48vbHNqRbWhyR5eCTvi/0NRUpw4VSDO6lPyPVZLGpzhVO0CaEFtSP1Yk3b/BT63CTY2KKfkf6x950rQf/HRRhKmbWpTUt2cZmr3dqcsaudW/SckckzMMvfezk57OoKfc3FzWEz3REz35dE95eXmqKK++Z6tJkyZ64IEHlJSUZNWmTJmid955Rzt27NDPP/+sFi1a6Pvvv1dsbKw1pnfv3oqNjdWsWbP0xhtv6N577/W4HLCoqEhBQUFasGCBrrnmGt12223KycnRJ598Yo1ZtWqV+vTpo4MHD1bozFaTJk2UmZlpXZfJqwL0RE/05Is9tXhoKWeBfLynHVOOv2eL9URP9ERPvtpTTk6O6tWrV6H3bHn1zFZeXp51WUoxp9Np/ZCio6NVv359rVy50gpbOTk5+vbbbzVq1ChJxxNoVlaWNm7cqE6dOkmSvvzyS7ndbl188cXWmIceekiFhYXy9/eXJK1YsUKtW7cuFbQkKTAwUIGBgaXq/v7+1uNPnK/T6Sw1tviXUtH6yfutTN3hcJT6eZ6qXt7c6YmezrROT77VU6HbKFWTjv/hfyb1fFd59dI1s5y6W0aZdZdpyHUGdXqqeE/G/09krCd6oid6Km+OZ1qvaj2Vt70sXr1BxpVXXqnHH39cixYt0i+//KKPP/5YTz/9tK655hpJxw/oY8aM0ZQpU/TZZ59py5Ytuu2229SwYUNdffXVkqS2bdvq8ssv1x133KGUlBR98803Gj16tAYNGqSGDRtKkm6++WYFBARoxIgR2rp1q95//33NmjVL48aN81brAAAAAKo5r57Zev755/XII4/o7rvv1v79+9WwYUPdeeedmjhxojVm/PjxOnLkiEaOHKmsrCz16NFDS5cuVVBQkDXm3Xff1ejRo3XppZfK4XBo4MCBeu6556zt4eHhWr58uZKSktSpUyfVq1dPEydO9PgsLgAAAAA4m7z6nq2qgs/ZAoDj+Jwt8DlbAHxdlfmcLQAAAACorghbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANvDz9gRQdTR/YJG3pwAv+2VaorenAAAAUGVwZgsAAAAAbEDYAgAAAAAbELYAAAAAwAaELQAAAACwAWELAAAAAGxA2AIAAAAAGxC2AAAAAMAGhC0AAAAAsAFhCwAAAABs4NWw1bx5cxmGUepfUlKSJOnYsWNKSkpS3bp1VbNmTQ0cOFAZGRke+0hLS1NiYqJCQkIUGRmp++67T0VFRR5jvvrqK3Xs2FGBgYFq2bKl5s2bd65aBAAAAOCjvBq2NmzYoD/++MP6t2LFCknS9ddfL0kaO3asPv/8cy1YsECrV6/Wvn37dO2111qPd7lcSkxMVEFBgdatW6c333xT8+bN08SJE60xe/bsUWJiouLj45WamqoxY8bo9ttv17Jly85tswAAAAB8imGapuntSRQbM2aMFi5cqF27diknJ0cRERGaP3++rrvuOknSjh071LZtWyUnJ6tr165asmSJrrjiCu3bt09RUVGSpNmzZ+v+++/XgQMHFBAQoPvvv1+LFi3Sf//7X+v7DBo0SFlZWVq6dGmF5pWTk6Pw8HBlZ2crLCzs7DdeRTR/YJG3pwAv+2VaorenAC/jOACOAwB83ZlkA79zNKfTKigo0DvvvKNx48bJMAxt3LhRhYWF6tu3rzWmTZs2atq0qRW2kpOT1b59eytoSVJCQoJGjRqlrVu3qkOHDkpOTvbYR/GYMWPGlDuX/Px85efnW1/n5ORIkgoLC1VYWChJcjgccjqdcrlccrvd1tjielFRkU7MsU6nUw6Ho9x68X6L+fkd/9WcfElkeXV/f3+53W65XC6rZhiG/Pz8yq2XN/fy6gEOU4ZR8j2L3JLLNErVC12SW4YCnZ45vsAlmZICnR5l5bskQ1JAqbohh0z5n1A3TanAbchpmPJznL7uNqVCtyF/hylHBeZOT6fuyTRNrzz3quN6qqo9SWI9+XhPxc9P1hM90RM9+WpPJ28/lb9M2Prkk0+UlZWloUOHSpLS09MVEBCgWrVqeYyLiopSenq6NebEoFW8vXjbqcbk5OTo6NGjCg4OLjWXqVOnatKkSaXqy5cvV0hIiCSpadOm6tChgzZv3qy0tDRrTOvWrdWmTRulpKTowIEDVj02NlbNmjXTmjVrlJuba9Xj4uIUGRmp5cuXezz54uPjFRwcrMWLF3vMYcCAATp69KhWrVpl1fz8/JSYmKjMzEwlJydb9dDQUPXp00d79+5VamqqVY+IiFC3bt20a9cu7dy506qfrqfhrd1qW6vkCfnv3Q6t329oXHuXGoSUzPHlbQ7tyDY0uaNLQSc8w6amOnWoQJrRpWTRSNL4FKdqB0gTYkvqx4qk+zf4qVW4qVExJYvmjzxp2g9+uijC1E0tSurbswzN3u7UZY3c6t+kZI7JGYbe+9mpgc3diosqqS/Za2jpb056OsOecnNzvfLcq47rqar2JIn15OM95ebmsp7oiZ7oyad7ysvLU0X9ZS4jTEhIUEBAgD7//HNJ0vz58zVs2DCPM0yS1KVLF8XHx2v69OkaOXKkfv31V4/3X+Xl5alGjRpavHix+vfvr1atWmnYsGGaMGGCNWbx4sVKTExUXl5emWGrrDNbTZo0UWZmpnWq0BdfFWj14EJe4fXxnvZMTawWr0idrk5P5ffU4qGlrCcf72nHlOOXEbKe6Ime6MlXe8rJyVG9evWqzmWEv/76q7744gt99NFHVq1+/foqKChQVlaWx9mtjIwM1a9f3xqTkpLisa/iuxWeOObkOxhmZGQoLCyszKAlSYGBgQoMDCxV9/f3l7+/v0fN6XTK6XSWGlv8S6lo/eT9VqbucDisy3wqUi9v7uXVC9xGqdqp6vmu8uqla2Y5dbeMMusu05DrDOqFZzh3eip77oZheOW5Vx3XU1XuifXk2z0Z/z+RsZ7oiZ7oqbw5nmm9qvVU3vay/CU+Z2vu3LmKjIxUYmLJm247deokf39/rVy50qrt3LlTaWlpiouLk3T8VN+WLVu0f/9+a8yKFSsUFhammJgYa8yJ+ygeU7wPAAAAALCD18OW2+3W3LlzNWTIEI90GR4erhEjRmjcuHFatWqVNm7cqGHDhikuLk5du3aVJPXr108xMTG69dZb9cMPP2jZsmV6+OGHlZSUZJ2Zuuuuu/Tzzz9r/Pjx2rFjh1566SV98MEHGjt2rFf6BQAAAOAbvH4Z4RdffKG0tDQNHz681LZnnnlGDodDAwcOVH5+vhISEvTSSy9Z251OpxYuXKhRo0YpLi5ONWrU0JAhQzR58mRrTHR0tBYtWqSxY8dq1qxZaty4sV577TUlJCSck/4AAAAA+Ka/zA0y/sr4nK3j+Hwd8Pk64DgAjgMAfN2ZZAOvX0YIAAAAANURYQsAAAAAbOD192wBAACgauGSYnBJccVwZgsAAAAAbEDYAgAAAAAbELYAAAAAwAaELQAAAACwAWELAAAAAGxA2AIAAAAAGxC2AAAAAMAGhC0AAAAAsAFhCwAAAABsQNgCAAAAABsQtgAAAADABoQtAAAAALABYQsAAAAAbEDYAgAAAAAbELYAAAAAwAaELQAAAACwAWELAAAAAGxA2AIAAAAAGxC2AAAAAMAGhC0AAAAAsAFhCwAAAABsQNgCAAAAABsQtgAAAADABoQtAAAAALABYQsAAAAAbEDYAgAAAAAbELYAAAAAwAaELQAAAACwAWELAAAAAGxA2AIAAAAAGxC2AAAAAMAGhC0AAAAAsAFhCwAAAABsQNgCAAAAABsQtgAAAADABoQtAAAAALABYQsAAAAAbEDYAgAAAAAbeD1s/f7777rllltUt25dBQcHq3379vruu++s7aZpauLEiWrQoIGCg4PVt29f7dq1y2MfBw8e1ODBgxUWFqZatWppxIgROnz4sMeYzZs3q2fPngoKClKTJk00Y8aMc9IfAAAAAN/k1bB16NAhde/eXf7+/lqyZIm2bdummTNnqnbt2taYGTNm6LnnntPs2bP17bffqkaNGkpISNCxY8esMYMHD9bWrVu1YsUKLVy4UGvWrNHIkSOt7Tk5OerXr5+aNWumjRs36sknn9Sjjz6qV1555Zz2CwAAAMB3+Hnzm0+fPl1NmjTR3LlzrVp0dLT136Zp6tlnn9XDDz+sv//975Kkt956S1FRUfrkk080aNAgbd++XUuXLtWGDRvUuXNnSdLzzz+vAQMG6KmnnlLDhg317rvvqqCgQG+88YYCAgLUrl07paam6umnn/YIZQAAAABwtng1bH322WdKSEjQ9ddfr9WrV6tRo0a6++67dccdd0iS9uzZo/T0dPXt29d6THh4uC6++GIlJydr0KBBSk5OVq1ataygJUl9+/aVw+HQt99+q2uuuUbJycnq1auXAgICrDEJCQmaPn26Dh065HEmTZLy8/OVn59vfZ2TkyNJKiwsVGFhoSTJ4XDI6XTK5XLJ7XZbY4vrRUVFMk3TqjudTjkcjnLrxfst5ud3/FdTVFRUobq/v7/cbrdcLpdVMwxDfn5+5dbLm3t59QCHKcMo+Z5FbsllGqXqhS7JLUOBzpI+JanAJZmSAp0eZeW7JENSQKm6IYdM+Z9QN02pwG3IaZjyc5y+7jalQrchf4cpRwXmTk+n7sk0Ta8896rjeqqqPUliPfl4T8XPT9aTb/ckifXk4z253W6fXU8nbz8Vr4atn3/+WS+//LLGjRunBx98UBs2bNA999yjgIAADRkyROnp6ZKkqKgoj8dFRUVZ29LT0xUZGemx3c/PT3Xq1PEYc+IZsxP3mZ6eXipsTZ06VZMmTSo13+XLlyskJESS1LRpU3Xo0EGbN29WWlqaNaZ169Zq06aNUlJSdODAAaseGxurZs2aac2aNcrNzbXqcXFxioyM1PLlyz2efPHx8QoODtbixYs95jBgwAAdPXpUq1at8ug3MTFRmZmZSk5OtuqhoaHq06eP9u7dq9TUVKseERGhbt26adeuXdq5c6dVP11Pw1u71bZWyRPy37sdWr/f0Lj2LjUIKZnjy9sc2pFtaHJHl4JOeIZNTXXqUIE0o0vJopGk8SlO1Q6QJsSW1I8VSfdv8FOrcFOjYkoWzR950rQf/HRRhKmbWpTUt2cZmr3dqcsaudW/SckckzMMvfezUwObuxUXVVJfstfQ0t+c9HSGPeXm5nrluVcd11NV7UkS68nHe8rNzWU90ZMksZ58vKfMzEyfXU95eXmqKMM8Mc6dYwEBAercubPWrVtn1e655x5t2LBBycnJWrdunbp37659+/apQYMG1pgbbrhBhmHo/fff1xNPPKE333zT4wcuSZGRkZo0aZJGjRqlfv36KTo6WnPmzLG2b9u2Te3atdO2bdvUtm1bj8eWdWarSZMmyszMVFhYmKSq/4pUZV4VaPXgQp999Yaejve0Z2pitXhF6nR1eiq/pxYPLWU9+XhPO6YkHn8c68mne2r58DLWk4/3tP2xAT67nnJyclSvXj1lZ2db2aA8Xj2z1aBBA8XExHjU2rZtqw8//FCSVL9+fUlSRkaGR9jKyMhQbGysNWb//v0e+ygqKtLBgwetx9evX18ZGRkeY4q/Lh5zosDAQAUGBpaq+/v7y9/f36PmdDrldDpLjS3+pVS0fvJ+K1N3OBzWZT4VqZc39/LqBW6jVO1U9XxXefXSNbOcultGmXWXach1BvXCM5w7PZU9d8MwvPLcq47rqSr3xHry7Z6KLyFjPdET68m3e7IuLffB9VTe9rJ49W6E3bt3L3VG6scff1SzZs0kHb9ZRv369bVy5Upre05Ojr799lvFxcVJOn66LysrSxs3brTGfPnll3K73br44outMWvWrPFIqStWrFDr1q1LXUIIAAAAAGeDV8PW2LFjtX79ej3xxBP66aefNH/+fL3yyitKSkqSdPzVszFjxmjKlCn67LPPtGXLFt12221q2LChrr76aknHz4RdfvnluuOOO5SSkqJvvvlGo0eP1qBBg9SwYUNJ0s0336yAgACNGDFCW7du1fvvv69Zs2Zp3Lhx3modAAAAQDXn1csIL7roIn388ceaMGGCJk+erOjoaD377LMaPHiwNWb8+PE6cuSIRo4cqaysLPXo0UNLly5VUFCQNebdd9/V6NGjdemll8rhcGjgwIF67rnnrO3h4eFavny5kpKS1KlTJ9WrV08TJ07ktu8AAAAAbOPVG2RUFTk5OQoPD6/Qm+Cqs+YPLPL2FOBlv0xL9PYU4GUcB8BxABLHAvj2seBMsoFXLyMEAAAAgOqKsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADbwath69NFHZRiGx782bdpY248dO6akpCTVrVtXNWvW1MCBA5WRkeGxj7S0NCUmJiokJESRkZG67777VFRU5DHmq6++UseOHRUYGKiWLVtq3rx556I9AAAAAD7M62e22rVrpz/++MP6t3btWmvb2LFj9fnnn2vBggVavXq19u3bp2uvvdba7nK5lJiYqIKCAq1bt05vvvmm5s2bp4kTJ1pj9uzZo8TERMXHxys1NVVjxozR7bffrmXLlp3TPgEAAAD4Fj+vT8DPT/Xr1y9Vz87O1uuvv6758+erT58+kqS5c+eqbdu2Wr9+vbp27arly5dr27Zt+uKLLxQVFaXY2Fg99thjuv/++/Xoo48qICBAs2fPVnR0tGbOnClJatu2rdauXatnnnlGCQkJ57RXAAAAAL7D62Fr165datiwoYKCghQXF6epU6eqadOm2rhxowoLC9W3b19rbJs2bdS0aVMlJyera9euSk5OVvv27RUVFWWNSUhI0KhRo7R161Z16NBBycnJHvsoHjNmzJhy55Sfn6/8/Hzr65ycHElSYWGhCgsLJUkOh0NOp1Mul0tut9saW1wvKiqSaZpW3el0yuFwlFsv3m8xP7/jv5qTL4ksr+7v7y+32y2Xy2XVDMOQn59fufXy5l5ePcBhyjBKvmeRW3KZRql6oUtyy1Cgs6RPSSpwSaakQKdHWfkuyZAUUKpuyCFT/ifUTVMqcBtyGqb8HKevu02p0G3I32HKUYG509OpezJN0yvPveq4nqpqT5JYTz7eU/Hzk/Xk2z1JYj35eE9ut9tn19PJ20/Fq2Hr4osv1rx589S6dWv98ccfmjRpknr27Kn//ve/Sk9PV0BAgGrVquXxmKioKKWnp0uS0tPTPYJW8fbibacak5OTo6NHjyo4OLjUvKZOnapJkyaVqi9fvlwhISGSpKZNm6pDhw7avHmz0tLSrDGtW7dWmzZtlJKSogMHDlj12NhYNWvWTGvWrFFubq5Vj4uLU2RkpJYvX+7x5IuPj1dwcLAWL17sMYcBAwbo6NGjWrVqlVXz8/NTYmKiMjMzlZycbNVDQ0PVp08f7d27V6mpqVY9IiJC3bp1065du7Rz506rfrqehrd2q22tkifkv3c7tH6/oXHtXWoQUjLHl7c5tCPb0OSOLgWd8AybmurUoQJpRpeSRSNJ41Ocqh0gTYgtqR8rku7f4KdW4aZGxZQsmj/ypGk/+OmiCFM3tSipb88yNHu7U5c1cqt/k5I5JmcYeu9npwY2dysuqqS+ZK+hpb856ekMe8rNzfXKc686rqeq2pMk1pOP95Sbm8t6oidJYj35eE+ZmZk+u57y8vJUUYZ5YpzzsqysLDVr1kxPP/20goODNWzYMI8zTJLUpUsXxcfHa/r06Ro5cqR+/fVXj/df5eXlqUaNGlq8eLH69++vVq1aadiwYZowYYI1ZvHixUpMTFReXl6ZYausM1tNmjRRZmamwsLCJFX9V6Qq86pAqwcX+uyrN/R0vKc9UxOrxStSp6vTU/k9tXhoKevJx3vaMSXx+ONYTz7dU8uHl7GefLyn7Y8N8Nn1lJOTo3r16ik7O9vKBuXx+mWEJ6pVq5ZatWqln376SZdddpkKCgqUlZXlcXYrIyPDeo9X/fr1lZKS4rGP4rsVnjjm5DsYZmRkKCwsrMygJUmBgYEKDAwsVff395e/v79Hzel0yul0lhpb/EupaP3k/Vam7nA4rMt8KlIvb+7l1QvcRqnaqer5rvLqpWtmOXW3jDLrLtOQ6wzqhWc4d3oqe+6GYXjluVcd11NV7on15Ns9FV9CxnqiJ9aTb/dkXVrug+upvO1l8frdCE90+PBh7d69Ww0aNFCnTp3k7++vlStXWtt37typtLQ0xcXFSTp+qm/Lli3av3+/NWbFihUKCwtTTEyMNebEfRSPKd4HAAAAANih0mErKytLr732miZMmKCDBw9KkjZt2qTff/+9wvv45z//qdWrV+uXX37RunXrdM0118jpdOqmm25SeHi4RowYoXHjxmnVqlXauHGjhg0bpri4OHXt2lWS1K9fP8XExOjWW2/VDz/8oGXLlunhhx9WUlKSdWbqrrvu0s8//6zx48drx44deumll/TBBx9o7NixlW0dAAAAAE6rUpcRbt68WX379lV4eLh++eUX3XHHHapTp44++ugjpaWl6a233qrQfn777TfddNNN+vPPPxUREaEePXpo/fr11hsvn3nmGTkcDg0cOFD5+flKSEjQSy+9ZD3e6XRq4cKFGjVqlOLi4lSjRg0NGTJEkydPtsZER0dr0aJFGjt2rGbNmqXGjRvrtdde47bvAAAAAGxVqbA1btw4DR06VDNmzFBoaKhVHzBggG6++eYK7+e999475fagoCC9+OKLevHFF8sd06xZs1J3OznZJZdcou+//77C8wIAAACA/1WlLiPcsGGD7rzzzlL1Ro0aWbdcBwAAAABfVqmwFRgYaH3Q74l+/PFH6xJAAAAAAPBllQpbV111lSZPnmzdg94wDKWlpen+++/XwIEDz+oEAQAAAKAqqlTYmjlzpg4fPqzIyEgdPXpUvXv3VsuWLRUaGqrHH3/8bM8RAAAAAKqcSt0gIzw8XCtWrNDatWu1efNmHT58WB07dlTfvn3P9vwAAAAAoEqqVNgq1qNHD/Xo0eNszQUAAAAAqo1Kha0TP8eqLBMnTqzUZAAAAACguqhU2Pr44489vi4sLNSePXvk5+enFi1aELYAAAAA+LxKha2yPiA4JydHQ4cO1TXXXPM/TwoAAAAAqrpK3Y2wLGFhYZo0aZIeeeSRs7VLAAAAAKiyzlrYkqTs7GxlZ2efzV0CAAAAQJVUqcsIn3vuOY+vTdPUH3/8obffflv9+/c/KxMDAAAAgKqsUmHrmWee8fja4XAoIiJCQ4YM0YQJE87KxAAAAACgKqtU2NqzZ8/ZngcAAAAAVCtn9T1bAAAAAIDjKnVm68iRI5o2bZpWrlyp/fv3y+12e2z/+eefz8rkAAAAAKCqqlTYuv3227V69WrdeuutatCggQzDONvzAgAAAIAqrVJha8mSJVq0aJG6d+9+tucDAAAAANVCpd6zVbt2bdWpU+dszwUAAAAAqo1Kha3HHntMEydOVF5e3tmeDwAAAABUC5W6jHDmzJnavXu3oqKi1Lx5c/n7+3ts37Rp01mZHAAAAABUVZUKW1dfffVZngYAAAAAVC+VClv/+te/zvY8AAAAAKBaqfSHGmdlZem1117ThAkTdPDgQUnHLx/8/fffz9rkAAAAAKCqqtSZrc2bN6tv374KDw/XL7/8ojvuuEN16tTRRx99pLS0NL311ltne54AAAAAUKVU6szWuHHjNHToUO3atUtBQUFWfcCAAVqzZs1ZmxwAAAAAVFWVClsbNmzQnXfeWareqFEjpaen/8+TAgAAAICqrlJhKzAwUDk5OaXqP/74oyIiIv7nSQEAAABAVVepsHXVVVdp8uTJKiwslCQZhqG0tDTdf//9Gjhw4FmdIAAAAABURZUKWzNnztThw4cVGRmpo0ePqnfv3mrZsqVCQ0P1+OOPn+05AgAAAECVU6m7EYaHh2vFihVau3atNm/erMOHD6tjx47q27fv2Z4fAAAAAFRJlQpbe/fuVZMmTdSjRw/16NHjbM8JAAAAAKq8Sl1G2Lx5c/Xu3VuvvvqqDh06dLbnBAAAAABVXqXC1nfffacuXbpo8uTJatCgga6++mr95z//UX5+/tmeHwAAAABUSZUKWx06dNCTTz6ptLQ0LVmyRBERERo5cqSioqI0fPjwsz1HAAAAAKhyKhW2ihmGofj4eL366qv64osvFB0drTfffPNszQ0AAAAAqqz/KWz99ttvmjFjhmJjY9WlSxfVrFlTL7744tmaGwAAAABUWZW6G+GcOXM0f/58ffPNN2rTpo0GDx6sTz/9VM2aNTvb8wMAAACAKqlSYWvKlCm66aab9Nxzz+nCCy8823MCAAAAgCqvUmErLS1NhmGc7bkAAAAAQLVRqfdsGYahr7/+Wrfccovi4uL0+++/S5LefvttrV279qxOEAAAAACqokqFrQ8//FAJCQkKDg7W999/b32+VnZ2tp544olKTWTatGkyDENjxoyxaseOHVNSUpLq1q2rmjVrauDAgcrIyPB4XFpamhITExUSEqLIyEjdd999Kioq8hjz1VdfqWPHjgoMDFTLli01b968Ss0RAAAAACqqUmFrypQpmj17tl599VX5+/tb9e7du2vTpk1nvL8NGzZozpw5uuCCCzzqY8eO1eeff64FCxZo9erV2rdvn6699lpru8vlUmJiogoKCrRu3Tq9+eabmjdvniZOnGiN2bNnjxITExUfH6/U1FSNGTNGt99+u5YtW1aJzgEAAACgYioVtnbu3KlevXqVqoeHhysrK+uM9nX48GENHjxYr776qmrXrm3Vs7Oz9frrr+vpp59Wnz591KlTJ82dO1fr1q3T+vXrJUnLly/Xtm3b9M477yg2Nlb9+/fXY489phdffFEFBQWSpNmzZys6OlozZ85U27ZtNXr0aF133XV65plnKtM6AAAAAFRIpW6QUb9+ff30009q3ry5R33t2rX629/+dkb7SkpKUmJiovr27aspU6ZY9Y0bN6qwsFB9+/a1am3atFHTpk2VnJysrl27Kjk5We3bt1dUVJQ1JiEhQaNGjdLWrVvVoUMHJScne+yjeMyJlyueLD8/37o0UpJycnIkSYWFhSosLJQkORwOOZ1OuVwuud1ua2xxvaioSKZpWnWn0ymHw1FuvXi/xfz8jv9qTr4ksry6v7+/3G63XC6XVTMMQ35+fuXWy5t7efUAh6kT74tS5JZcplGqXuiS3DIU6CzpU5IKXJIpKdDpUVa+SzIkBZSqG3LIlP8JddOUCtyGnIYpP8fp625TKnQb8neYclRg7vR06p5M0/TKc686rqeq2pMk1pOP91T8/GQ9+XZPklhPPt6T2+322fV08vZTqVTYuuOOO/R///d/euONN2QYhvbt26fk5GTde++9Hpfwnc57772nTZs2acOGDaW2paenKyAgQLVq1fKoR0VFKT093RpzYtAq3l687VRjcnJydPToUQUHB5f63lOnTtWkSZNK1ZcvX66QkBBJUtOmTdWhQwdt3rxZaWlp1pjWrVurTZs2SklJ0YEDB6x6bGysmjVrpjVr1ig3N9eqx8XFKTIyUsuXL/d48sXHxys4OFiLFy/2mMOAAQN09OhRrVq1yqr5+fkpMTFRmZmZSk5OtuqhoaHq06eP9u7dq9TUVKseERGhbt26adeuXdq5c6dVP11Pw1u71bZWyRPy37sdWr/f0Lj2LjUIKZnjy9sc2pFtaHJHl4JOeIZNTXXqUIE0o0vJopGk8SlO1Q6QJsSW1I8VSfdv8FOrcFOjYkoWzR950rQf/HRRhKmbWpTUt2cZmr3dqcsaudW/SckckzMMvfezUwObuxUXVVJfstfQ0t+c9HSGPeXm5nrluVcd11NV7UkS68nHe8rNzWU90ZMksZ58vKfMzEyfXU95eXmqKMM8Mc5VkGmaeuKJJzR16lTrmwUGBuq+++7ThAkTygwwJ9u7d686d+6sFStWWO/VuuSSSxQbG6tnn31W8+fP17BhwzzOMElSly5dFB8fr+nTp2vkyJH69ddfPd5/lZeXpxo1amjx4sXq37+/WrVqpWHDhmnChAnWmMWLFysxMVF5eXllzrWsM1tNmjRRZmamwsLCJFX9V6Qq86pAqwcX+uyrN/R0vKc9UxOrxStSp6vTU/k9tXhoKevJx3vaMSXx+ONYTz7dU8uHl7GefLyn7Y8N8Nn1lJOTo3r16ik7O9vKBuWp1JktwzD00EMP6b777tNPP/2kw4cPKyYmRnPmzFF0dLR1VulUNm7cqP3796tjx45WzeVyac2aNXrhhRe0bNkyFRQUKCsry+PsVkZGhurXry/p+OWMKSkpHvstvlvhiWNOvoNhRkaGwsLCyg2FgYGBCgwMLFX39/f3uCGIdPyX4nQ6S40t/qVUtH7yfitTdzgc1mU+FamXN/fy6gXusj9brbx6vqu8eumaWU7dLaPMuss05DqDeuEZzp2eyp67YRheee5Vx/VUlXtiPfl2T8WXkLGe6In15Ns9WZeW++B6Km97Wc7oBhn5+fmaMGGCOnfurO7du2vx4sWKiYnR1q1b1bp1a82aNUtjx46t0L4uvfRSbdmyRampqda/zp07a/DgwdZ/+/v7a+XKldZjdu7cqbS0NMXFxUk6fqpvy5Yt2r9/vzVmxYoVCgsLU0xMjDXmxH0UjyneBwAAAADY4YzObE2cOFFz5sxR3759tW7dOl1//fUaNmyY1q9fr5kzZ+r6668vM02WJTQ0VOeff75HrUaNGqpbt65VHzFihMaNG6c6deooLCxM//jHPxQXF6euXbtKkvr166eYmBjdeuutmjFjhtLT0/Xwww8rKSnJOjN111136YUXXtD48eM1fPhwffnll/rggw+0aNGiM2kdAAAAAM7IGYWtBQsW6K233tJVV12l//73v7rgggtUVFSkH374wbqs4Gx65pln5HA4NHDgQOXn5yshIUEvvfSStd3pdGrhwoUaNWqU4uLiVKNGDQ0ZMkSTJ0+2xkRHR2vRokUaO3asZs2apcaNG+u1115TQkLCWZ8vAAAAABQ7oxtkBAQEaM+ePWrUqJEkKTg4WCkpKWrfvr1tE/wryMnJUXh4eIXeBFedNX+As4G+7pdpid6eAryM4wA4DkDiWADfPhacSTY4o/dsuVwuBQQEWF/7+fmpZs2alZslAAAAAFRjZ3QZoWmaGjp0qPV+qGPHjumuu+5SjRo1PMZ99NFHZ2+GAAAAAFAFnVHYGjJkiMfXt9xyy1mdDAAAAABUF2cUtubOnWvXPAAAAACgWjmj92wBAAAAACqGsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANjAq2Hr5Zdf1gUXXKCwsDCFhYUpLi5OS5YssbYfO3ZMSUlJqlu3rmrWrKmBAwcqIyPDYx9paWlKTExUSEiIIiMjdd9996moqMhjzFdffaWOHTsqMDBQLVu21Lx5885FewAAAAB8mFfDVuPGjTVt2jRt3LhR3333nfr06aO///3v2rp1qyRp7Nix+vzzz7VgwQKtXr1a+/bt07XXXms93uVyKTExUQUFBVq3bp3efPNNzZs3TxMnTrTG7NmzR4mJiYqPj1dqaqrGjBmj22+/XcuWLTvn/QIAAADwHYZpmqa3J3GiOnXq6Mknn9R1112niIgIzZ8/X9ddd50kaceOHWrbtq2Sk5PVtWtXLVmyRFdccYX27dunqKgoSdLs2bN1//3368CBAwoICND999+vRYsW6b///a/1PQYNGqSsrCwtXbq0QnPKyclReHi4srOzFRYWdvabriKaP7DI21OAl/0yLdHbU4CXcRwAxwFIHAvg28eCM8kGf5n3bLlcLr333ns6cuSI4uLitHHjRhUWFqpv377WmDZt2qhp06ZKTk6WJCUnJ6t9+/ZW0JKkhIQE5eTkWGfHkpOTPfZRPKZ4HwAAAABgBz9vT2DLli2Ki4vTsWPHVLNmTX388ceKiYlRamqqAgICVKtWLY/xUVFRSk9PlySlp6d7BK3i7cXbTjUmJydHR48eVXBwcKk55efnKz8/3/o6JydHklRYWKjCwkJJksPhkNPplMvlktvttsYW14uKinTiSUOn0ymHw1FuvXi/xfz8jv9qTn7/WXl1f39/ud1uuVwuq2YYhvz8/Mqtlzf38uoBDlOGUfI9i9ySyzRK1QtdkluGAp2eJ00LXJIpKdDpUVa+SzIkBZSqG3LIlP8JddOUCtyGnIYpP8fp625TKnQb8neYclRg7vR06p5M0/TKc686rqeq2pMk1pOP91T8/GQ9+XZPklhPPt6T2+322fV08vZT8XrYat26tVJTU5Wdna3//Oc/GjJkiFavXu3VOU2dOlWTJk0qVV++fLlCQkIkSU2bNlWHDh20efNmpaWlWWNat26tNm3aKCUlRQcOHLDqsbGxatasmdasWaPc3FyrHhcXp8jISC1fvtzjyRcfH6/g4GAtXrzYYw4DBgzQ0aNHtWrVKqvm5+enxMREZWZmepyxCw0NVZ8+fbR3716lpqZa9YiICHXr1k27du3Szp07rfrpehre2q22tUqekP/e7dD6/YbGtXepQUjJHF/e5tCObEOTO7oUdMIzbGqqU4cKpBldShaNJI1Pcap2gDQhtqR+rEi6f4OfWoWbGhVTsmj+yJOm/eCniyJM3dSipL49y9Ds7U5d1sit/k1K5picYei9n50a2NytuKiS+pK9hpb+5qSnM+wpNzfXK8+96rieqmpPklhPPt5Tbm4u64meJIn15OM9ZWZm+ux6ysvLU0X95d6z1bdvX7Vo0UI33nijLr30Uh06dMjj7FazZs00ZswYjR07VhMnTtRnn33m8UvYs2eP/va3v2nTpk3q0KGDevXqpY4dO+rZZ5+1xsydO1djxoxRdnZ2mXMo68xWkyZNlJmZaV2XWdVfkarMqwKtHlzos6/e0NPxnvZMTawWr0idrk5P5ffU4qGlrCcf72nHlOPv02A9+XZPLR9exnry8Z62PzbAZ9dTTk6O6tWrV6H3bHn9zNbJ3G638vPz1alTJ/n7+2vlypUaOHCgJGnnzp1KS0tTXFycpOPp8/HHH9f+/fsVGRkpSVqxYoXCwsIUExNjjTk5Wa9YscLaR1kCAwMVGBhYqu7v7y9/f3+PmtPplNPpLDW2+JdS0frJ+61M3eFwWJf5VKRe3tzLqxe4jVK1U9XzXeXVS9fMcupuGWXWXaYh1xnUC89w7vRU9twNw/DKc686rqeq3BPrybd7Kr6EjPVET6wn3+7JurTcB9dTedvL/B4VHmmDCRMmqH///mratKlyc3M1f/58ffXVV1q2bJnCw8M1YsQIjRs3TnXq1FFYWJj+8Y9/KC4uTl27dpUk9evXTzExMbr11ls1Y8YMpaen6+GHH1ZSUpIVlu666y698MILGj9+vIYPH64vv/xSH3zwgRYt4i46AAAAAOzj1bC1f/9+3Xbbbfrjjz8UHh6uCy64QMuWLdNll10mSXrmmWfkcDg0cOBA5efnKyEhQS+99JL1eKfTqYULF2rUqFGKi4tTjRo1NGTIEE2ePNkaEx0drUWLFmns2LGaNWuWGjdurNdee00JCQnnvF8AAAAAvsOrYev1118/5fagoCC9+OKLevHFF8sd06xZs1KXCZ7skksu0ffff1+pOQIAAABAZfxlPmcLAAAAAKoTwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANjAq2Fr6tSpuuiiixQaGqrIyEhdffXV2rlzp8eYY8eOKSkpSXXr1lXNmjU1cOBAZWRkeIxJS0tTYmKiQkJCFBkZqfvuu09FRUUeY7766it17NhRgYGBatmypebNm2d3ewAAAAB8mFfD1urVq5WUlKT169drxYoVKiwsVL9+/XTkyBFrzNixY/X5559rwYIFWr16tfbt26drr73W2u5yuZSYmKiCggKtW7dOb775pubNm6eJEydaY/bs2aPExETFx8crNTVVY8aM0e23365ly5ad034BAAAA+A4/b37zpUuXenw9b948RUZGauPGjerVq5eys7P1+uuva/78+erTp48kae7cuWrbtq3Wr1+vrl27avny5dq2bZu++OILRUVFKTY2Vo899pjuv/9+PfroowoICNDs2bMVHR2tmTNnSpLatm2rtWvX6plnnlFCQsI57xsAAABA9efVsHWy7OxsSVKdOnUkSRs3blRhYaH69u1rjWnTpo2aNm2q5ORkde3aVcnJyWrfvr2ioqKsMQkJCRo1apS2bt2qDh06KDk52WMfxWPGjBlT5jzy8/OVn59vfZ2TkyNJKiwsVGFhoSTJ4XDI6XTK5XLJ7XZbY4vrRUVFMk3TqjudTjkcjnLrxfst5ud3/Fdz8uWQ5dX9/f3ldrvlcrmsmmEY8vPzK7de3tzLqwc4TBlGyfcscksu0yhVL3RJbhkKdJb0KUkFLsmUFOj0KCvfJRmSAkrVDTlkyv+EumlKBW5DTsOUn+P0dbcpFboN+TtMOSowd3o6dU+maXrluVcd11NV7UkS68nHeyp+frKefLsnSawnH+/J7Xb77Ho6efup/GXCltvt1pgxY9S9e3edf/75kqT09HQFBASoVq1aHmOjoqKUnp5ujTkxaBVvL952qjE5OTk6evSogoODPbZNnTpVkyZNKjXH5cuXKyQkRJLUtGlTdejQQZs3b1ZaWpo1pnXr1mrTpo1SUlJ04MABqx4bG6tmzZppzZo1ys3NtepxcXGKjIzU8uXLPZ588fHxCg4O1uLFiz3mMGDAAB09elSrVq2yan5+fkpMTFRmZqaSk5OtemhoqPr06aO9e/cqNTXVqkdERKhbt27atWuXx3vkTtfT8NZuta1V8oT8926H1u83NK69Sw1CSub48jaHdmQbmtzRpaATnmFTU506VCDN6FKyaCRpfIpTtQOkCbEl9WNF0v0b/NQq3NSomJJF80eeNO0HP10UYeqmFiX17VmGZm936rJGbvVvUjLH5AxD7/3s1MDmbsVFldSX7DW09DcnPZ1hT7m5uV557lXH9VRVe5LEevLxnnJzc1lP9CRJrCcf7ykzM9Nn11NeXp4qyjBPjHNeNGrUKC1ZskRr165V48aNJUnz58/XsGHDPM4ySVKXLl0UHx+v6dOna+TIkfr111893n+Vl5enGjVqaPHixerfv79atWqlYcOGacKECdaYxYsXKzExUXl5eaXCVllntpo0aaLMzEyFhYVJqvqvSFXmVYFWDy702Vdv6Ol4T3umJlaLV6ROV6en8ntq8dBS1pOP97RjSuLxx7GefLqnlg8vYz35eE/bHxvgs+spJydH9erVU3Z2tpUNyvOXOLM1evRoLVy4UGvWrLGCliTVr19fBQUFysrK8ji7lZGRofr161tjUlJSPPZXfLfCE8ecfAfDjIwMhYWFlQpakhQYGKjAwMBSdX9/f/n7+3vUnE6nnE5nqbHFv5SK1k/eb2XqDofDusynIvXy5l5evcBtlKqdqp7vKq9eumaWU3fLKLPuMg25zqBeeIZzp6ey524Yhleee9VxPVXlnlhPvt1T8SVkrCd6Yj35dk/WpeU+uJ7K214Wr96N0DRNjR49Wh9//LG+/PJLRUdHe2zv1KmT/P39tXLlSqu2c+dOpaWlKS4uTtLx031btmzR/v37rTErVqxQWFiYYmJirDEn7qN4TPE+AAAAAOBs8+qZraSkJM2fP1+ffvqpQkNDrfdYhYeHKzg4WOHh4RoxYoTGjRunOnXqKCwsTP/4xz8UFxenrl27SpL69eunmJgY3XrrrZoxY4bS09P18MMPKykpyTo7ddddd+mFF17Q+PHjNXz4cH355Zf64IMPtGjRIq/1DgAAAKB68+qZrZdfflnZ2dm65JJL1KBBA+vf+++/b4155plndMUVV2jgwIHq1auX6tevr48++sja7nQ6tXDhQjmdTsXFxemWW27RbbfdpsmTJ1tjoqOjtWjRIq1YsUIXXnihZs6cqddee43bvgMAAACwjVfPbFXk3hxBQUF68cUX9eKLL5Y7plmzZqXueHKySy65RN9///0ZzxEAAAAAKsOrZ7YAAAAAoLoibAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA28GrYWrNmja688ko1bNhQhmHok08+8dhumqYmTpyoBg0aKDg4WH379tWuXbs8xhw8eFCDBw9WWFiYatWqpREjRujw4cMeYzZv3qyePXsqKChITZo00YwZM+xuDQAAAICP82rYOnLkiC688EK9+OKLZW6fMWOGnnvuOc2ePVvffvutatSooYSEBB07dswaM3jwYG3dulUrVqzQwoULtWbNGo0cOdLanpOTo379+qlZs2bauHGjnnzyST366KN65ZVXbO8PAAAAgO/y8+Y379+/v/r371/mNtM09eyzz+rhhx/W3//+d0nSW2+9paioKH3yyScaNGiQtm/frqVLl2rDhg3q3LmzJOn555/XgAED9NRTT6lhw4Z69913VVBQoDfeeEMBAQFq166dUlNT9fTTT3uEMgAAAAA4m/6y79nas2eP0tPT1bdvX6sWHh6uiy++WMnJyZKk5ORk1apVywpaktS3b185HA59++231phevXopICDAGpOQkKCdO3fq0KFD56gbAAAAAL7Gq2e2TiU9PV2SFBUV5VGPioqytqWnpysyMtJju5+fn+rUqeMxJjo6utQ+irfVrl271PfOz89Xfn6+9XVOTo4kqbCwUIWFhZIkh8Mhp9Mpl8slt9ttjS2uFxUVyTRNq+50OuVwOMqtF+/3xD4kqaioqEJ1f39/ud1uuVwuq2YYhvz8/Mqtlzf38uoBDlOGUfI9i9ySyzRK1QtdkluGAp0lfUpSgUsyJQU6PcrKd0mGpIBSdUMOmfI/oW6aUoHbkNMw5ec4fd1tSoVuQ/4OU44KzJ2eTt2TaZpeee5Vx/VUVXuSxHry8Z6Kn5+sJ9/uSRLrycd7crvdPrueTt5+Kn/ZsOVNU6dO1aRJk0rVly9frpCQEElS06ZN1aFDB23evFlpaWnWmNatW6tNmzZKSUnRgQMHrHpsbKyaNWumNWvWKDc316rHxcUpMjJSy5cv93jyxcfHKzg4WIsXL/aYw4ABA3T06FGtWrXKqvn5+SkxMVGZmZnWWT9JCg0NVZ8+fbR3716lpqZa9YiICHXr1k27du3Szp07rfrpehre2q22tUqekP/e7dD6/YbGtXepQUjJHF/e5tCObEOTO7oUdMIzbGqqU4cKpBldShaNJI1Pcap2gDQhtqR+rEi6f4OfWoWbGhVTsmj+yJOm/eCniyJM3dSipL49y9Ds7U5d1sit/k1K5picYei9n50a2NytuKiS+pK9hpb+5qSnM+wpNzfXK8+96rieqmpPklhPPt5Tbm4u64meJIn15OM9ZWZm+ux6ysvLU0UZ5olxzosMw9DHH3+sq6++WpL0888/q0WLFvr+++8VGxtrjevdu7diY2M1a9YsvfHGG7r33ns9LgcsKipSUFCQFixYoGuuuUa33XabcnJyPO50uGrVKvXp00cHDx6s8JmtJk2aKDMzU2FhYZKq/itSlXlVoNWDC3321Rt6Ot7TnqmJ1eIVqdPV6an8nlo8tJT15OM97ZiSePxxrCef7qnlw8tYTz7e0/bHBvjsesrJyVG9evWUnZ1tZYPy/GXPbEVHR6t+/fpauXKlFbZycnL07bffatSoUZKOp8+srCxt3LhRnTp1kiR9+eWXcrvduvjii60xDz30kAoLC+Xv7y9JWrFihVq3bl1m0JKkwMBABQYGlqr7+/tb+yjmdDrldDpLjS3+pVS0fvJ+K1N3OBzWZT4VqZc39/LqBW6jVO1U9XxXefXSNbOcultGmXWXach1BvXCM5w7PZU9d8MwvPLcq47rqSr3xHry7Z6KLyFjPdET68m3e7IuLffB9VTe9rJ49QYZhw8fVmpqqnV6cM+ePUpNTVVaWpoMw9CYMWM0ZcoUffbZZ9qyZYtuu+02NWzY0Dr71bZtW11++eW64447lJKSom+++UajR4/WoEGD1LBhQ0nSzTffrICAAI0YMUJbt27V+++/r1mzZmncuHFe6hoAAACAL/Dqma3vvvtO8fHx1tfFAWjIkCGaN2+exo8fryNHjmjkyJHKyspSjx49tHTpUgUFBVmPeffddzV69GhdeumlcjgcGjhwoJ577jlre3h4uJYvX66kpCR16tRJ9erV08SJE7ntOwAAAABbeTVsXXLJJTrVW8YMw9DkyZM1efLkcsfUqVNH8+fPP+X3ueCCC/T1119Xep4AAAAAcKb+sp+zBQAAAABVGWELAAAAAGxA2AIAAAAAGxC2AAAAAMAGhC0AAAAAsAFhCwAAAABsQNgCAAAAABsQtgAAAADABoQtAAAAALABYQsAAAAAbEDYAgAAAAAbELYAAAAAwAaELQAAAACwAWELAAAAAGxA2AIAAAAAGxC2AAAAAMAGhC0AAAAAsAFhCwAAAABsQNgCAAAAABsQtgAAAADABoQtAAAAALABYQsAAAAAbEDYAgAAAAAbELYAAAAAwAaELQAAAACwAWELAAAAAGxA2AIAAAAAGxC2AAAAAMAGhC0AAAAAsAFhCwAAAABsQNgCAAAAABsQtgAAAADABoQtAAAAALABYQsAAAAAbEDYAgAAAAAbELYAAAAAwAaELQAAAACwAWELAAAAAGxA2AIAAAAAGxC2AAAAAMAGhC0AAAAAsAFhCwAAAABs4FNh68UXX1Tz5s0VFBSkiy++WCkpKd6eEgAAAIBqymfC1vvvv69x48bpX//6lzZt2qQLL7xQCQkJ2r9/v7enBgAAAKAa8pmw9fTTT+uOO+7QsGHDFBMTo9mzZyskJERvvPGGt6cGAAAAoBry8/YEzoWCggJt3LhREyZMsGoOh0N9+/ZVcnJyqfH5+fnKz8+3vs7OzpYkHTx4UIWFhdbjnU6nXC6X3G63x36dTqeKiopkmqZVdzqdcjgc5daL91vMz+/4r6aoqKhCdX9/f7ndbrlcLqtmGIb8/PzKrZc39/LqzsIjMoyS7+lySy7TkL/D9KgXuSS3DAU4S/qUpEKXZEoKcHqUVeCSDEn+peqGHDLld0LdNKVCtyGnYcrpqHj95DmWN3d6OnVP2dnZXnnuVcf1VFV7cufnsZ58vKfi/yeynny7J3d+HuvJx3vKysry2fWUm5v7/3+Wnr/PsvhE2MrMzJTL5VJUVJRHPSoqSjt27Cg1furUqZo0aVKpenR0tG1zBKqCWs96ewYAvK3WTG/PAMBfQW2OBcrNzVV4ePgpx/hE2DpTEyZM0Lhx46yv3W63Dh48qLp168o4MdLDp+Tk5KhJkybau3evwsLCvD0dAF7AcQAAxwGYpqnc3Fw1bNjwtGN9ImzVq1dPTqdTGRkZHvWMjAzVr1+/1PjAwEAFBgZ61GrVqmXnFFGFhIWFcXAFfBzHAQAcB3zb6c5oFfOJG2QEBASoU6dOWrlypVVzu91auXKl4uLivDgzAAAAANWVT5zZkqRx48ZpyJAh6ty5s7p06aJnn31WR44c0bBhw7w9NQAAAADVkM+ErRtvvFEHDhzQxIkTlZ6ertjYWC1durTUTTOA8gQGBupf//pXqUtMAfgOjgMAOA7gTBhmRe5ZCAAAAAA4Iz7xni0AAAAAONcIWwAAAABgA8IWAAAAANiAsAUAAAAANiBsAQAAAIANCFvAOeB2u709BQAAAJxj3PodsFF2drbCw8MlHQ9cDgevbwC+Zt++fdqwYYOOHTum8847Tx07dvT2lAB4wZ49e/TJJ5/owIEDiouL05VXXuntKeEcIGwBNtm2bZu6deum8ePH68EHH5RE4AJ8zZYtW3TNNdeodu3a2r9/vyTppZdeUmJiopdnBuBc2rx5sxITE9WqVSsdO3ZMycnJ+uSTT3TVVVd5e2qwGX/1ATb47bffdMsttygqKkozZ87UtGnTJEkOh4NLCgEfsXv3bvXv31/XXXedVqxYoYULF2rAgAGaO3eujhw5Il7rBHzDjz/+qMTERN16661asmSJFi5cqP79++u3337z9tRwDvh5ewJAdeN2u/Xhhx8qOjpao0ePVkpKip544glJ0gMPPGAFLs5wAdVXQUGBXnzxRXXr1k2PPfaY/P39VatWLV100UV65JFH5Ha7ZRiGt6cJwGYFBQWaNGmSLr30Uj322GNyOp0KCAhQcHCw1q9fr40bN6pDhw4aPHiwateu7e3pwgaELeAsczgcGjBggCIjIxUfH6/Y2FiZpqmpU6dKInABvsDhcKhly5aKjo6Wv7+/TNOUYRjq06ePJk+erOzsbIWGhnp7mgBsFhAQoAcffFD79u2T0+mUJD3xxBP6+OOPddNNNykoKEj33HOPdu7cqeeff97Ls4UdCFuADc477zy1bNlSklS7dm3dfvvtMgzD4wyXaZr6/PPPFRcXp3r16nlzugDOMj8/P11zzTVq0KCBRz0gIECS5HK5rAC2Y8cOtWnTxhvTBHAOtGvXTu3atZN0/H2cq1at0sKFC3X55ZdbL8IMGjRIo0ePVuvWrb08W5xthC3gLNi3b59+//13/fnnn+rbt68cDoccDoeKiork5+enevXqafjw4ZKOv6Jlmqb+/PNPzZo1S2lpaV6ePYCzofg4kJmZqYSEBEVFRUmSdRxwu93KyclRXl6eAgICZBiGJkyYoOnTp+vQoUMKCwvj0kKgGijvbwJJat++vd566y2PF2IcDodiYmJ44bWaImwB/6PNmzfriiuuUGhoqH788Ue1b99eI0eO1C233KKaNWvK5XLJ6XQqIiJCI0aMkGmaevDBB1WrVi0lJyeXeuUbQNVT1nHgjjvu0K233qqaNWtalw0HBATIz89PwcHBmjRpkl588UWtX7/e+ogIAFXb6f4mkKT69et7PCY5OVmNGze2znyjeuENI8D/IDMzU4MGDdLNN9+sRYsWad++fWrTpo3mzZunRx55RLm5uXI6ndYdCOvVq6dt27YpNDRUa9euVefOnb3cAYD/VXnHgTfffNM6DhS/ql2zZk1FRERo5MiReuKJJ7Rq1Sp16dLFyx0AOBsq8jeBJOsM9h9//KFHHnlEr776qqZPn877OKspwhbwP0hPT9fRo0d18803q3nz5oqIiNC8efOUkJCgdevWafr06Tp27JgcDodM09Q777yj5cuXa9WqVYqJifH29AGcBRU9DkjSgQMHtGXLFi1cuFApKSnq1KmTl2cP4Gw5k2PBxo0bNX78eL377rtatWqVzj//fC/PHnYhbAH/g+L3XRS/76qoqEgBAQF65JFH1Lt3by1atEgbNmyQdPyVrO7du+vbb79Vx44dvTltAGfRmRwHGjVqpHvvvVcbN27UhRde6M1pAzjLzuRYUL9+fd1www1auXKlYmNjvThr2M0w+VRFoNLy8/PVo0cP1a9fX5988omcTqf1ZnjTNHXhhReqQ4cOevPNN607jwGoXs7kOFA8PjAw0MuzBnC2VeRYEBsbq7feesvbU8U5xJktoJLcbrcCAwM1d+5crVmzRqNGjZIk66BqGIauuuoq7d+/X5IIWkA1dCbHgeLXNglaQPVT0WPBgQMHvDxTnGuELaCSHA6HXC6Xzj//fL355pv697//rdtuu00ZGRnWmD179qh27dpyuVxenCkAu5zJcaD4RjkAqh/+JkB5uIwQqKDiWzcXK7404PDhw8rPz1dqaqpuvvlmNWvWTHXq1FHdunX16aefKjk5We3bt/fizAGcLRwHAEgcC1BxnNkCTiMzM1NSyatWkuRyueTn56dffvlFrVq10oYNG3TppZdq69atGjBggBo1aqTIyEilpKRwUAWqAY4DACSOBThznNkCTuHHH39U586dNWjQIL3yyiuSZH1I8d69e9WxY0f9/e9/16uvviq32y2n02ldm33yq14AqiaOAwAkjgWoHH7rwCls27ZNwcHB2rJli+68805JktPpVEFBgT777DPdeuutmjNnjgzDkNPp9HgsN8QAqgeOAwAkjgWoHMIWcAqBgYGqVauWrr76aiUnJ+uuu+6SdPyzNP7+97/r6aefLveAyoEVqB44DgCQOBagcvy8PQHgr6x9+/bq1KmTbr/9dgUEBGjevHkaN26csrOz1aVLFw0fPlz+/v7eniYAG3EcACBxLEDlELaAU6hTp462bt2qvXv36s4771TNmjU1YcIEHTx4UGPGjJG/v791vTaA6onjAACJYwEqh8sIgXIUFhYqMDBQ9evX1+HDhxUSEqKVK1eqsLBQLVu21GuvvSZJHFSBaozjAACJYwEqjzNbgKR9+/Zp06ZNKigoUPPmzdWxY0frUoBOnTrpp59+0iuvvKI1a9bo888/15YtWzRt2jT5+flp5syZXp49gLOB4wAAiWMBzi7CFnzeli1bdPXVV6tevXr6+eef1bx5c91///267rrrJB1/Q+zw4cPVvHlzLVy4UB07dtQFF1wgh8OhhIQEL88ewNnAcQCAxLEAZx+fswWftnv3bl1yySW6+eab9eCDD+qnn37S888/L6fTqTlz5sjPz09FRUX6v//7Pw0dOlQXXXQRn5kBVDMcBwBIHAtgD8IWfFZBQYEmTJig3377TW+//bYCAgIkSW+88YbGjx+vnTt3qm7dul6eJQA7cRwAIHEsgH24jBA+y+12q3Hjxmrbtq0CAgKsV6e6deummjVrqrCwsMzH8MoVUH1wHAAgcSyAfQhb8FlBQUG6+uqrFR0d7VGvVauW/P39PQ6s33//vTp06MBBFahmOA4AkDgWwD48S+BT/vjjD6WkpGjp0qVyu93WQdXlclmf7p6dna1Dhw5Zj5k4caIuvfRS/fnnn+KqW6Dq4zgAQOJYgHODM1vwGZs3b9ZVV12lwMBAZWRkqEGDBpo4caISEhJUp04d65IBwzDkcDhUs2ZNTZkyRU899ZS+/vprrtUGqgGOAwAkjgU4d7hBBnzCgQMH1KtXL1177bUaMWKEgoKCNG7cOG3evFk33HCDkpKSFBERIUnav3+/Lr/8crVq1Uoff/yx1q1bp06dOnm5AwD/K44DACSOBTi3OLMFn3DgwAEdO3ZM1157rf72t79Jkt577z098MAD+uijj1SjRg0lJSUpJCREf/75p1JTU7Vjxw59++23io2N9e7kAZwVHAcASBwLcG7xni34hMLCQhUVFSkvL0+SdPToUUnStGnTFB8fr5dfflk//fSTJKl27dq6++67tWnTJg6qQDXCcQCAxLEA5xaXEcJndOnSRTVr1tSXX34pScrPz1dgYKAk6aKLLlLLli3173//W5J07NgxBQUFeW2uAOzBcQCAxLEA5w5ntlAtHTlyRLm5ucrJybFqc+bM0datW3XzzTdLkgIDA1VUVCRJ6tWrl44cOWKN5aAKVH0cBwBIHAvgXYQtVDvbtm3Ttddeq969e6tt27Z69913JUlt27bVrFmztGLFCl1//fUqLCy0PiNj//79qlGjhoqKiriVK1ANcBwAIHEsgPdxgwxUK9u2bVOvXr102223qXPnztq4caOGDRummJgYdejQQVdddZVq1Kihu+++WxdccIHatGmjgIAALVq0SOvXr5efH0sCqOo4DgCQOBbgr4H3bKHaOHjwoG666Sa1adNGs2bNsurx8fFq3769nnvuOauWm5urKVOm6ODBgwoKCtKoUaMUExPjjWkDOIs4DgCQOBbgr4PIjmqjsLBQWVlZuu666yRJbrdbDodD0dHROnjwoCTJNE2ZpqnQ0FBNnz7dYxyAqo/jAACJYwH+Ong2odqIiorSO++8o549e0qSXC6XJKlRo0bWgbP4k+BPfJOsYRjnfrIAbMFxAIDEsQB/HYQtVCvnnXeepOOvTPn7+0s6/srV/v37rTFTp07Va6+9Zt11iAMrUL1wHAAgcSzAXwOXEaJacjgcMk3TOmgWv4o1ceJETZkyRd9//z1vfAWqOY4DACSOBfAuzmyh2iq+94ufn5+aNGmip556SjNmzNB3332nCy+80MuzA3AucBwAIHEsgPcQ41FtFb9y5e/vr1dffVVhYWFau3atOnbs6OWZAThXOA4AkDgWwHs4s4VqLyEhQZK0bt06de7c2cuzAeANHAcASBwLcO7xOVvwCUeOHFGNGjW8PQ0AXsRxAIDEsQDnFmELAAAAAGzAZYQAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQDAOWAYhj755BNvTwMAcA4RtgAA1drQoUNlGIbuuuuuUtuSkpJkGIaGDh161r7fo48+qtjY2LO2PwBA1UXYAgBUe02aNNF7772no0ePWrVjx45p/vz5atq0qRdnBgCozghbAIBqr2PHjmrSpIk++ugjq/bRRx+padOm6tChg1XLz8/XPffco8jISAUFBalHjx7asGGDtf2rr76SYRhauXKlOnfurJCQEHXr1k07d+6UJM2bN0+TJk3SDz/8IMMwZBiG5s2bZz0+MzNT11xzjUJCQnTeeefps88+s795AIDXELYAAD5h+PDhmjt3rvX1G2+8oWHDhnmMGT9+vD788EO9+eab2rRpk1q2bKmEhAQdPHjQY9xDDz2kmTNn6rvvvpOfn5+GDx8uSbrxxht17733ql27dvrjjz/0xx9/6MYbb7QeN2nSJN1www3avHmzBgwYoMGDB5faNwCg+iBsAQB8wi233KK1a9fq119/1a+//qpvvvlGt9xyi7X9yJEjevnll/Xkk0+qf//+iomJ0auvvqrg4GC9/vrrHvt6/PHH1bt3b8XExOiBBx7QunXrdOzYMQUHB6tmzZry8/NT/fr1Vb9+fQUHB1uPGzp0qG666Sa1bNlSTzzxhA4fPqyUlJRz9jMAAJxbft6eAAAA50JERIQSExM1b948maapxMRE1atXz9q+e/duFRYWqnv37lbN399fXbp00fbt2z32dcEFF1j/3aBBA0nS/v37T/v+rxMfV6NGDYWFhWn//v3/U18AgL8uwhYAwGcMHz5co0ePliS9+OKLld6Pv7+/9d+GYUiS3G73GT2u+LEVeRwAoGriMkIAgM+4/PLLVVBQoMLCQiUkJHhsa9GihQICAvTNN99YtcLCQm3YsEExMTEV/h4BAQFyuVxnbc4AgKqLM1sAAJ/hdDqtSwKdTqfHtho1amjUqFG67777VKdOHTVt2lQzZsxQXl6eRowYUeHv0bx5c+3Zs0epqalq3LixQkNDFRgYeFb7AABUDYQtAIBPCQsLK3fbtGnT5Ha7deuttyo3N1edO3fWsmXLVLt27Qrvf+DAgfroo48UHx+vrKwszZ0796x+aDIAoOowTNM0vT0JAAAAAKhueM8WAAAAANiAsAUAAAAANiBsAQAAAIANCFsAAAAAYAPCFgAAAADYgLAFAAAAADYgbAEAAACADQhbAAAAAGADwhYAAAAA2ICwBQAAAAA2IGwBAAAAgA0IWwAAAABgg/8HJ5ZWWItUs7YAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "data['quarter'] = data['order_date'].dt.quarter\n",
+    "data['year'] = data['order_date'].dt.year\n",
+    "\n",
+    "current_year = data['year'].max()\n",
+    "current_quarter = data[data['year'] == current_year]['quarter'].max()\n",
+    "\n",
+    "#Makes sure the there is previous year data\n",
+    "if (current_quarter - 1 == 0) and (data['year'].isin([current_year-1]).any()):\n",
+    "        last_quarter = 4\n",
+    "        quarter_year = current_year -1\n",
+    "else:\n",
+    "    last_quarter = current_quarter - 1\n",
+    "    quarter_year = current_year\n",
+    "\n",
+    "last_quarter_df = data[(data['year'] == quarter_year) & (data['quarter'] == last_quarter)]\n",
+    "\n",
+    "\n",
+    "last_quarter_report = last_quarter_df.groupby(['month'])['revenue'].sum()\n",
+    "print(f'Last Quarter Report:\\n{last_quarter_report}')\n",
+    "last_quarter_report.plot(kind='bar', figsize = (10,6),title= f'{quarter_year} Quarter {last_quarter} Revenue Breakdown')\n",
+    "plt.ylabel(\"Revenue\")\n",
+    "plt.xlabel(\"Month\")\n",
+    "plt.xticks(rotation = 45)\n",
+    "plt.grid(axis='y', linestyle='--')\n",
+    "plt.show()\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9b5c29e",
+   "metadata": {},
+   "source": [
+    "### 2. Top Customers\n",
+    "Who are the top 5 customers by total revenue contribution?\n",
+    "\n",
+    "The top five customers are carrying a big chunk of revenue and it's mostly Seniors and Professionals. I would lock these in with a simple retention plan like having early access on high value items and a check-in offer, then I would look for two to three lookalikes in those customer segments to grow the list further. Moving foward we should be tracking their monthly spend and make sure we never stock out on what they actually buy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "75147e3f",
+   "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>customer_id</th>\n",
+       "      <th>revenue</th>\n",
+       "      <th>customer_segment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>62</td>\n",
+       "      <td>4448.39</td>\n",
+       "      <td>Senior</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>24</td>\n",
+       "      <td>3369.44</td>\n",
+       "      <td>Professional</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>90</td>\n",
+       "      <td>2976.78</td>\n",
+       "      <td>Senior</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>92</td>\n",
+       "      <td>2838.74</td>\n",
+       "      <td>Professional</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>97</td>\n",
+       "      <td>2835.62</td>\n",
+       "      <td>Professional</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   customer_id  revenue customer_segment\n",
+       "0           62  4448.39           Senior\n",
+       "1           24  3369.44     Professional\n",
+       "2           90  2976.78           Senior\n",
+       "3           92  2838.74     Professional\n",
+       "4           97  2835.62     Professional"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "top5 = data.groupby('customer_id').agg({'revenue': 'sum','customer_segment': 'first'}).sort_values(by= 'revenue',ascending=False).reset_index()\n",
+    "top5.head()\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ce59b98",
+   "metadata": {},
+   "source": [
+    "### 3. Segment Analysis\n",
+    "Which customer segment (Student, Professional, Senior) spends the most on average per order?\n",
+    "\n",
+    "Seniors have the highest average spend at about $262, Professionals are around $256, and Students are about $254. The gap is small, so I would keep the core offer the same  to all maybe bumping up promos and advertising to studesnts to pick the up from the bottom."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6fa0576d",
+   "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>customer_segment</th>\n",
+       "      <th>revenue</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Senior</td>\n",
+       "      <td>262.133925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Professional</td>\n",
+       "      <td>255.652674</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Student</td>\n",
+       "      <td>253.812087</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  customer_segment     revenue\n",
+       "0           Senior  262.133925\n",
+       "1     Professional  255.652674\n",
+       "2          Student  253.812087"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "segment_avg = data.groupby('customer_segment')['revenue'].mean().sort_values(ascending=False).reset_index()\n",
+    "segment_avg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "507ae942",
+   "metadata": {},
+   "source": [
+    "### 4. Regional Trends\n",
+    "Which region generated the highest revenue? Which region had the highest average order size?\n",
+    "\n",
+    "South and North lead on total revenue which the south being marginally higher, so that is where we keep pushing. West has the largest average order size but still trails on the revenue which points to lower priced items bought or heavier discounting. I would keep the premium inventory tight in South and North and test price mix in the west to turn those bigger carts into higher revenue."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "91f5071a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Highest Region Revenue:\n",
+      "  region   revenue\n",
+      "0  South  34839.45\n",
+      "1  North  34351.69\n",
+      "2   East  31221.80\n",
+      "3   West  27727.73\n",
+      "Highest Region Average Order Size:\n",
+      "  region  quantity\n",
+      "0   West  2.627273\n",
+      "1   East  2.469231\n",
+      "2  South  2.440945\n",
+      "3  North  2.360902\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "top_region = data.groupby(\"region\")[\"revenue\"].sum().sort_values(ascending=False).reset_index()\n",
+    "top_region_avg = data.groupby(\"region\")[\"quantity\"].mean().sort_values(ascending=False).reset_index()\n",
+    "print(f\"Highest Region Revenue:\\n{top_region}\\nHighest Region Average Order Size:\\n{top_region_avg}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b7e51d1",
+   "metadata": {},
+   "source": [
+    "### 5. Product Category Performance\n",
+    "Which product category is most popular by quantity vs. by revenue?\n",
+    "\n",
+    "Home products lead on both revenue and units sold so we should keep it stocked. Electronics ranks high on revenue with fewer unit which says higher ticket items sre selling so we shoulf lean into premium product promotion and warranties. Books are light on both so we can treat it as fill in inventory or pair it as an add on in bundles with the top sellers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1041c72c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Products Sorted by Revenue\n",
+      "  product_category   revenue\n",
+      "0             Home  29871.47\n",
+      "1      Electronics  25498.31\n",
+      "2         Clothing  24804.57\n",
+      "3            Books  24046.06\n",
+      "4           Sports  23920.26 \n",
+      "\n",
+      "\n",
+      "Products Sorted by Quantity\n",
+      "  product_category  quantity\n",
+      "0             Home       269\n",
+      "1         Clothing       253\n",
+      "2           Sports       246\n",
+      "3      Electronics       239\n",
+      "4            Books       227\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1600x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "p_rev = data.groupby('product_category')['revenue'].sum().sort_values(ascending=False).reset_index()\n",
+    "p_quant = data.groupby('product_category')['quantity'].sum().sort_values(ascending=False).reset_index()\n",
+    "print(\"Products Sorted by Revenue\")\n",
+    "print(p_rev,\"\\n\\n\")\n",
+    "print(\"Products Sorted by Quantity\")\n",
+    "print(p_quant)\n",
+    "\n",
+    "plt.figure(figsize=(16,6))\n",
+    "\n",
+    "plt.subplot(1,2,1)\n",
+    "sns.barplot(data=p_rev, x = \"product_category\", y = \"revenue\")\n",
+    "plt.title(\"Revenue of Products\")\n",
+    "plt.xlabel(\"Products\")\n",
+    "plt.ylabel(\"Revenue\")\n",
+    "plt.xticks(rotation = 45)\n",
+    "\n",
+    "plt.subplot(1,2,2)\n",
+    "sns.barplot(data=p_quant, x = \"product_category\", y = \"quantity\")\n",
+    "plt.title(\"Quantity of Products\")\n",
+    "plt.xlabel(\"Products\")\n",
+    "plt.ylabel(\"Quantity\")\n",
+    "plt.xticks(rotation = 45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc82f78d",
+   "metadata": {},
+   "source": [
+    "### 6. Discount Effectiveness\n",
+    "Do discounted orders generate higher or lower revenue per order compared to non-discounted orders?\n",
+    "\n",
+    "Discounted orders bring in less revenue per order. Net average revenue is about $222 vs $248 for non discounted items so we’re giving up money even though quantity per order is a touch higher. There are more discounted orders overall but total net is still lower than the non discounted side. We should use discounts only when we need to clear old stock."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "7cb097dc",
+   "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>discounted</th>\n",
+       "      <th>net_sum</th>\n",
+       "      <th>net_mean</th>\n",
+       "      <th>avg_quantity</th>\n",
+       "      <th>unit_price</th>\n",
+       "      <th>order_amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>False</td>\n",
+       "      <td>60336.0100</td>\n",
+       "      <td>248.296337</td>\n",
+       "      <td>2.436214</td>\n",
+       "      <td>101.364733</td>\n",
+       "      <td>243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>True</td>\n",
+       "      <td>56935.1155</td>\n",
+       "      <td>221.537414</td>\n",
+       "      <td>2.498054</td>\n",
+       "      <td>105.556576</td>\n",
+       "      <td>257</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   discounted     net_sum    net_mean  avg_quantity  unit_price  order_amount\n",
+       "0       False  60336.0100  248.296337      2.436214  101.364733           243\n",
+       "1        True  56935.1155  221.537414      2.498054  105.556576           257"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "data['discounted'] = data['discount_applied'] > 0\n",
+    "data['net_revenue'] = data['revenue'] * (1 - (data['discount_applied'].astype(float)/100.0))\n",
+    "\n",
+    "data.groupby('discounted').agg(net_sum=('net_revenue','sum'), net_mean=('net_revenue', 'mean'), avg_quantity=('quantity', 'mean'), unit_price=('unit_price', 'mean'), order_amount=('order_id', 'count')).reset_index()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a495feba",
+   "metadata": {},
+   "source": [
+    "### 7. Payment Method Usage\n",
+    "What percentage of orders use each payment method? Does any payment method correlate with higher spending?\n",
+    "\n",
+    "The payment mix is pretty even but credit a little ahead on usage. Debit cards shows the highest average and median revenue with lower usage so it’s worth looking in to different methods to increase debit usage something like a “Recommended” next to debit or list debit first in the payment options but keep others visible. Gift card sits in the middle, PayPal is lowest.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "64b6f2b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  payment_method  amount_used  revenue_mean  revenue_median\n",
+      "0    Credit Card          137    257.444234         186.630\n",
+      "1      Gift Card          124    254.362500         215.750\n",
+      "2     Debit Card          123    275.980650         234.160\n",
+      "3         PayPal          116    236.071034         164.945\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "\n",
+    "print(data.groupby('payment_method').agg( amount_used=(\"order_id\", \"count\"), revenue_mean=(\"revenue\", \"mean\"), revenue_median=('revenue',\"median\")).sort_values(by=\"amount_used\", ascending=False).reset_index())\n",
+    "pay_counts = data['payment_method'].value_counts()\n",
+    "plt.figure(figsize=(14,7))\n",
+    "pay_counts.plot(kind='pie', autopct = lambda p:f'{p:.2f}%, {p*pay_counts.sum()/100: .0f} Payments')\n",
+    "plt.title(\"Percentages of Different Payment Methods\")\n",
+    "plt.ylabel(\"\")\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a525cd28",
+   "metadata": {},
+   "source": [
+    "### 8. Seasonality Check\n",
+    "Plot revenue by day of week — are weekends busier than weekdays?\n",
+    "\n",
+    "Weekdays are higher than the the weekends but not by much theres not a significant spike in either direction for any of the days. Wednesdays and Tuesdays are the highest averges and overall revenue sums, so it might be worth it to considering pushing promos for the middle of the week."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "594adf29",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  day_of_week     r_sum  buy_counts      r_mean\n",
+      "6   Wednesday  18908.21          71  266.312817\n",
+      "5     Tuesday  18968.41          72  263.450139\n",
+      "2    Saturday  18660.35          71  262.821831\n",
+      "0      Friday  18522.40          71  260.878873\n",
+      "4    Thursday  17846.36          71  251.357183\n",
+      "1      Monday  17704.70          72  245.898611\n",
+      "3      Sunday  17530.24          72  243.475556\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "day_order = [\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\",\"Sunday\"]\n",
+    "dow = data.groupby('day_of_week').agg(r_sum=('revenue', 'sum'),buy_counts=('order_id', 'count'), r_mean=('revenue', 'mean') ).reset_index().sort_values(by='r_mean', ascending=False)\n",
+    "print(dow)\n",
+    "\n",
+    "plt.figure(figsize=(12,6))\n",
+    "sns.barplot(data=dow,x= 'day_of_week', y='r_sum', palette='dark:#5A9_r', hue='day_of_week', legend=False, order=day_order)\n",
+    "plt.title(\"Revneue By Each Day Of The Week \")\n",
+    "plt.xlabel(\"Days Of The Week\")\n",
+    "plt.ylabel(\"Revenue Total\")\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ec1d6f8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08ae2706",
+   "metadata": {},
+   "source": [
+    "### 9. Profitability (Stretch)\n",
+    "Assume profit margin = 20% of revenue. Estimate total profit and identify the most profitable region.\n",
+    "\n",
+    "With the 20 percent margin the profit mirrors revenue charts. The south is the most profitable, north is next, then east, with west last. This shows where the money are concentrated right now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "490e9ee2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "data['profit'] = data['revenue'] * 0.2\n",
+    "region_profit = data.groupby('region')['profit'].sum().sort_values(ascending=False).reset_index()\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "sns.barplot(data=region_profit, x=\"region\", y=\"profit\")\n",
+    "plt.title(\"Estimated Profit by Region (20% Margin)\")\n",
+    "plt.xlabel(\"Region\")\n",
+    "plt.ylabel(\"Profit\")\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "259fe6f7",
+   "metadata": {},
+   "source": [
+    "### 10. Business Recommendation\n",
+    "Based on your findings, recommend one strategy (e.g., focus on certain customer segments, increase discounts, promote specific regions/products)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9415b35d",
+   "metadata": {},
+   "source": [
+    "**Your recommendation here:**\n",
+    "\n",
+    "The South and North are already bringing in the most revenue with fewer items, so that’s where we should push harder and keep high value items in stock, we could add a couple bundle offers and try to bump up the quantity without leaning on discounts. Discounts don’t seem worth it overall and unless it’s stale inventory we should only be using them just to clear space. Payment methods are pretty even, but debit has a higher average order this is worth a small checkout nudge and see if it sticks. Weekday vs weekend looks flat, and with a 20% margin, profit also says double down on South/North."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d86796a",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## Submission Requirements\n",
+    "\n",
+    "1. **Complete code** for all 10 questions\n",
+    "2. **Clear visualizations** where appropriate  \n",
+    "3. **Brief interpretation** of each result\n",
+    "4. **Final business recommendation** with supporting data\n",
+    "\n",
+    "Remember: Focus on what the numbers tell us about the business, not just the calculations themselves."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}