PYF_Project_FoodHubProject_RobBarker_FullCode.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gc8x8QimJY_H"
   },
   "source": [
    "# Project Python Foundations: FoodHub Data Analysis\n",
    "\n",
    "| Project Submission| Date |\n",
    "| --- | --- |\n",
    "| Rob Barker | June 7, 2024 | "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NqikjkWwJY_L"
   },
   "source": [
    "### Context\n",
    "\n",
    "The number of restaurants in New York is increasing day by day. Lots of students and busy professionals rely on those restaurants due to their hectic lifestyles. Online food delivery service is a great option for them. It provides them with good food from their favorite restaurants. A food aggregator company FoodHub offers access to multiple restaurants through a single smartphone app.\n",
    "\n",
    "The app allows the restaurants to receive a direct online order from a customer. The app assigns a delivery person from the company to pick up the order after it is confirmed by the restaurant. The delivery person then uses the map to reach the restaurant and waits for the food package. Once the food package is handed over to the delivery person, he/she confirms the pick-up in the app and travels to the customer's location to deliver the food. The delivery person confirms the drop-off in the app after delivering the food package to the customer. The customer can rate the order in the app. The food aggregator earns money by collecting a fixed margin of the delivery order from the restaurants.\n",
    "\n",
    "### Objective\n",
    "\n",
    "The food aggregator company has stored the data of the different orders made by the registered customers in their online portal. They want to analyze the data to get a fair idea about the demand of different restaurants which will help them in enhancing their customer experience. Suppose you are hired as a Data Scientist in this company and the Data Science team has shared some of the key questions that need to be answered. Perform the data analysis to find answers to these questions that will help the company to improve the business.\n",
    "\n",
    "### Data Description\n",
    "\n",
    "The data contains the different data related to a food order. The detailed data dictionary is given below.\n",
    "\n",
    "### Data Dictionary\n",
    "\n",
    "* order_id: Unique ID of the order\n",
    "* customer_id: ID of the customer who ordered the food\n",
    "* restaurant_name: Name of the restaurant\n",
    "* cuisine_type: Cuisine ordered by the customer\n",
    "* cost_of_the_order: Cost of the order\n",
    "* day_of_the_week: Indicates whether the order is placed on a weekday or weekend (The weekday is from Monday to Friday and the weekend is Saturday and Sunday)\n",
    "* rating: Rating given by the customer out of 5\n",
    "* food_preparation_time: Time (in minutes) taken by the restaurant to prepare the food. This is calculated by taking the difference between the timestamps of the restaurant's order confirmation and the delivery person's pick-up confirmation.\n",
    "* delivery_time: Time (in minutes) taken by the delivery person to deliver the food package. This is calculated by taking the difference between the timestamps of the delivery person's pick-up confirmation and drop-off information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cEtNUxNAJY_M"
   },
   "source": [
    "---\n",
    "## Let us start by importing the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "_aG-ZzhYXs_1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# Installing the libraries with the specified version.\n",
    "#!pip install numpy==1.25.2 pandas==1.5.3 matplotlib==3.7.1 seaborn==0.13.1 -q --user\n",
    "\n",
    "# Visual Studio Code implementation using %pip.\n",
    "%pip install numpy==1.25.2 pandas==1.5.3 matplotlib==3.7.1 seaborn==0.13.1 -q --user "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "u6PlpJAPJY_N"
   },
   "outputs": [],
   "source": [
    "# import libraries for data manipulation.\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# import libraries for data visualization.\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Development Environment\n",
    "* Local development with Visual Studio Code.\n",
    "* Jupyter Notebook and Python 3.11.7 with Anaconda3. \n",
    "* Google Colab/Drive not used.\n",
    "* Generated HTML using the jupyter cli\n",
    "\n",
    "   ```jupyter nbconvert --to html PYF_Project_LearnerNotebook_FullCode.ipynb```\n",
    "* Added --- (markdown) lines for easier readability for myself. \n",
    "\n",
    "### Formatting Notes\n",
    "* Added line separators for readability."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AdracY0HJY_O"
   },
   "source": [
    "---\n",
    "## Understanding the structure of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "GF01IZDbJY_O",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Read the dataset from the foodhub_order.csv file. Pandas DataFrame object assigned to foodhub_df.\n",
    "foodhub_df = pd.read_csv(\"/Users/robbarker/Desktop/FoodHub Project/foodhub_order.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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>restaurant_name</th>\n",
       "      <th>cuisine_type</th>\n",
       "      <th>cost_of_the_order</th>\n",
       "      <th>day_of_the_week</th>\n",
       "      <th>rating</th>\n",
       "      <th>food_preparation_time</th>\n",
       "      <th>delivery_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1477147</td>\n",
       "      <td>337525</td>\n",
       "      <td>Hangawi</td>\n",
       "      <td>Korean</td>\n",
       "      <td>30.75</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>Not given</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1477685</td>\n",
       "      <td>358141</td>\n",
       "      <td>Blue Ribbon Sushi Izakaya</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>12.08</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>Not given</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1477070</td>\n",
       "      <td>66393</td>\n",
       "      <td>Cafe Habana</td>\n",
       "      <td>Mexican</td>\n",
       "      <td>12.23</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>5</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1477334</td>\n",
       "      <td>106968</td>\n",
       "      <td>Blue Ribbon Fried Chicken</td>\n",
       "      <td>American</td>\n",
       "      <td>29.20</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>3</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1478249</td>\n",
       "      <td>76942</td>\n",
       "      <td>Dirty Bird to Go</td>\n",
       "      <td>American</td>\n",
       "      <td>11.59</td>\n",
       "      <td>Weekday</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   order_id  customer_id            restaurant_name cuisine_type  \\\n",
       "0   1477147       337525                    Hangawi       Korean   \n",
       "1   1477685       358141  Blue Ribbon Sushi Izakaya     Japanese   \n",
       "2   1477070        66393                Cafe Habana      Mexican   \n",
       "3   1477334       106968  Blue Ribbon Fried Chicken     American   \n",
       "4   1478249        76942           Dirty Bird to Go     American   \n",
       "\n",
       "   cost_of_the_order day_of_the_week     rating  food_preparation_time  \\\n",
       "0              30.75         Weekend  Not given                     25   \n",
       "1              12.08         Weekend  Not given                     25   \n",
       "2              12.23         Weekday          5                     23   \n",
       "3              29.20         Weekend          3                     25   \n",
       "4              11.59         Weekday          4                     25   \n",
       "\n",
       "   delivery_time  \n",
       "0             20  \n",
       "1             23  \n",
       "2             28  \n",
       "3             15  \n",
       "4             24  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve first 5 rows of the dataset.\n",
    "foodhub_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c3kenV4vJY_Q"
   },
   "source": [
    "---\n",
    "### **Question 1:** How many rows and columns are present in the data? [0.5 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "Yvz5i-PhJY_Q"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Rows: 1,898\n",
      "Number of Columns: 9\n"
     ]
    }
   ],
   "source": [
    "# Retrieve number of rows and columns using the shape attribute of the DataFrame.\n",
    "rows, columns = foodhub_df.shape\n",
    "\n",
    "# Print the number of rows and columns from the dataset. Output is formatted into thousands. \n",
    "# There are only 9 columns but for consistency, the output includes (,) format.\n",
    "print(f'Number of Rows: {rows:,}')\n",
    "print(f'Number of Columns: {columns:,}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UsTNy3unJY_R"
   },
   "source": [
    "#### Observations:\n",
    "The number of rows and columns in the DataFrame, foodhub_df, are:\n",
    "* 1,898 rows\n",
    "* 9 columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jRXakVrUJY_R"
   },
   "source": [
    "---\n",
    "### **Question 2:** What are the datatypes of the different columns in the dataset? (The info() function can be used) [0.5 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1898 entries, 0 to 1897\n",
      "Data columns (total 9 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   order_id               1898 non-null   int64  \n",
      " 1   customer_id            1898 non-null   int64  \n",
      " 2   restaurant_name        1898 non-null   object \n",
      " 3   cuisine_type           1898 non-null   object \n",
      " 4   cost_of_the_order      1898 non-null   float64\n",
      " 5   day_of_the_week        1898 non-null   object \n",
      " 6   rating                 1898 non-null   object \n",
      " 7   food_preparation_time  1898 non-null   int64  \n",
      " 8   delivery_time          1898 non-null   int64  \n",
      "dtypes: float64(1), int64(4), object(4)\n",
      "memory usage: 133.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# Retrieve summary information of the DataFrame using the info() method.\n",
    "foodhub_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "Using the DataFrame info() method the following observations can be made:\n",
    "\n",
    "| Information | Food Hub DataFrame | \n",
    "| -------- | -------- | \n",
    "| Number of rows and columns. | 1,898 rows and 9 columns. | \n",
    "| Column names and corresponding data types. | 1 float64, 4 int64, 4 object | \n",
    "| Number of non-null values in each column. foodhubdf is complete with no non-null values. | Non-Null Count indicates a full dataset. | \n",
    "| Memory usage of the DataFrame | 133.6+ KB | "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pF_92MN6JY_S"
   },
   "source": [
    "---\n",
    "### **Question 3:** Are there any missing values in the data? If yes, treat them using an appropriate method. [1 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "Zth-RR1PJY_S"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are no missing data points in the Food Hub Project dataset.\n"
     ]
    }
   ],
   "source": [
    "# Determine if there is any missing data.\n",
    "missing_values = foodhub_df.isnull().sum()\n",
    "\n",
    "# Output if there are any missing data points in the dataset.\n",
    "if missing_values.sum() > 0:\n",
    "    print(\"There are missing data points in the Food Hub Project dataset.\")\n",
    "else:\n",
    "    print(\"There are no missing data points in the Food Hub Project dataset.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jDHT3vD1JY_S"
   },
   "source": [
    "#### Observations:\n",
    "There are no missing data points in the Food Hub Project dataset. With no missing data in the Food Hub Project dataset this is beneficial for analysis and modeling to visualize a more accurate and comprehensive investigation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MnJo7MWrJY_S"
   },
   "source": [
    "---\n",
    "### **Question 4:** Check the statistical summary of the data. What is the minimum, average, and maximum time it takes for food to be prepared once an order is placed? [2 marks]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " There are several different methods that can be used to view the statistical information of a dataset. \n",
    "* The describe() method for each statistic of the dataset.\n",
    "* The describe() method with .T for an easier readable view.\n",
    "* View specific methods for min, mean and max.\n",
    "\n",
    "All are demonstrated below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <td>1.898000e+03</td>\n",
       "      <td>1898.000000</td>\n",
       "      <td>1898.000000</td>\n",
       "      <td>1898.000000</td>\n",
       "      <td>1898.000000</td>\n",
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       "      <th>mean</th>\n",
       "      <td>1.477496e+06</td>\n",
       "      <td>171168.478398</td>\n",
       "      <td>16.498851</td>\n",
       "      <td>27.371970</td>\n",
       "      <td>24.161749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.480497e+02</td>\n",
       "      <td>113698.139743</td>\n",
       "      <td>7.483812</td>\n",
       "      <td>4.632481</td>\n",
       "      <td>4.972637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.476547e+06</td>\n",
       "      <td>1311.000000</td>\n",
       "      <td>4.470000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.477021e+06</td>\n",
       "      <td>77787.750000</td>\n",
       "      <td>12.080000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.477496e+06</td>\n",
       "      <td>128600.000000</td>\n",
       "      <td>14.140000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.477970e+06</td>\n",
       "      <td>270525.000000</td>\n",
       "      <td>22.297500</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.478444e+06</td>\n",
       "      <td>405334.000000</td>\n",
       "      <td>35.410000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           order_id    customer_id  cost_of_the_order  food_preparation_time  \\\n",
       "count  1.898000e+03    1898.000000        1898.000000            1898.000000   \n",
       "mean   1.477496e+06  171168.478398          16.498851              27.371970   \n",
       "std    5.480497e+02  113698.139743           7.483812               4.632481   \n",
       "min    1.476547e+06    1311.000000           4.470000              20.000000   \n",
       "25%    1.477021e+06   77787.750000          12.080000              23.000000   \n",
       "50%    1.477496e+06  128600.000000          14.140000              27.000000   \n",
       "75%    1.477970e+06  270525.000000          22.297500              31.000000   \n",
       "max    1.478444e+06  405334.000000          35.410000              35.000000   \n",
       "\n",
       "       delivery_time  \n",
       "count    1898.000000  \n",
       "mean       24.161749  \n",
       "std         4.972637  \n",
       "min        15.000000  \n",
       "25%        20.000000  \n",
       "50%        25.000000  \n",
       "75%        28.000000  \n",
       "max        33.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve full statistics of the dataset.\n",
    "foodhub_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "      <td>1476547.0</td>\n",
       "      <td>1477021.25</td>\n",
       "      <td>1477495.5</td>\n",
       "      <td>1477969.75</td>\n",
       "      <td>1478444.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customer_id</th>\n",
       "      <td>1898.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>171168.478398</td>\n",
       "      <td>113698.139743</td>\n",
       "      <td>1311.0</td>\n",
       "      <td>77787.75</td>\n",
       "      <td>128600.0</td>\n",
       "      <td>270525.0</td>\n",
       "      <td>405334.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restaurant_name</th>\n",
       "      <td>1898</td>\n",
       "      <td>178</td>\n",
       "      <td>Shake Shack</td>\n",
       "      <td>219</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cuisine_type</th>\n",
       "      <td>1898</td>\n",
       "      <td>14</td>\n",
       "      <td>American</td>\n",
       "      <td>584</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cost_of_the_order</th>\n",
       "      <td>1898.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.498851</td>\n",
       "      <td>7.483812</td>\n",
       "      <td>4.47</td>\n",
       "      <td>12.08</td>\n",
       "      <td>14.14</td>\n",
       "      <td>22.2975</td>\n",
       "      <td>35.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_of_the_week</th>\n",
       "      <td>1898</td>\n",
       "      <td>2</td>\n",
       "      <td>Weekend</td>\n",
       "      <td>1351</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rating</th>\n",
       "      <td>1898</td>\n",
       "      <td>4</td>\n",
       "      <td>Not given</td>\n",
       "      <td>736</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>food_preparation_time</th>\n",
       "      <td>1898.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.37197</td>\n",
       "      <td>4.632481</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>delivery_time</th>\n",
       "      <td>1898.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.161749</td>\n",
       "      <td>4.972637</td>\n",
       "      <td>15.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        count unique          top  freq           mean  \\\n",
       "order_id               1898.0    NaN          NaN   NaN      1477495.5   \n",
       "customer_id            1898.0    NaN          NaN   NaN  171168.478398   \n",
       "restaurant_name          1898    178  Shake Shack   219            NaN   \n",
       "cuisine_type             1898     14     American   584            NaN   \n",
       "cost_of_the_order      1898.0    NaN          NaN   NaN      16.498851   \n",
       "day_of_the_week          1898      2      Weekend  1351            NaN   \n",
       "rating                   1898      4    Not given   736            NaN   \n",
       "food_preparation_time  1898.0    NaN          NaN   NaN       27.37197   \n",
       "delivery_time          1898.0    NaN          NaN   NaN      24.161749   \n",
       "\n",
       "                                 std        min         25%        50%  \\\n",
       "order_id                  548.049724  1476547.0  1477021.25  1477495.5   \n",
       "customer_id            113698.139743     1311.0    77787.75   128600.0   \n",
       "restaurant_name                  NaN        NaN         NaN        NaN   \n",
       "cuisine_type                     NaN        NaN         NaN        NaN   \n",
       "cost_of_the_order           7.483812       4.47       12.08      14.14   \n",
       "day_of_the_week                  NaN        NaN         NaN        NaN   \n",
       "rating                           NaN        NaN         NaN        NaN   \n",
       "food_preparation_time       4.632481       20.0        23.0       27.0   \n",
       "delivery_time               4.972637       15.0        20.0       25.0   \n",
       "\n",
       "                              75%        max  \n",
       "order_id               1477969.75  1478444.0  \n",
       "customer_id              270525.0   405334.0  \n",
       "restaurant_name               NaN        NaN  \n",
       "cuisine_type                  NaN        NaN  \n",
       "cost_of_the_order         22.2975      35.41  \n",
       "day_of_the_week               NaN        NaN  \n",
       "rating                        NaN        NaN  \n",
       "food_preparation_time        31.0       35.0  \n",
       "delivery_time                28.0       33.0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the describe() function with (include-'all').T can make the data more readable.\n",
    "foodhub_df.describe(include='all').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "id": "WprQBgOSJY_T"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Food preparation time statistics in minutes:\n",
      "Minimum: 20\n",
      "Average: 27.371970495258168\n",
      "Maximum: 35\n"
     ]
    }
   ],
   "source": [
    "# Retrieve the statisical summary of the data.\n",
    "foodprep_min_value = foodhub_df['food_preparation_time'].min()\n",
    "foodprep_avg_value = foodhub_df['food_preparation_time'].mean()\n",
    "foodprep_max_value = foodhub_df['food_preparation_time'].max()\n",
    "\n",
    "print('Food preparation time statistics in minutes:')\n",
    "print('Minimum:', foodprep_min_value)\n",
    "print('Average:', foodprep_avg_value)\n",
    "print('Maximum:', foodprep_max_value)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9YbSOR5wJY_T"
   },
   "source": [
    "#### Observations:\n",
    "Full details are provided to understand all of the different data points in the dataset for general statistics. In some cases this may be too much information to consume easily. As the output illustrates below it is not so ease to quickly find the minimum, average and maximum information for food preparation time (food_preparation_time) without spending time looking at the columns and rows.\n",
    "\n",
    "| order_id | customer_id | cost_of_the_order | food_preparation_time | delivery_time\n",
    "| -------- | -------- | -------- | -------- | -------- |\n",
    "| count\t| 1.898000e+03\t| 1898.000000\t| 1898.000000\t|1898.000000\t|1898.000000\n",
    "| mean\t| 1.477496e+06\t| 171168.478398\t| 16.498851\t| 27.371970\t| 24.161749\n",
    "| std\t| 5.480497e+02\t| 113698.139743\t| 7.483812\t| 4.632481\t| 4.972637\n",
    "| min\t| 1.476547e+06\t| 1311.000000\t| 4.470000\t| 20.000000\t| 15.000000\n",
    "| 25%\t| 1.477021e+06\t| 77787.750000\t| 12.080000\t| 23.000000\t| 20.000000\n",
    "| 50%\t| 1.477496e+06\t| 128600.000000\t| 14.140000\t| 27.000000\t| 25.000000\n",
    "| 75%\t| 1.477970e+06\t| 270525.000000\t| 22.297500\t| 31.000000\t| 28.000000\n",
    "| max\t| 1.478444e+06\t| 405334.000000\t| 35.410000\t| 35.000000\t| 33.000000\n",
    "\n",
    "Retrieving statistical information for a column is valuable to understand a specific data point quickly.\n",
    "\n",
    "The statistical summary for food preparation (food_preparation_time) is as follows:\n",
    "* Minimum time is 20 minutes\n",
    "* Average time is 27.3 minutes\n",
    "* Maximum time is 35 minutes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cw3l58z_JY_T"
   },
   "source": [
    "---\n",
    "### **Question 5:** How many orders are not rated? [1 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "id": "hEzDUL9wgksi"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of orders not rated are 736\n",
      "Unrated orders have a 38.78% impact on the dataset.\n"
     ]
    }
   ],
   "source": [
    "# Count the number of orders that have not been rated (rating = 'Not given').\n",
    "no_ratings = foodhub_df['rating'].value_counts()['Not given']\n",
    "\n",
    "# Print the number of orders that have not been rated.\n",
    "print('The number of orders not rated are ' + str(no_ratings))\n",
    "\n",
    "# Calculate the percentage of orders that have not been rated.\n",
    "no_ratings_impact = round((no_ratings/rows)*100, 2)\n",
    "print(f'Unrated orders have a {no_ratings_impact}% impact on the dataset.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5E0O_oviJY_T"
   },
   "source": [
    "#### Observations:\n",
    "The number of orders not rated is 736 out of 1,898. The immediate observation that can be made is unrated orders could be impacting a restaurants ability to understand if there are quality issues, being able to offer special offers for repeat and/or new customers. To get more accurate rating data a recommendation is to incentivize customers to submit ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7msIfXE-JY_T"
   },
   "source": [
    "---\n",
    "# Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-2xfI28YJY_T"
   },
   "source": [
    "## Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0RHZnugBJY_U"
   },
   "source": [
    "---\n",
    "### **Question 6:** Explore all the variables and provide observations on their distributions. (Generally, histograms, boxplots, countplots, etc. are used for univariate exploration.) [9 marks]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Data Dictionary Reference\n",
    "| Column | Definition |\n",
    "| ------ | ---------- |\n",
    "| order_id | Unique ID of the order |\n",
    "| customer_id | ID of the customer who ordered the food |\n",
    "| restaurant_name | Name of the restaurant |\n",
    "| cuisine_type | Cuisine ordered by the customer |\n",
    "| cost_of_the_order | Cost of the order |\n",
    "| day_of_the_week | Indicates whether the order is placed on a weekday or weekend (The weekday is from Monday to Friday and the weekend is Saturday and Sunday) |\n",
    "| rating | Rating given by the customer out of 5 |\n",
    "| food_preparation_time | Time (in minutes) taken by the restaurant to prepare the food. This is calculated by taking the difference between the timestamps of the restaurant's order confirmation and the delivery person's pick-up confirmation. |\n",
    "| delivery_time | Time (in minutes) taken by the delivery person to deliver the food package. This is calculated by taking the difference between the timestamps |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Order ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1,898.\n"
     ]
    }
   ],
   "source": [
    "# Check for the unique orders in the dataset.\n",
    "print(f'There are {foodhub_df[\"order_id\"].nunique():,}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customer ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1,200 customers.\n"
     ]
    }
   ],
   "source": [
    "# Check for unique customers in the dataset.\n",
    "customer_ids = foodhub_df['customer_id'].nunique()\n",
    "print(f'There are {customer_ids:,} customers.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Restaurant Name\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 178 restaurants.\n"
     ]
    }
   ],
   "source": [
    "# Check for unique restaurants in the dataset.\n",
    "restaurants = foodhub_df['restaurant_name'].nunique()\n",
    "print(f'There are {restaurants:,} restaurants.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cuisine Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 14 cuisines.\n"
     ]
    }
   ],
   "source": [
    "# Check unique cuisine types in the dataset.\n",
    "cuisine_types = foodhub_df['cuisine_type'].nunique()\n",
    "print(f'There are {cuisine_types:,} cuisines.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create countplot for the different cuisine types.\n",
    "plt.figure(figsize = (15,5))\n",
    "sns.countplot(data = foodhub_df, x = 'cuisine_type')\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the output.\n",
    "plt.title('Countplot of Cuisine Types', fontsize=12, fontweight='bold')\n",
    "plt.xlabel('Cuisine Type', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Number', fontsize=10, fontweight='bold')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "# Show plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cost of the Order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram for the 'cost_of_order' column.\n",
    "plt.figure(figsize=(10, 4))\n",
    "sns.histplot(foodhub_df['cost_of_the_order'], bins=30, edgecolor='black', kde=True)\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the barchart output.\n",
    "plt.title('Histogram for Costs of the Orders', fontsize=12, fontweight='bold')\n",
    "plt.xlabel('Cost of the Order', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Frequency', fontsize=10, fontweight='bold')\n",
    "\n",
    "# Show to plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RI9qyZYsWL16st99+u8wH9vXu3Vu33HKLli9fLkkaPHiwJkyYoOuvv17GGP33v//Vm2++qb/85S966KGHNGLECM2aNUtSwbNkoqOj1axZM7388svWX9379OljnWbUpEkTDR48WO3bt1d0dLROnjxpfX5jjLKysuTv71+pdXq+v+vKGDt2rGbPnq0DBw4oISFBPXv21GOPPaaIiAitXLlSL7zwgtU2NjZWPj4+Feq3f//+evTRRyVJn3zyiaZPn66OHTtq9uzZJR6tGjlypF555RVJ0sSJE5WYmKh27dopKSlJe/fu1cqVKxUTE6O33367Qsvv2rWrOnTooE6dOqlu3bpKTU3Vtm3brOklHUEBcJlwwXUmAKq48i44l2Suuuoqpwunz+dWu6NHjy52EXLRC2T//Oc/W22LXtha9O5Uha+oqChz8uRJq31pt9odNmxYqZ+l6K12jXG+uLnoa/Xq1RX+3s69vWplb7Vb9DkrFfHXv/61WP89e/Yst9/C8UUvlD5z5kyZt9qtyHdijDHJycmmd+/eZfZTeLG9Med3q113d/dS2/bp08dqV9l1ej6/69K+x/Js2rTJ1K9fv8zv56GHHnKapyK/kfvvv79YP76+vqZu3brW+4reavfc5ZT1rBBjnO/Qde6rdevWJjc3t8LfD4CahfABoJjSwoevr69p06aNeeKJJ0xycnKx+b7++mvTr18/ExISYjw8PEx0dLS56667nB7Gtnz5cqu/rl27WneYys3NNX/4wx+snfHCW8oW3cnZunWrmTBhggkPDze+vr6mX79+1sMIC5V2t6e8vDwzb94806lTJ+Pv72+8vb1N8+bNzWOPPeb0kMFCX375penQoYPTHbkuJHwYY8yuXbvMqFGjTN26dY2Hh4epXbu26du3r9OzNYy5sPCRkpJixo4da6Kjo62d5sqGD2OMSUtLM9OnTzft27c3vr6+xs/PzzRr1swMGTLEvP/++9ZD78qTn59vFi1aZG699VYTGRlpPD09TUREhOnSpYt59tlnnQKkMcZ89NFHpmfPniYoKMh4enqahg0blviQwWeffdb06dPH1KtXz3h7e1sPj3z44YdNSkqKU9vKrFNjKva7Lu97LE9SUpKZPn266dixowkMDDSenp6mbt26ZujQoSXWWJHfSE5Ojpk6daqJiooyPj4+pmvXruaHH34oMzh8/vnnpm/fvtYDFevWrWu6detmnnnmGac70ZUXPt58800zcOBAExMTY/z8/Kx1eP/995vjx4+f13cDoGZxGHOeVxcCgI169eqltWvXSpL279+vhg0burYgAABQaVxwDgAAAMAWhA8AAAAAtiB8AAAAALAF13wAAAAAsAVHPgAAAADYgvABAAAAwBaVfsJ5fn6+jh49qsDAQOupwAAAAAAuP8YYpaamKjo6Wm5upR/fqHT4OHr0qOrXr1/Z2QEAAADUMIcPH1a9evVKnV7p8BEYGGgtICgoqLLdAAAAAKjmUlJSVL9+fSsjlKbS4aPwVKugoCDCBwAAAIByL8fggnMAAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALTxcXQBwuTpx4oSSk5NdXQaAUgQHB6tOnTquLgMAahTCB+ACJ06c0B//313Kyc5ydSkASuHp5a2F/36XAAIAFxHhA3CB5ORk5WRn6Wzjnsr3CXZ1OefF7WySfPev09lGPZTvW8vV5QCXhFtmsrRvrZKTkwkfAHARET4AF8r3CVa+f5iry6iUfN9a1bZ2AADgGlxwDgAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC28HB1AUBNlZycrIyMjBKnnTx5UllZWcpOT5Ex1WszdGSkyKOa1l4Rbp5ecvf2c3UZAADUSDVvzwGoApKTkzV79mzl5OSUOt0YI4/jWyUPb5uru0C5WdW39gpIy86Xb6seBBAAAC4BwgdwCWRkZCgnJ0e33367wsPDi01PTExUTEyMckKbyHj4uqDCynPknpVnwt5qWXt5UpPP6ItlnyonJ5vwAQDAJUD4AC6h8PBwRUVFFRvv5eWlWrVqKTskTMazeu3kOnIy5JWXUC1rBwAArsUF5wAAAABsQfgAAAAAYAvCBwAAAABbED6AGirqt590X58r5ZWWIklqvvJTjb69m4urAgAAlzMuOAdcwH/8eI3+4ANt6ztIcROnOU3rNvtpXbHsI+28qb/WTJ5+0Za5t2cfHerU/aL1dz46vPeGGmyMU+i+Xcr38NC/Fn9XalvvlCQN+fMdCjh9Ugs+WafsgCDb+wUAAJcGRz4AF0kLCVGTuK/lnpVpjXPPzlLT1V8qNaL4HbIuVJ63jzJrhVz0fivCPTdH+3rcpG23DC23bc8XpymxUTOX9gsAAC4NjnwALpIYE6OAxGQ1+v4b7bn+FklSo+++UVp4lFIio50b5+fryo8WqNUXn8jvTIKS68bolzvv1f7uN1lN6m+M07XznlfAqRM60bKtdt3U36mL5is/1bXznreODgQdPaxr5r+giB2b5Jl5VmcaNNLGMRP0v6u6WPOMvKuftvcbrKCjh9U4bpWyAoL067C7tK9jq/P6rD/f9YBVQ1laL/tI3ump+uXO+9Tgp+9d1i8AALg0OPIBuNDOm25Vi68+s963+OpT7ew9oFi7Dh+8peZff664CX/TR69/ok2336nrn31CUZt+liT5nzyu3n//qw527qmP536oHf1uU+e3Z5W5bI+zGTrUqZs+f/Z1fTz3Ax3+Q1f1jX1QASePObVr98m/dap5a30y5wNt63+Hus2dqaBjv7fp//A96jVz6oV8DZKkWgf36qr3Xtfqh/8h43BccH+Xul8AAHD+CB+AC+2+ro8it/6qgBNHFXDiqCK3xWv3Dbc4tXHLzlaHD97S2knTdOQP1yo1qp529R6o3TfcolbLP5YkXfH5R0qJqqcN9/1VyfUbas/1t2jnTcVDTFGJTVpo+y1DdKZhU6XUjdHPo8YpJaqeYtavdWp3qFM3bes/TCl1Gyj+jjHKDApW1I4d1vS08ChlhBR/ivv5cMvO1o0zpujHP01U2kU85exS9QsAACqH064AF8oMrq1DnbqrxarPJGN0qFN3ZQbXdmoTfPSQPLMydcuU+53Gu+Xm6HSTlpKkWof362TLtk7TT7RqV+ayPc5m6A//nqcGG+Pkl3hKbnl5cs/OUsAp5yMfTtdJOBw6WytUPikp1qjVj/yjwp+3NJ0XzNKZBo2KBa+q2i8AAKgcwgfgYjv7DFTXOc9Ikr4bP6XYdM/Ms5KkL6e/qvTQCKdpeZ5elV7uNa+/qLq/btCGeycpJbq+cr28ddM/Jss9J8epXb678z8TxuGQw5hKL7ck0fEbFXJgjxr3+7pwKZKkUUOv068j7rGu7agq/QIAgMohfAAudvgPXeWemyPjcOhIx2uLTT/ToLFyPb0UcPK4jrX7Q4l9JNVvpJgNzqdL1dmxuczl1tkWr103DdCBrtdLKjgSEnjiqI6VOdelsWrqC3LPzrLeR+zcol4vTtNnL7yt5Oj6Va5fAABQOYQPwMWMu7s+fGOJNXyuHD9/bRpyl66ZP1MOk6/jV3SQV3qa6myLV46fv3bdNEDbbh2qdov/rS5vvKjtfW9X+O5tar7qs2J9FZVSt4Eaff+NDnbpISOHrn53bqWOaFz33N+UHhahjXdPKLVNwMlj8k5NVsDJ43Lk5yt0b8E1I8nRDZTr66eUc4KAT/IZSdKZBo2s53GE79is656fqs+fna+MsDoXrV8AAGAfwgdQBeT4B5Q5/adR43Q2uLau/OBtBR0/omz/QJ1u2kq/jrhHkpQWEaWVf3tB185/Xld8+oFOtWijn0aPV68Xp5Xa5w9j/6peL07TwImjlRlUS7/dMVpeGWnnXXvAqWMybmXfReoP785Vi1XLrPdDHhguSfrsuTd0rP3VFVqOR1amah85ILfc3IvaLwAAsI/DmMqdvJ2SkqLg4GAlJycrKIi/IAJFHTt2TPPnz9d9992nqKjid1lKSEjQsmXLlB3eQsbTzwUVVp4jJ0Nep3ZWy9rLk5xwSh8u/JdyGnSSZ0AtV5cDF3JLPy3/bZ/p9ddfV/PmzV1dDgBUeRXNBtxqFwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFjznA7iETp06VeL4xMREJSUlKcf9tIyHr81VXRhH7ll5VtPay5P6fw8hBAAAlwbhA7gE/Pz85OnpqcWLF5c4PTk5WevXr1dOcH3Jw9vm6i5QbpY8kw9Xz9orIC07X76eXq4uAwCAGonwAVwCwcHBGj9+vDIyMkqcvnfvXm3YsEG5kVfI+IXYXN2FcWQkyivlSLWsvSJ8Pb3k7l2zHp4IAEBVQfgALpHg4GAFBweXOC01NVXe3t7K9Q9Svn8tewu7QG6O3GpbOwAAcC0uOAcAAABgC8IHAAAAAFtw2hVqtOTk5FKvu3ClkydPKisrS9npKTKmam6Gblz7gAuUl5Wh/JxsV5dRKY6MFHlkZenkyZMKDAx0dTmV4ufnV+qpnwDgKlVzrwe4CJKTkzV79mzl5OS4upRikpOTZYyRx/GtVfaOUWnZ+fJt1YMAgkrJy8rQ2e3rFOBVTQ+w52bJGKNPPvmk2u7Ae3p6avz48dW2fgA1E+EDNVZGRoZycnJ0++23Kzw83NXlOElMTFRMTIxyQptUyWdlpCaf0RfLPlVOTjbhA5WSn5OtAC833dx/oAKDa7u6nPPmyD0rz4S9uummmxQSUv3u6nbq1CktXrxYGRkZhA8AVQrhAzVeeHi4oqKiXF2GEy8vL9WqVUvZIWEynuzco+YKDK6t4NCqFf4rwpGTIa+8BEVGRio0NNTV5QBAjVFNj4cDAAAAqG4IHwAAAABsQfgAAAAAYAvCBwAAAABbcME5LluHkw8rdk2sVuxZodMZpxUVGKVBLQbpyZ5PKtTv9wtMF29frHk/z9Mvx35R4tlE/Xrfr7oy8srzXt7rv7yu9za/p/8e+69Ss1M1p/4cpw3wdOb/9MWRN7QzZaNSshMU7BWuzuE3q1/de+Xh5llqvzn5Wfr4wAv6OeEr5eZnq3WtazWi0eMK8vr9MxxI26IlB2fpUPo2OeRQw4A2uj3mIdXzb3HenwO4GBJPeurzf0dq68+BSktxV3BIrtpfm6xb7jyhgKA8q92yf9fRz2tq6cwpT3l4GjVoelYDxxxXo5YVf37P6eOe+uK9OtoZH6CUM54KDs1R5+vPqN+Ik/LwNJKknb/565vF4Tqwy0+Z6W6KiM7UzTflqH//0vv97TfpmWek776TTp+WGjaU7r9fevBB53Zz5kizZ0sHDkgNGkhPPCHdddfv03v1ktauLd7/zTdLy5cXDI8eLb3zjvP0Pn2kFSsq/DUAQJVA+MBlad+ZfbrmrWvUPLS53h/8vhrVbqStJ7fq4VUP68s9X2rDnzYoxLfg9prp2enq1qCb7rjiDt277N5KLzMjJ0N9m/ZV36Z9NeWbKcWmnzh7QEb5urPx3xTu00BHM/Zo4b6/KysvU0MaTiq130UHZmrzmTjd2/x5+boH6IP9z2jerkl6pE3BnkpmXoZe3T5O7Wr31IjGjyvf5GrZ4Xmatf0BzbhqhdzLCDbApXDqmJeee6ip6tTN0j1TDiosMltHD/po8RtR2vpTkB59ebf8/y+A1KmbpeHj/qewqGzlZDn0zZJwvTKlsaYv2K7AWnnlLKnAicM+MvkO3fngEYVHZ+voAR8tfLmesjLdNGTsMUnSvm3+qtc4U33uOKmg2rna/IOP3nijm3r2TNPIkSX3+8svUkSEtHChVL++9MMP0tixkru7NH58QZvXXpOmTJHeeEO6+mpp40bp3nul2rVlBZvFi6XsIs9iTEiQ2reXhg51Xl7fvtKCBb+/966ajwgCgDIRPnBZGvfFOHm5e2nlH1fK17PgORsNghuoQ1QHNZnVRE9884Reu/U1SdL/a///JEkHkg5c0DIf6vKQJGnNgTUlTr+idlddUbur9T7cp55OnD2gdScWlRo+zuam6vuTS3R3sxlqGdxJkjSq6VOaFn+b9qVuUuPAdjpxdr/Sc5PVv/4DCvGOlCTdWu8+Td80VAlZxxTh2+CCPhdwvj6YXVceHkYTZuyTl3fBkYeQiBzVb3JWU0e31Kf/itTICf+TJHW6Pslp3iFjj+r7FaH6335fteyQVqHlXXF1qq64OtV6Hx6VrRNHTmnd56FW+Og34qTTPDcMTNKOHz20fHl4qeHj7rud3zduLK1fXxAmCsPHv/8t3XefNGzY721++kl69tnfw8e5jxH54APJz694+PD2liIjK/SRAaDK4poPXHYSzybqqz1f6YE/PGAFj0KRAZG6s+2d+nDrhzLGVLjP0UtHq9e/el3kSqWzeWny8yj9AWEH07crz+SqVXBna1ykbyOFeEVpX+pvkqQ6vg3l71FL359cotz8HGXnZer7k0sU6dtYoT7RF71moCzpKe7a9kugevZPsIJHoeCQXHW6Pkk/r62lkja/3ByH4r4Ila9/nuo1PmuNf+HhJvrXzPrnVcfZdHf5BZZ95OTsWS/VqlXxfwckKTnZOUxkZUk+Ps5tfH0LjoDk5JTcx1tvScOHS/7+zuPXrCk40tKihfTnPxccIQGA6oYjH7js7E7YLSOjVuGtSpzeKqyVzmSe0amMU4rwj6hQn1EBUco3+RezTJ08e0irj3+gwTETS22Tkn1aHg5P+XkEOY0P9AxRSk7BnomPu78mXfGm5u2YqC+OvCFJivBpoAmt58rdwT8BsNfJo94yxqHIBpklTo9skKmMtFClJnsoqFauJGnThkC9NSNG2VluCgrJ1YMz9iog+PfgEBKereCQUvbkS6rhf15a/WmYBt97tNQ2P8eFav/+MI0cmSrJt9R2Rf3wg/Thh79fpyEVXJfx5pvSoEHSVVcVnKr15psFweP0aenc559u3Cht2VIQQIrq21e6/XapUSNp717p8celfv0KjrS4u1fscwNAVcCeBy5b5R3Z8HL3qnBfM26ccaHlODmTdUKvbh+njqE3qXudwRfUV3Zepv69d5qaBLXXPZEzlG/yterou5qz/S96rO1/5OXuU34nwEVW3oFFD4/fw3yLK9P1xNxdSkvx0HdfhuiNf8bo0Vl7rHAy5pHDFV7umdMeevWJxurYI0ndb04ssc3OeH+983IjjR79g1q2bF2hfrdskQYOlGJjpd69fx8/dap0/LjUpUvBZ65TRxo1SnruOcmthHMP3npLattW6tTJefzw4b8Pt20rtWsnNWlScDTkhhsqVCIAVAmcdoXLTtOQpnLIoe2nt5c4ffvp7Qr3C1ctn1r2FvZ/krJP6qVt96pxYHvd2XhqmW2DvMKUa3KUkZviND41J1FBngV3u/rp9JdKyDqqu5r8XQ0D2qhxYDvd02yGTmf9T7+dWXOpPgZQovDoLDkcRscPlRx6jx/yUWBwjvwCfg8f3j75iqibrcatMnTXpCNyc5d+WBFS4vxlSUrw0EuPNFXj1um688EjJbbZtclfc2Mbaei9B9S1694K9bttW0EAGDtW+tvfnKf5+kpvvy1lZBTc7erQoYK7YgUGSuHhzm3T0wuu97jnnvKX2bixFBYm7dlToRIBoMogfOCyE+oXqpua3KS5P83V2ZyzTtOOpx3Xfzb/R6OvHO2S2s5kndCLW/+kBv6tNarpU3JzlL2Jxvi3krvDQzuSN1rjjp89oMTsY2oc2F6SlJ2fKYfc5JDDauNwOOSQQ+YinyoGlCcgKE+trkrT2s9DlZ3lcJqWnOihjd/WUpfeZ8rswxgpJ8dRZptznTntoRcfbqoGzTI06q+HSzzqsPM3f82Z2ki33XNMPfqeLN6gBFu3StddV3A045//LL2dp6dUr17BKVIffCDdemvxIx+LFhVcI/LHP5a/3CNHCq75OPe0LQCo6mpE+MjMzNSuXbuUmVnyOcTAuWb3m62svCz1WdhH6w6u0+Hkw1qxZ4Vu+vdNah7aXE/2fNJqm3g2UfHH47Xt1DZJ0s7TOxV/PF7H045bbaZ8PUV3Lbmr2HKKOp52XPHH47UnseBPlUeyj+hwxi6l5yRL+r/gse1PCvGO0uCYiUrNOaPk7NNKzj5t9XEm64Rifx2k/ambJUm+HoHqGnGbPj7wgnYm/6SDadv07p4n1TignRoHtpMktarVRRm5KXp//9M6lrFPRzP26N09sXJzuKtF8NUX4dsEzs+wcUeUm+OmWY831u7N/ko86amtPwXqlSmNVadetm6584QkKSvTTUvfjtS+7X5KOOGpg7t99e4L9ZV02lMduydZ/S14rr6WvF36baAKg0dIeLYG33tUqckeSk4seBXaGV8QPK4beFoduiUr+YynkpN9debM7yFnyRKpZcvf+92ypSB49O4tTZpUcHrV8ePSqVO/t9m1q+BWvLt3F1zPMXx4wXxPP128zrfeKrg2JDTUeXxamvTww9KGDQVHT775puAUr6ZNC64pAXD5qo77wDXimo9Dhw5p7Nixev3119W8eXNXl4NqoFloM/1070+atmaa7lh0h06mn5SR0e2tbte/b/u3/Dz9rLaf7fxMYz4dY70f/knBydexPWM1rdc0SdKxtGM6lHyozGXO+3menlr7lPV+xokZ0gnpriZP6dqIgdqevEGnMg/rVOZhTfmv8x7FvGviJUl5JlcnMg8oO//3f2SGNpwshxyav/OvyjW/P2SwUKRvIz3Q8hUtPzJfz225Sw65qb5/S/2l1VwFe51z3gdggzp1s/XYrF36fGGk3vhnjFKTPGSMQx26JmnMI4fk5VNwQYibm9HxI95aP72h0lPc5R+Yp5jmGZr8wh5FN8yy+ks85aWyDhJu/2+gTh311qmj3ppy5xVO0+Z9VXBXuPVfhyg7y10rPqyjFR/W+b+pf9BHH+Xo++8L3iUnSzt3/j7vxx8XBI2FCwtehWJiCkKCJOXlSS+8UDCfp2dBWPnhh4JTr4raubPgYYUrVxav391d2rSp4CGDSUlSdHRB4Jk+nWd9AJe76rgP7DDncz/RIlJSUhQcHKzk5GQFBQWVP8MltGvXrmr3xePSO3bsmObPn6/77rtPURU4NyF2daxe3PCiVv2/VepSr8slrS0hIUHLli1TdngLmSJBp6pITjilDxf+SzkNOskzoJbTNLf00/Lf9pnSWw9Qvn+YawpElZeTliTPQxs17I+jFRxafshd9m4dfb04XA/O2KfGrSr+9PJLxZGTIa9TO9W/f3+Fnnsooho433//AFRPVWkfuKLZoEYc+QAuhqeue0oNazXUhiMb1Klup3KvtwBw8fS/64RC62Rr/3Y/NWyRUeI1GQCA6o/wARQxpsOY8hsBuCSu7VP2heYAgOqPvy0BAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALLjhHjXeq6BO/qojExEQlJSUpx/20jIevq8spJjWZC39xcVTX35Ij96w8k5J0/PhxZWdnu7qc81YV/90DAInwgRrMz89Pnp6eWrx4satLKSY5OVnr169XTnB9yaNqPiUsLTtfvp5eri4D1ZSbp5fSsvP1xbJPXV1K5eRmyTP5sA4ePKjg4GBXV1Mpnp6e8vOres8RAnB5I3ygxgoODtb48eOVkeH6B5ada+/evdqwYYNyI6+Q8QtxdTkl8vX0krs3Oy6oHHdvP/m26qGcnOp31ECSHBmJ8ko5osGDB6tJkyauLqdS/Pz8qm1wAlBzET5QowUHB1fJ//mmpqbK29tbuf5Byvev5epygEvC3duv2gZYN0euvL29FRERwRPCAeAi4oJzAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALbwqGjDrKwsZWVlWe9TUlIuSUEX4uDBg64uAagQfqtA9cC2CqAqq47/RlU4fMyYMUNPPfXUpazlgv3zn/90dQkAgBqE/68AwMVV4fAxZcoUTZo0yXqfkpKi+vXrX5KiKuuJJ55QTEyMq8sAynXw4EF2aoBqgP+vAKjKquP+RIXDh7e3t7y9vS9lLRcsJiZGzZs3d3UZAIAagv+vAMDFxQXnAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAW9SI8NGgQQO9/vrratCggatLAQAAAGxRHfeBPVxdwMXg4+Oj5s2bu7oMAAAAwDbVcR+4Rhz5AAAAAFD1ET4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYwsPVBQCXM7fMZFeXcN7cziY5/ReoiarjtgkA1QHhA3CB4OBgeXp5S/vWurqUSvPdv87VJQCXlKeXt4KDg11dBgDUKIQPwAXq1Kmjhf9+V8nJ/HUVqKqCg4NVp04dV5cBADUK4QNwkTp16rBjAwAALitccA4AAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwhUdlZzTGSJJSUlIuWjEAAAAAqp/CTFCYEUpT6fCRmpoqSapfv35luwAAAABQg6Smpio4OLjU6Q5TXjwpRX5+vo4eParAwEA5HI5KF+gKKSkpql+/vg4fPqygoCBXl4NKYj3WDKzHmoH1WDOwHqs/1mHNUB3XozFGqampio6Olptb6Vd2VPrIh5ubm+rVq1fZ2auEoKCgarNCUTrWY83AeqwZWI81A+ux+mMd1gzVbT2WdcSjEBecAwAAALAF4QMAAACALS7L8OHt7a3Y2Fh5e3u7uhRcANZjzcB6rBlYjzUD67H6Yx3WDDV5PVb6gnMAAAAAOB+X5ZEPAAAAAPYjfAAAAACwBeEDAAAAgC0IHwAAAABscVmFj2nTpsnhcDi9WrZs6eqyUI5169apf//+io6OlsPh0NKlS52mG2P05JNPKioqSr6+vrrxxhu1e/du1xSLUpW3HkePHl1s++zbt69rikWJZsyYoauvvlqBgYGKiIjQoEGDtHPnTqc2mZmZGjdunEJDQxUQEKDBgwfrxIkTLqoYJanIeuzVq1ex7fH+++93UcUoyWuvvaZ27dpZD6G75ppr9OWXX1rT2Rarh/LWY03cFi+r8CFJV1xxhY4dO2a9vvvuO1eXhHKkp6erffv2mjNnTonTn3vuOc2aNUvz5s3Tjz/+KH9/f/Xp00eZmZk2V4qylLceJalv375O2+f7779vY4Uoz9q1azVu3Dht2LBBq1atUk5Ojnr37q309HSrzcSJE7Vs2TItWrRIa9eu1dGjR3X77be7sGqcqyLrUZLuvfdep+3xueeec1HFKEm9evX0zDPP6JdfftHPP/+s66+/XgMHDtTWrVslsS1WF+WtR6kGbovmMhIbG2vat2/v6jJwASSZJUuWWO/z8/NNZGSkef75561xSUlJxtvb27z//vsuqBAVce56NMaYUaNGmYEDB7qkHlTOyZMnjSSzdu1aY0zBtufp6WkWLVpktdm+fbuRZNavX++qMlGOc9ejMcb07NnTPPjgg64rCpVSu3Zt8+abb7ItVnOF69GYmrktXnZHPnbv3q3o6Gg1btxYd955pw4dOuTqknAB9u/fr+PHj+vGG2+0xgUHB6tz585av369CytDZaxZs0YRERFq0aKF/vznPyshIcHVJaEMycnJkqSQkBBJ0i+//KKcnByn7bFly5Zq0KAB22MVdu56LPSf//xHYWFhatOmjaZMmaKMjAxXlIcKyMvL0wcffKD09HRdc801bIvV1LnrsVBN2xY9XF2AnTp37qx//etfatGihY4dO6annnpK3bt315YtWxQYGOjq8lAJx48flyTVqVPHaXydOnWsaage+vbtq9tvv12NGjXS3r179fjjj6tfv35av3693N3dXV0ezpGfn6+HHnpIXbt2VZs2bSQVbI9eXl6qVauWU1u2x6qrpPUoSSNHjlRMTIyio6O1adMmPfroo9q5c6cWL17swmpxrs2bN+uaa65RZmamAgICtGTJErVu3Vrx8fFsi9VIaetRqpnb4mUVPvr162cNt2vXTp07d1ZMTIw++ugj3XPPPS6sDMDw4cOt4bZt26pdu3Zq0qSJ1qxZoxtuuMGFlaEk48aN05YtW7hurporbT2OHTvWGm7btq2ioqJ0ww03aO/evWrSpIndZaIULVq0UHx8vJKTk/Xxxx9r1KhRWrt2ravLwnkqbT22bt26Rm6Ll91pV0XVqlVLzZs31549e1xdCiopMjJSkordwePEiRPWNFRPjRs3VlhYGNtnFTR+/Hh9/vnnWr16terVq2eNj4yMVHZ2tpKSkpzasz1WTaWtx5J07txZktgeqxgvLy81bdpUHTt21IwZM9S+fXu98sorbIvVTGnrsSQ1YVu8rMNHWlqa9u7dq6ioKFeXgkpq1KiRIiMj9c0331jjUlJS9OOPPzqdL4nq58iRI0pISGD7rEKMMRo/fryWLFmib7/9Vo0aNXKa3rFjR3l6ejptjzt37tShQ4fYHquQ8tZjSeLj4yWJ7bGKy8/PV1ZWFttiNVe4HktSE7bFy+q0q8mTJ6t///6KiYnR0aNHFRsbK3d3d40YMcLVpaEMaWlpTgl///79io+PV0hIiBo0aKCHHnpI//jHP9SsWTM1atRIU6dOVXR0tAYNGuS6olFMWesxJCRETz31lAYPHqzIyEjt3btXjzzyiJo2bao+ffq4sGoUNW7cOL333nv69NNPFRgYaJ07HhwcLF9fXwUHB+uee+7RpEmTFBISoqCgIP3lL3/RNddcoy5duri4ehQqbz3u3btX7733nm6++WaFhoZq06ZNmjhxonr06KF27dq5uHoUmjJlivr166cGDRooNTVV7733ntasWaOvvvqKbbEaKWs91tht0dW327LTsGHDTFRUlPHy8jJ169Y1w4YNM3v27HF1WSjH6tWrjaRir1GjRhljCm63O3XqVFOnTh3j7e1tbrjhBrNz507XFo1iylqPGRkZpnfv3iY8PNx4enqamJgYc++995rjx4+7umwUUdL6k2QWLFhgtTl79qx54IEHTO3atY2fn5+57bbbzLFjx1xXNIopbz0eOnTI9OjRw4SEhBhvb2/TtGlT8/DDD5vk5GTXFg4nd999t4mJiTFeXl4mPDzc3HDDDWblypXWdLbF6qGs9VhTt0WHMcbYGXYAAAAAXJ4u62s+AAAAANiH8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4A4DKze/duDRgwQBEREXI4HHI4HFq6dGml++vVq5fVz4EDBy5anVXJgQMHrM/Yq1cvV5cDANWWh6sLAICqIj09Xa+//rqWLFmirVu3Kj09XVFRUbriiis0fPhw3XHHHfLy8rpky1+6dKni4+MlSaNHj1bDhg0v+jLy8vJ02223aevWrRWeJz4+3gonvXr1qlI735s2bdLLL7+sNWvW6NixY/Lw8FCjRo108803a+LEiapTp46rSwQAFEH4AABJ27ZtU//+/bVv3z6n8QcOHNCBAwe0fPlytWnTRldeeeUlq2Hp0qV65513JBXs5F+K8LF//34reDRr1kxz5syRr6+vWrduXeo88fHxeuqpp6z3VSV8zJo1SxMnTlR+fr7T+M2bN2vz5s164403tHTpUnXv3t1FFQIAzkX4AHDZS0xMVL9+/XTo0CFJUnR0tB5++GG1bdtWqampWrt2rRYsWODiKi+Oo0ePWsPXXHONbrrpJhdWU3nLly/Xgw8+aL0fM2aMhg0bprS0NL3yyiuKi4tTYmKiBg0apE2bNqlu3bpl9pednS03Nzd5eLjmf4vp6eny9/d3ybIBwFYGAC5zU6ZMMZKMJBMcHGyOHDlSrM2JEydMQkKC9T4rK8s888wzpn379sbPz8/4+vqadu3amRkzZpisrCyneePj482AAQNMeHi48fDwMCEhIaZ9+/bmvvvuMwcPHjT79++3ll/Sa/Xq1WXWn5+fb+bPn286d+5sAgICjLe3t2nRooWZMmWKSUpKstr17Nmz1GXs37+/xL5jYmJKnSc2NrZYv1u2bDEPPfSQiYiIMD4+PqZv377mwIEDxfpdt26d6d+/vwkLCzOenp6mYcOGZuLEiSYxMbHMz1qobdu21jJHjx7tNC0zM9O0aNHCmv6Xv/zFmjZq1Chr/BdffGEmTZpkIiMjjcPhsL6Dffv2mf79+xs/Pz8THh5uJkyYYLZu3WrN17NnT6flpaammtjYWHPFFVcYHx8fExgYaHr27Gm++OILp3ZF13PPnj3N2rVrTZcuXYyPj48ZNWpUhT43AFR3hA8Al73GjRtbO4XTpk0rt31mZqbp0aNHqTvlPXr0sALI6dOnTXh4eKltV61adUHhIz8/3wwfPrzUeVu2bGnt0NsRPlq1alWsXdeuXZ36fOONN4ybm1uJfbZo0aLcALJnzx6nebZs2VKszauvvmpNb9CggTW+aPgout4Lv4OEhARTv379YnW1a9euxPCRlJTkFITOfc2ZM8dqW3Q9R0dHGx8fH+s94QPA5YK7XQG4rKWlpTld51GR6wNefvllrVu3TpJUv359vffee3r//ffVoEEDSdK6dev00ksvSZLWr1+vU6dOSZJGjBihVatWaenSpZo5c6Z69uwpd3d3RUVFKS4uTv369bOWMWvWLMXFxSkuLk4dOnQotZaPPvpIH3zwgSSpdu3a1gXz7dq1kyTt2LFDjz/+uCTp1Vdf1axZs6x5+/XrZy0jKiqqxP4//vhja36p4PSmwnnuvvvuYu2PHDmiefPmaeHChapVq5Yk6fvvv7euM/nf//6n8ePHKz8/X4GBgXr11Vf11VdfacyYMZKknTt3Oi2vJNu2bbOGvby8Srxepei1OYcOHVJaWlqxNvv27dOECRO0YsUKzZ8/X4GBgXr++ed1+PBhSVLDhg314Ycf6l//+pfT6WpFPfHEE9q8ebMk6eabb9by5cv17rvvKjIyUpI0ceJEq7+ijh49qnr16mnhwoX64osvNGjQoDI/MwDUGK5OPwDgSkeOHHH6S/X27dvLnafoX8GXLVtmjV+2bJk1vn379sYYY1asWGGNe+SRR8yhQ4dMfn5+if0W/at8eadaFRowYIA1z6uvvmqN37x5szW+du3a1jJXr1593n9tX7BgQbGjHUUVPfLx0ksvWePvv/9+a/zSpUuNMca89NJL1rgxY8aYuLg4ExcXZ9atW2f8/PyMVHDqW15eXqn1LFy40OojKiqqxDY7duxwWq+Fp9IV/Y5HjhxZbL6iR26WL19ujX/jjTeKHfnIy8sztWvXNpKMl5eX+frrr63P88ADD1jtZ86caYxxPvLh5uZmduzYUeb3DgA1ERecA7isBQcHO70/evSoWrZsWeY8u3btsoY7d+5sDXfq1KlYm+7du6tZs2bavXu3nnvuOT333HMKDAzUVVddpTvvvFP33HOP3NwqfxC6tFratGkjPz8/ZWRk6MyZMzp16pQiIiIqvZyK6tmzpzUcGhpqDSclJRWrd8GCBSVeyJ+cnGwdGShJUFCQNZyQkCBjjBwOh1ObwqNNhc5dz5LUv3//YuOKHgW7+uqrreGi67bQ6dOndebMGUkFF6zfeOONJda7ffv2YuOaNWumFi1alNgeAGoyTrsCcFkLCAhQ48aNrffff/99pfs6dwdYkvz8/PT999/r73//u66//npFRkZad9AaO3asnnvuuUovryqqXbu2NVz0zlHGmPPqJz09vdRpRU+zys7OdjoNq9Bvv/1mDdevX18BAQHF2pzPM0BKWrcVVdJn4fkjAC5XhA8Al71hw4ZZwy+++GKJ5/efPHlSiYmJkqTmzZtb4zdu3GgN//jjj9ZwYRtjjMLDwzV16lR98803OnbsmPbt22ftDC9evNiap+gRkHOfXVGa0mrZsmWLMjIyJBUEgvDw8Ar1V5LK1FWaovXGxsbKFNz4xOmVnp5e5lGBJk2aqE2bNtb7F1980Wl6dna2Zs+ebb0v7XqKkgJF0SD6888/W8NF122hsLAwK2wFBAQoNTW12GfJy8sr8ejOhYQZAKjOOO0KwGVv8uTJ+s9//qNDhw4pKSlJnTt31uTJk63nfKxZs0YLFizQmjVrFBISopEjR2rTpk2SpHHjxik1NVUOh0OPPfaY1eeIESMkST/88IMmTJigwYMHq1mzZgoLC9OmTZusYJCVlWXNU/SowcKFC+Xu7i53d3d169at1NpHjhypzz77TJL05JNPytvbW2FhYU4PBRw2bNgF7ewWrWvFihXq0aOHfHx81LZt2xJPZyrLkCFD9NhjjykrK0vPPPOMHA6HrrnmGmVkZGj//v1avXq1zp49q1WrVpXZz9NPP60BAwZIkt5++205HA4NHTpU6enpmjVrlnbs2GHV/sgjj1S4vgEDBlinSY0fP17PPPOMMjMz9cQTTxRr6+bmphEjRmju3LlKS0tT7969NWHCBIWFhenIkSPasmWLFi9erLfffrvKPJgRAFzONZeaAEDVsnXr1mK3Xj339euvvxpjCm61271791LbFb3VblxcXJl9zpgxw6qh6AXrRV9lyc/PN8OGDSu1/6K32jWmchecnzp1ynh7exfru/Ci+KIXnBe9ZW9sbKw1fsGCBdb4sm61KxV/jkZpXnjhhTL7qVWrVrEL98u7qP/06dOmbt26xfpq1qxZifWdOXOmzFvtFl3Ouc/5AIDLEaddAYAKriPYtGmTXnzxRXXr1k0hISHy8vJS/fr11adPH73zzjvWtQbe3t5atWqVnnnmGbVr106+vr7WkYAZM2Zo5cqV8vLyklRwmtGjjz6qLl26qE6dOvLw8FBAQICuvvpqzZkzR48++qhVw6233qqZM2eqSZMmFX7StsPh0Hvvvad58+apU6dO8vf3l7e3t5o3b67HHntMGzZscDpyURlhYWFaunSpOnToIF9f3wvqS5L+9Kc/ad26dbr99tut76ROnTrq1KmTpk6dqrlz51aon0mTJumXX37R6NGj1bBhQ3l7e8vf319t2rTRI488om3btp33EYfQ0FCtW7dOt956q/z8/BQSEqJ7771XixYtKrF9rVq1tH79ek2fPl3t27eXr6+v/Pz81KxZMw0ZMkTvv/++unTpcl41AEBN5jDmPK8CBAAAAIBK4MgHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALb4/+HaQeWOmkxTAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create boxplot for cost of orders.\n",
    "plt.figure(figsize=(10, 4))\n",
    "box_plot = sns.boxplot(x=foodhub_df['cost_of_the_order'])\n",
    "\n",
    "# Calculate quartiles and median\n",
    "Q1 = foodhub_df['cost_of_the_order'].quantile(0.25)\n",
    "Q3 = foodhub_df['cost_of_the_order'].quantile(0.75)\n",
    "median = foodhub_df['cost_of_the_order'].median()\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the output.\n",
    "plt.text(x=median, y=-0.3, s=f'Median: {median}', color='red', ha='center', bbox=dict(facecolor='white', alpha=0.5))\n",
    "plt.text(x=Q1, y=-0.2, s=f'Q1: {Q1}', color='green', ha='center', bbox=dict(facecolor='white', alpha=0.5))\n",
    "plt.text(x=Q3, y=-0.2, s=f'Q3: {Q3}', color='blue', ha='center', bbox=dict(facecolor='white', alpha=0.5)) \n",
    "plt.xlabel('Cost of the Order', fontsize=12, fontweight='bold')\n",
    "plt.title('Boxplot for the Cost of Orders', fontsize=12, fontweight='bold')\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Day of the Week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 days of the week. The days of the week are Weekend, Weekday.\n"
     ]
    }
   ],
   "source": [
    "# Get the unique days of the week in the dataset.\n",
    "days_of_week = foodhub_df['day_of_the_week'].nunique()\n",
    "\n",
    "# Get the names of the days of the week.\n",
    "names_of_days = foodhub_df['day_of_the_week'].unique()\n",
    "\n",
    "# Convert the names of the days to a string.\n",
    "names_of_days_str = ', '.join(names_of_days)\n",
    "\n",
    "# Output the number of days of the week and the names of the days.\n",
    "print(f'There are {days_of_week:,} days of the week. The days of the week are {names_of_days_str}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zc9OgQYM0b948SVJcXJwmT56s06dPO93aXPTLpui/Vj/88EO5u7vL3d1dHTt2VEREhFU3e/ZseXl5KSEhQe+9916ZbcMzzzyjlStXXrK+YcOG1utHHnlEDzzwgFasWFFulzv+9re/qUmTJmrbtq3eeOMN63/abdu2VZ06dSRJvXv31pdffqnDhw/rzjvv1MiRI+Xv76+jR4/qm2++0Weffab4+Pjf9BDM+++/3zqbMmnSJHl7e6tGjRrWwwEl6d577y21MyDHjh3Tpk2blJaWpi+++EIffPCBVTdmzBhFRUVJ0jUfMyEhIbr99tu1dOlSbd682SofNGiQU7ubbrpJbdu2VYcOHXTdddfpl19+0d69e6367OzsS/a9R48eCg4O1smTJ7Vjxw6NHTtWffr00Zdffmndgl6jRo1rfjbThapVq6ZTp07pxIkTWrx4sSIiIhQaGqpGjRpd03KqV69easdQTEyMHA6HFVwk57M/ha+Lho+i9a1bt1aLFi20e/dubdiwQUOGDNHAgQPl6empI0eOaNu2bVq6dKl1qT4uLk7ffvutJOn222/Xk08+qdq1a+vHH3/U/v379fnnn+vxxx/XsGHDLurra6+9pkOHDmn58uVKTEzUwIED9cUXXzidRUMFKr/hSsC1udRdZidPnrzibfeFA22NMWb58uXFtjPm/N0zRQcNF0433XRTsYNtf8ug6qK6dOnitL6ig6r37t1r3aJfdCq6zZcaVF1UYVnRAbBFH0XQuXNnq7zooOpGjRpdtG4PDw+nv8HlbpkunAoHhJdksLMx5wfMFt6ZV9xU9Lb7kq6n6KDqS00jR440OTk5Ttt+LceMMcasWLHCqW3hbeBFFffIhMKpWbNmJi8v77LbsmzZsqu67b5QSQZVDxgw4KJlF+7rosd70e2/1DF3LcfQlTRr1sxpvhkzZjjVX3hMFx2kb8zlb7u/cB9lZ2df9rb7Cz+fF34Os7KyTJs2bazyESNGXNU2ouxxyQyVTvXq1bV161ZNnDhRUVFR1kPs2rdvr7feektLlixxOmtw++2367XXXlODBg0u+pdY3bp1tWrVKnXo0EE+Pj5q0KCB3nzzTT3wwANlug3PPvvsJeuaNm2qxYsXq2HDhvLy8lKLFi30ySef6N577y3TPhWaOHGipk+frnr16snLy0tt27bVihUr1KVLF6tN3bp19c0332jChAlq0qSJqlSpIn9/fzVp0kRDhgzRF198YZ1NKimHw6ElS5Zo3rx56tChg/z8/KyH/D399NPaunWr09m/0uDu7q6goCA1a9ZMgwcP1oYNG/Tuu+86/WxHSY6ZXr16KTw83Hpf3OWyiRMnqm/fvoqIiJCvr688PT1Vr149jR49WmvXrnUag1Kcvn37Kj4+XnfffbdCQkLk4eGhmjVrqn///tqyZUupDOJ/4403dM8995TKZcrSPIaKXjaTLh4fdKX666+/XklJSRo9erTq168vLy8vBQUFqUWLFho9erTTJTAvLy+tXLlSs2fPVocOHawHhkZGRqpPnz567733dNddd12yr35+flqxYoV1PMyfP1+TJk26qu1E2XIYc42j3AAA12zEiBFasGCB3Nzc9MMPP1z2rjEA5Y8LlwBQRsz/H9dy+PBh/fOf/5Qk3XrrrYQhwAURiACgjBw9elSRkZHWe4fDYT16AYBrYQwRAJQxd3d3RUVFacmSJerYsWNFdwdAMRhDBAAAbI8zRAAAwPYIRAAAwPYYVH0VCgoKdOLECfn7+1fI7wIBAIBrZ4zRL7/8ovDwcKefcSoOgegqnDhx4jc/ZA4AAFSM48ePq3bt2pdtU6GBaOPGjXr11VeVmJioH3/8UUuXLlW/fv2KbTt69Gj99a9/1YwZMzRu3Dir/NSpU3rkkUe0fPlyubm5acCAAZo1a5aqVq1qtfnuu+80ZswYbd++XTVr1tQjjzzi9MvUV+Lv7y/p/A691C+HAwAA15KZmak6depY3+OXU6GB6MyZM2rdurVGjBih/v37X7Ld0qVLtXXrVqdH3xeKi4vTjz/+qNWrVys3N1fDhw/Xgw8+qCVLlkg6vzN69Oih7t27a968edq1a5dGjBihoKAgPfjgg1fVz8LLZAEBAQQiAAAqmasZ7lKhgah3797q3bv3Zdv897//1SOPPKKvvvpKffr0carbt2+fVq5cqe3bt6tdu3aSpDlz5ui2227Ta6+9pvDwcC1evFg5OTmaP3++vLy81Lx5cyUlJen111+/6kAEAAB+31z6LrOCggINHjxYEyZMUPPmzS+qj4+PV1BQkBWGJKl79+5yc3NTQkKC1aZTp07y8vKy2vTs2VMHDhzQ6dOni11vdna2MjMznSYAAPD75dKB6JVXXpGHh4f+9Kc/FVufkpKikJAQpzIPDw9Vr15dKSkpVpvQ0FCnNoXvC9tcaOrUqQoMDLQmBlQDAPD75rKBKDExUbNmzdLChQvL/Vb3iRMnKiMjw5qOHz9erusHAADly2UD0aZNm5SWlqa6devKw8NDHh4eOnr0qB5//HHVq1dPkhQWFqa0tDSn+fLy8nTq1CmFhYVZbVJTU53aFL4vbHMhb29vawA1A6kBAPj9c9lANHjwYH333XdKSkqypvDwcE2YMEFfffWVJCk2Nlbp6elKTEy05lu7dq0KCgoUExNjtdm4caNyc3OtNqtXr1ZUVJSqVatWvhsFAABcUoXeZZaVlaVDhw5Z75OTk5WUlKTq1aurbt26Cg4Odmrv6empsLAwRUVFSZKaNm2qXr16adSoUZo3b55yc3M1duxYDRo0yLpF//7779dzzz2nkSNH6qmnntLu3bs1a9YszZgxo/w2FAAAuLQKDUQ7duxQ165drffjx4+XJA0dOlQLFy68qmUsXrxYY8eO1S233GI9mHH27NlWfWBgoFatWqUxY8YoOjpaNWrU0KRJk7jlHgAAWBzGGFPRnXB1mZmZCgwMVEZGBuOJAACoJK7l+9tlxxABAACUFwIRAACwPQIRAACwPQIRAACwPQIRAACwPQIRAACwvQp9DhGcRU94v6K7ALikxFeHVHQXAPzOcYYIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYXoUGoo0bN+qOO+5QeHi4HA6Hli1bZtXl5ubqqaeeUsuWLeXn56fw8HANGTJEJ06ccFrGqVOnFBcXp4CAAAUFBWnkyJHKyspyavPdd9/p5ptvVpUqVVSnTh1Nnz69PDYPAABUEhUaiM6cOaPWrVtr7ty5F9X9+uuv2rlzp5599lnt3LlTn332mQ4cOKA777zTqV1cXJz27Nmj1atXa8WKFdq4caMefPBBqz4zM1M9evRQRESEEhMT9eqrr2rKlCl6++23y3z7AABA5eBRkSvv3bu3evfuXWxdYGCgVq9e7VT2xhtvqEOHDjp27Jjq1q2rffv2aeXKldq+fbvatWsnSZozZ45uu+02vfbaawoPD9fixYuVk5Oj+fPny8vLS82bN1dSUpJef/11p+AEAADsq1KNIcrIyJDD4VBQUJAkKT4+XkFBQVYYkqTu3bvLzc1NCQkJVptOnTrJy8vLatOzZ08dOHBAp0+fLnY92dnZyszMdJoAAMDvV6UJROfOndNTTz2l++67TwEBAZKklJQUhYSEOLXz8PBQ9erVlZKSYrUJDQ11alP4vrDNhaZOnarAwEBrqlOnTmlvDgAAcCGVIhDl5ubqnnvukTFGb731Vpmvb+LEicrIyLCm48ePl/k6AQBAxanQMURXozAMHT16VGvXrrXODklSWFiY0tLSnNrn5eXp1KlTCgsLs9qkpqY6tSl8X9jmQt7e3vL29i7NzQAAAC7Mpc8QFYahgwcP6uuvv1ZwcLBTfWxsrNLT05WYmGiVrV27VgUFBYqJibHabNy4Ubm5uVab1atXKyoqStWqVSufDQEAAC6tQgNRVlaWkpKSlJSUJElKTk5WUlKSjh07ptzcXN19993asWOHFi9erPz8fKWkpCglJUU5OTmSpKZNm6pXr14aNWqUtm3bps2bN2vs2LEaNGiQwsPDJUn333+/vLy8NHLkSO3Zs0cff/yxZs2apfHjx1fUZgMAABdToZfMduzYoa5du1rvC0PK0KFDNWXKFH3xxReSpDZt2jjNt27dOnXp0kWStHjxYo0dO1a33HKL3NzcNGDAAM2ePdtqGxgYqFWrVmnMmDGKjo5WjRo1NGnSJG65BwAAlgoNRF26dJEx5pL1l6srVL16dS1ZsuSybVq1aqVNmzZdc/8AAIA9uPQYIgAAgPJAIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZHIAIAALZXoYFo48aNuuOOOxQeHi6Hw6Fly5Y51RtjNGnSJNWqVUs+Pj7q3r27Dh486NTm1KlTiouLU0BAgIKCgjRy5EhlZWU5tfnuu+908803q0qVKqpTp46mT59e1psGAAAqkQoNRGfOnFHr1q01d+7cYuunT5+u2bNna968eUpISJCfn5969uypc+fOWW3i4uK0Z88erV69WitWrNDGjRv14IMPWvWZmZnq0aOHIiIilJiYqFdffVVTpkzR22+/XebbBwAAKgePilx579691bt372LrjDGaOXOmnnnmGfXt21eS9P777ys0NFTLli3ToEGDtG/fPq1cuVLbt29Xu3btJElz5szRbbfdptdee03h4eFavHixcnJyNH/+fHl5eal58+ZKSkrS66+/7hScAACAfbnsGKLk5GSlpKSoe/fuVllgYKBiYmIUHx8vSYqPj1dQUJAVhiSpe/fucnNzU0JCgtWmU6dO8vLystr07NlTBw4c0OnTp4tdd3Z2tjIzM50mAADw++WygSglJUWSFBoa6lQeGhpq1aWkpCgkJMSp3sPDQ9WrV3dqU9wyiq7jQlOnTlVgYKA11alT57dvEAAAcFkuG4gq0sSJE5WRkWFNx48fr+guAQCAMuSygSgsLEySlJqa6lSemppq1YWFhSktLc2pPi8vT6dOnXJqU9wyiq7jQt7e3goICHCaAADA75fLBqLIyEiFhYVpzZo1VllmZqYSEhIUGxsrSYqNjVV6eroSExOtNmvXrlVBQYFiYmKsNhs3blRubq7VZvXq1YqKilK1atXKaWsAAIArq9BAlJWVpaSkJCUlJUk6P5A6KSlJx44dk8Ph0Lhx4/Tiiy/qiy++0K5duzRkyBCFh4erX79+kqSmTZuqV69eGjVqlLZt26bNmzdr7NixGjRokMLDwyVJ999/v7y8vDRy5Ejt2bNHH3/8sWbNmqXx48dX0FYDAABXU6G33e/YsUNdu3a13heGlKFDh2rhwoV68skndebMGT344INKT09Xx44dtXLlSlWpUsWaZ/HixRo7dqxuueUWubm5acCAAZo9e7ZVHxgYqFWrVmnMmDGKjo5WjRo1NGnSJG65BwAAFocxxlR0J1xdZmamAgMDlZGRUabjiaInvF9mywYqs8RXh1R0FwBUQtfy/e2yY4gAAADKC4EIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADYHoEIAADY3jUHotzcXLm7uys0NFTGmLLokyU/P1/PPvusIiMj5ePjowYNGuiFF15wWq8xRpMmTVKtWrXk4+Oj7t276+DBg07LOXXqlOLi4hQQEKCgoCCNHDlSWVlZZdp3AABQeVxzIPL09FStWrVUs2ZNORyOsuiT5ZVXXtFbb72lN954Q/v27dMrr7yi6dOna86cOVab6dOna/bs2Zo3b54SEhLk5+ennj176ty5c1abuLg47dmzR6tXr9aKFSu0ceNGPfjgg2XadwAAUHmU6JLZo48+qgMHDmjVqlWl3R8nW7ZsUd++fdWnTx/Vq1dPd999t3r06KFt27ZJOn92aObMmXrmmWfUt29ftWrVSu+//75OnDihZcuWSZL27dunlStX6t1331VMTIw6duyoOXPm6KOPPtKJEyfKtP8AAKByKFEg+vLLL+Xu7q7evXurWbNm6tq1q7p166Zu3brplltuKbXO3XjjjVqzZo2+//57SdK3336rf//73+rdu7ckKTk5WSkpKerevbs1T2BgoGJiYhQfHy9Jio+PV1BQkNq1a2e16d69u9zc3JSQkFDserOzs5WZmek0AQCA3y+Pksy0YcMG6/X+/fu1f/9+631pXkZ7+umnlZmZqSZNmsjd3V35+fl66aWXFBcXJ0lKSUmRJIWGhjrNFxoaatWlpKQoJCTEqd7Dw0PVq1e32lxo6tSpeu6550ptOwAAgGsrUSAaMmRImY8fkqRPPvlEixcv1pIlS9S8eXMlJSVp3LhxCg8P19ChQ8tsvRMnTtT48eOt95mZmapTp06ZrQ8AAFSsEgWihQsXlnI3ijdhwgQ9/fTTGjRokCSpZcuWOnr0qKZOnaqhQ4cqLCxMkpSamqpatWpZ86WmpqpNmzaSpLCwMKWlpTktNy8vT6dOnbLmv5C3t7e8vb3LYIsAAIAr+k3PIVq3bp2mTp2qefPmKTMzU8eOHVN2dnZp9U2//vqr3Nycu+ju7q6CggJJUmRkpMLCwrRmzRqrPjMzUwkJCYqNjZUkxcbGKj09XYmJiVabtWvXqqCgQDExMaXWVwAAUHmV6AzR2bNndeedd2rt2rWSpJiYGIWEhGjgwIF6+eWX9dRTT5VK5+644w699NJLqlu3rpo3b65vvvlGr7/+ukaMGCHp/HilcePG6cUXX1SjRo0UGRmpZ599VuHh4erXr58kqWnTpurVq5dGjRqlefPmKTc3V2PHjtWgQYMUHh5eKv0EAACVW4kC0TPPPON0VkaS+vTpIy8vL/3zn/8stUA0Z84cPfvss3r44YeVlpam8PBw/fGPf9SkSZOsNk8++aTOnDmjBx98UOnp6erYsaNWrlypKlWqWG0WL16ssWPH6pZbbpGbm5sGDBig2bNnl0ofAQBA5ecwJXjcdJ06dXTq1CnFx8erTZs2uuGGG7RlyxY1adJE6enpl7x7q7LKzMxUYGCgMjIyFBAQUGbriZ7wfpktG6jMEl8dUtFdAFAJXcv3d4nGEKWlpalx48Zq1aqVU7mnp6fS09NLskgAAIAKU6JAVKtWLX3//fc6fPiwVZaUlKR9+/YxLgcAAFQ6JQpEffv21dmzZ9WiRQs5HA5988036tChg4wx6tu3b2n3EQAAoEyVKBC98MILat26tbKzs2WMUXZ2tvLy8tSyZUue8AwAACqdEt1lFhAQoG3btmnJkiXavn27JKl9+/a677775OXlVaodBAAAKGslCkTS+QHUQ4cO1e233y5JCg4OLrVOAQAAlKcSP6n6jTfeUHh4uEJCQhQSEqLw8HDNmTOnNPsGAABQLkoUiCZPnqxHH31UKSkpMsbIGKOUlBSNGzdOkydPLu0+AgAAlKkSBaK33npLknTzzTdr1qxZmjVrljp37ixjjFUHAABQWZRoDNG5c+d03XXXae3atXJ3d5ckPfTQQ4qMjFRmZmapdhAAAKCslegM0V133SVjjBwOh1VW+Lp///6l0zMAAIByctVniN5//3+/s9WuXTt99tln6tatm+6++25J0meffaaMjAxFR0eXfi8BAADK0FX/uKubm5vTGaHiGGPk5uamvLy8Uumcq+DHXYGKxY+7AiiJa/n+vqYxRFeTnQoKCq5lkQAAABXuqgMRQQcAAPxelfjBjAAAAL8XJbrtPj8/X/Pnz9e6deuUmprqdCnN4XBozZo1pdZBAACAslaiQPSnP/1J8+bNk3TxuKIrDbwGAABwNSUKRB9//LEk6aabblL9+vUJQQAAoFIrUSDy9fVVzZo1tXHjxtLuDwAAQLkr0aDqZ599VsnJyfroo4+UlZVV2n0CAAAoVyX+6Y4GDRooLi5OgYGBcnd3tyYPjxKddAIAAKgwJUovQ4YM0f79+6/qQY0AAACurkSBaP369XI4HLr//vtVr149zgoBAIBKrURJJioqSjk5Ofrggw9Kuz8AAADlrkRjiP785z/r6NGjmjZtmnbv3q1jx445TQAAAJXJVf/afVFubm6XfPaQw+Hg1+5LiF+7B4rHr90DKIky+7X7ohhQDQAAfi9KFIgWLFhQ2v0AAACoMCUKREOHDi3tfgAAAFSYEgWi99+//FiXIUO43g8AACqPEgWiYcOGXXZQNYEIAABUJgyqBgAAtlei5xAVFBQ4Tenp6Xr77bfl5eWlf/7zn6XdRwAAgDJVokB0oYCAAD3wwAO68cYb9X//93+lsUgAAIByU6JLZhc+jTo/P1/ff/+9kpKSlJ2dXSodAwAAKC8lCkSRkZGXrGvbtm2JOwMAAFARShSILjWgum7dunrzzTd/U4cAAADKW4kC0bp165zeOxwOhYSEqFGjRnJ3dy+VjgEAAJSXawpEl3sg45EjR7Rt2zZJPJgRAABULtcUiC73QMaiCEQAAKAyuebb7o0xl51K23//+1/94Q9/UHBwsHx8fNSyZUvt2LHDqT+TJk1SrVq15OPjo+7du+vgwYNOyzh16pTi4uIUEBCgoKAgjRw5UllZWaXeVwAAUDldUyDat2/fRdPy5ct1/fXXW2eOoqKiSq1zp0+f1k033SRPT0/961//0t69e/WXv/xF1apVs9pMnz5ds2fP1rx585SQkCA/Pz/17NlT586ds9rExcVpz549Wr16tVasWKGNGzfqwQcfLLV+AgCAys1hSnhaJyUlRc8//7zee+895ebmqk6dOpo8ebKGDRsmN7dSed6jnn76aW3evFmbNm0qtt4Yo/DwcD3++ON64oknJEkZGRkKDQ3VwoULNWjQIO3bt0/NmjXT9u3b1a5dO0nSypUrddttt+mHH35QeHj4FfuRmZmpwMBAZWRkKCAgoFS2rTjREy7/o7mAXSW+ymV4ANfuWr6/rzm5pKena+LEiWrYsKHmzZunwMBA/eUvf9HBgwc1YsSIUgtDkvTFF1+oXbt2GjhwoEJCQtS2bVu98847Vn1ycrJSUlLUvXt3qywwMFAxMTGKj4+XJMXHxysoKMgKQ5LUvXt3ubm5KSEhodj1ZmdnKzMz02kCAAC/X9eUXqZOnar69etr+vTp8vDw0JQpU5ScnKzHHntMXl5epd65//znP3rrrbfUqFEjffXVV3rooYf0pz/9SYsWLZJ0/iyVJIWGhjrNFxoaatWlpKQoJCTEqd7Dw0PVq1e32lxo6tSpCgwMtKY6deqU9qYBAAAXck13mf35z3+2xgqFhoZq5cqVWrlypVMbh8OhzZs3l0rnCgoK1K5dO7388suSzj8Fe/fu3Zo3b56GDh1aKusozsSJEzV+/HjrfWZmJqEIAIDfsRI/qfrQoUM6dOjQRXeWXc1t+VerVq1aatasmVNZ06ZN9Y9//EOSFBYWJklKTU1VrVq1rDapqalq06aN1SYtLc1pGXl5eTp16pQ1/4W8vb3l7e1dWpsBAABc3DUFok6dOpVq4LmSm266SQcOHHAq+/777xURESHp/G+qhYWFac2aNVYAyszMVEJCgh566CFJUmxsrNLT05WYmKjo6GhJ0tq1a1VQUKCYmJhy2xYAAOC6rikQrV+/voy6UbzHHntMN954o15++WXdc8892rZtm95++229/fbbks6fjRo3bpxefPFFNWrUSJGRkXr22WcVHh6ufv36STp/RqlXr14aNWqU5s2bp9zcXI0dO1aDBg26qjvMAADA71+JLpmVl/bt22vp0qWaOHGinn/+eUVGRmrmzJmKi4uz2jz55JM6c+aMHnzwQaWnp6tjx45auXKlqlSpYrVZvHixxo4dq1tuuUVubm4aMGCAZs+eXRGbBAAAXFCJn0NkJzyHCKhYPIcIQEmU6XOIAAAAfm8IRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPYIRAAAwPZc+sGMAPB7wXPGgOK5ynPGOEMEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsj0AEAABsr1IFomnTpsnhcGjcuHFW2blz5zRmzBgFBweratWqGjBggFJTU53mO3bsmPr06SNfX1+FhIRowoQJysvLK+feAwAAV1VpAtH27dv117/+Va1atXIqf+yxx7R8+XL9/e9/14YNG3TixAn179/fqs/Pz1efPn2Uk5OjLVu2aNGiRVq4cKEmTZpU3psAAABcVKUIRFlZWYqLi9M777yjatWqWeUZGRl677339Prrr6tbt26Kjo7WggULtGXLFm3dulWStGrVKu3du1cffvih2rRpo969e+uFF17Q3LlzlZOTU1GbBAAAXEilCERjxoxRnz591L17d6fyxMRE5ebmOpU3adJEdevWVXx8vCQpPj5eLVu2VGhoqNWmZ8+eyszM1J49e8pnAwAAgEvzqOgOXMlHH32knTt3avv27RfVpaSkyMvLS0FBQU7loaGhSklJsdoUDUOF9YV1xcnOzlZ2drb1PjMz87dsAgAAcHEufYbo+PHjevTRR7V48WJVqVKl3NY7depUBQYGWlOdOnXKbd0AAKD8uXQgSkxMVFpamq6//np5eHjIw8NDGzZs0OzZs+Xh4aHQ0FDl5OQoPT3dab7U1FSFhYVJksLCwi6666zwfWGbC02cOFEZGRnWdPz48dLfOAAA4DJcOhDdcsst2rVrl5KSkqypXbt2iouLs157enpqzZo11jwHDhzQsWPHFBsbK0mKjY3Vrl27lJaWZrVZvXq1AgIC1KxZs2LX6+3trYCAAKcJAAD8frn0GCJ/f3+1aNHCqczPz0/BwcFW+ciRIzV+/HhVr15dAQEBeuSRRxQbG6sbbrhBktSjRw81a9ZMgwcP1vTp05WSkqJnnnlGY8aMkbe3d7lvEwAAcD0uHYiuxowZM+Tm5qYBAwYoOztbPXv21JtvvmnVu7u7a8WKFXrooYcUGxsrPz8/DR06VM8//3wF9hoAALiSSheI1q9f7/S+SpUqmjt3rubOnXvJeSIiIvTll1+Wcc8AAEBl5dJjiAAAAMoDgQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANgegQgAANieSweiqVOnqn379vL391dISIj69eunAwcOOLU5d+6cxowZo+DgYFWtWlUDBgxQamqqU5tjx46pT58+8vX1VUhIiCZMmKC8vLzy3BQAAODCXDoQbdiwQWPGjNHWrVu1evVq5ebmqkePHjpz5ozV5rHHHtPy5cv197//XRs2bNCJEyfUv39/qz4/P199+vRRTk6OtmzZokWLFmnhwoWaNGlSRWwSAABwQR4V3YHLWblypdP7hQsXKiQkRImJierUqZMyMjL03nvvacmSJerWrZskacGCBWratKm2bt2qG264QatWrdLevXv19ddfKzQ0VG3atNELL7ygp556SlOmTJGXl1dFbBoAAHAhLn2G6EIZGRmSpOrVq0uSEhMTlZubq+7du1ttmjRporp16yo+Pl6SFB8fr5YtWyo0NNRq07NnT2VmZmrPnj3l2HsAAOCqXPoMUVEFBQUaN26cbrrpJrVo0UKSlJKSIi8vLwUFBTm1DQ0NVUpKitWmaBgqrC+sK052drays7Ot95mZmaW1GQAAwAVVmjNEY8aM0e7du/XRRx+V+bqmTp2qwMBAa6pTp06ZrxMAAFScShGIxo4dqxUrVmjdunWqXbu2VR4WFqacnBylp6c7tU9NTVVYWJjV5sK7zgrfF7a50MSJE5WRkWFNx48fL8WtAQAArsalA5ExRmPHjtXSpUu1du1aRUZGOtVHR0fL09NTa9asscoOHDigY8eOKTY2VpIUGxurXbt2KS0tzWqzevVqBQQEqFmzZsWu19vbWwEBAU4TAAD4/XLpMURjxozRkiVL9Pnnn8vf398a8xMYGCgfHx8FBgZq5MiRGj9+vKpXr66AgAA98sgjio2N1Q033CBJ6tGjh5o1a6bBgwdr+vTpSklJ0TPPPKMxY8bI29u7IjcPAAC4CJcORG+99ZYkqUuXLk7lCxYs0LBhwyRJM2bMkJubmwYMGKDs7Gz17NlTb775ptXW3d1dK1as0EMPPaTY2Fj5+flp6NChev7558trMwAAgItz6UBkjLlimypVqmju3LmaO3fuJdtEREToyy+/LM2uAQCA3xGXHkMEAABQHghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9ghEAADA9mwViObOnat69eqpSpUqiomJ0bZt2yq6SwAAwAXYJhB9/PHHGj9+vCZPnqydO3eqdevW6tmzp9LS0iq6awAAoILZJhC9/vrrGjVqlIYPH65mzZpp3rx58vX11fz58yu6awAAoILZIhDl5OQoMTFR3bt3t8rc3NzUvXt3xcfHV2DPAACAK/Co6A6Uh59//ln5+fkKDQ11Kg8NDdX+/fsvap+dna3s7GzrfUZGhiQpMzOzTPuZn322TJcPVFZl/dkrD3y+geKV5ee7cNnGmCu2tUUgulZTp07Vc889d1F5nTp1KqA3AALnjK7oLgAoI+Xx+f7ll18UGBh42Ta2CEQ1atSQu7u7UlNTncpTU1MVFhZ2UfuJEydq/Pjx1vuCggKdOnVKwcHBcjgcZd5fVKzMzEzVqVNHx48fV0BAQEV3B0Ap4vNtL8YY/fLLLwoPD79iW1sEIi8vL0VHR2vNmjXq16+fpPMhZ82aNRo7duxF7b29veXt7e1UFhQUVA49hSsJCAjgf5jA7xSfb/u40pmhQrYIRJI0fvx4DR06VO3atVOHDh00c+ZMnTlzRsOHD6/orgEAgApmm0B077336qefftKkSZOUkpKiNm3aaOXKlRcNtAYAAPZjm0AkSWPHji32EhlQlLe3tyZPnnzRZVMAlR+fb1yKw1zNvWgAAAC/Y7Z4MCMAAMDlEIgAAIDtEYgAAIDtEYiAC3Tp0kXjxo0r9/XWq1dPM2fOLPf1AnZSks/3sGHDrGfY4feLQASXNW/ePPn7+ysvL88qy8rKkqenp7p06eLUdv369XI4HDp8+HA59xJASfD5hqshEMFlde3aVVlZWdqxY4dVtmnTJoWFhSkhIUHnzp2zytetW6e6deuqQYMGFdFVANeIzzdcDYEILisqKkq1atXS+vXrrbL169erb9++ioyM1NatW53Ku3btqoKCAk2dOlWRkZHy8fFR69at9emnnzotd/fu3erdu7eqVq2q0NBQDR48WD///PMl+/HPf/5TgYGBWrx4sSTp+PHjuueeexQUFKTq1aurb9++OnLkiNW+8PT6a6+9plq1aik4OFhjxoxRbm6u1SYtLU133HGHfHx8FBkZaS0bsAtX/Xzn5+dr/PjxCgoKUnBwsJ588smLfil95cqV6tixo9Xm9ttvdzp71a1bt4ueeffTTz/Jy8tLa9asueZ9hfJBIIJL69q1q9atW2e9X7dunbp06aLOnTtb5WfPnlVCQoK6du2qqVOn6v3339e8efO0Z88ePfbYY/rDH/6gDRs2SJLS09PVrVs3tW3bVjt27NDKlSuVmpqqe+65p9j1L1myRPfdd58WL16suLg45ebmqmfPnvL399emTZu0efNmVa1aVb169VJOTo5TPw8fPqx169Zp0aJFWrhwoRYuXGjVDxs2TMePH9e6dev06aef6s0331RaWloZ7EHAdbna51uS/vKXv2jhwoWaP3++/v3vf+vUqVNaunSp03xnzpzR+PHjtWPHDq1Zs0Zubm666667VFBQIEl64IEHtGTJEmVnZ1vzfPjhh7ruuuvUrVu30tuBKF0GcGHvvPOO8fPzM7m5uSYzM9N4eHiYtLQ0s2TJEtOpUydjjDFr1qwxksyRI0eMr6+v2bJli9MyRo4cae677z5jjDEvvPCC6dGjh1P98ePHjSRz4MABY4wxnTt3No8++qh54403TGBgoFm/fr3V9oMPPjBRUVGmoKDAKsvOzjY+Pj7mq6++MsYYM3ToUBMREWHy8vKsNgMHDjT33nuvMcaYAwcOGElm27ZtVv2+ffuMJDNjxozfusuASsPVPt/GGFOrVi0zffp0631ubq6pXbu26du37yW346effjKSzK5du4wxxpw9e9ZUq1bNfPzxx1abVq1amSlTplzjHkJ5stVPd6Dy6dKli86cOaPt27fr9OnTaty4sWrWrKnOnTtr+PDhOnfunNavX6/69esrKytLv/76q2699VanZeTk5Kht27aSpG+//Vbr1q1T1apVL1rX4cOH1bhxY0nSp59+qrS0NG3evFnt27e32nz77bc6dOiQ/P39neY9d+6c0ynz5s2by93d3Xpfq1Yt7dq1S5K0b98+eXh4KDo62qpv0qSJgoKCSriXgMrJ1T7fGRkZ+vHHHxUTE2OVeXh4qF27dk6XzQ4ePKhJkyYpISFBP//8s3Vm6NixY2rRooWqVKmiwYMHa/78+brnnnu0c+dO7d69W1988UXp7TyUOgIRXFrDhg1Vu3ZtrVu3TqdPn1bnzp0lSeHh4apTp462bNmidevWqVu3bsrKypJ0fkzAdddd57Scwt8tysrK0h133KFXXnnlonXVqlXLet22bVvt3LlT8+fPV7t27eRwOKz5o6Ojix3zU7NmTeu1p6enU53D4bD+pwngPFf7fF+tO+64QxEREXrnnXcUHh6ugoICtWjRwumy+QMPPKA2bdrohx9+0IIFC9StWzdFRERc03pQvghEcHldu3bV+vXrdfr0aU2YMMEq79Spk/71r39p27Zteuihh9SsWTN5e3vr2LFj1v9YL3T99dfrH//4h+rVqycPj0sf/g0aNNBf/vIXdenSRe7u7nrjjTes+T/++GOFhIQoICCgRNvTpEkT5eXlKTEx0frX6YEDB5Senl6i5QGVmSt9vgMDA1WrVi0lJCSoU6dOkmR9Vq+//npJ0smTJ3XgwAG98847uvnmmyVJ//73vy9aR8uWLdWuXTu98847WrJkibUOuC4GVcPlde3aVf/+97+VlJTk9D/Czp07669//atycnLUtWtX+fv764knntBjjz2mRYsW6fDhw9q5c6fmzJmjRYsWSZLGjBmjU6dO6b777tP27dt1+PBhffXVVxo+fLjy8/Od1tu4cWOtW7dO//jHP6wHucXFxalGjRrq27evNm3apOTkZK1fv15/+tOf9MMPP1zV9kRFRalXr1764x//qISEBCUmJuqBBx6Qj49P6ewwoBJxpc+3JD366KOaNm2ali1bpv379+vhhx92+sdKtWrVFBwcrLfffluHDh3S2rVrNX78+GK37YEHHtC0adNkjNFdd91VejsNZaOiBzEBV5KcnGwkmSZNmjiVHzlyxEgyUVFRVllBQYGZOXOmiYqKMp6enqZmzZqmZ8+eZsOGDVab77//3tx1110mKCjI+Pj4mCZNmphx48ZZA6ULB10W2rt3rwkJCTHjx483xhjz448/miFDhpgaNWoYb29vU79+fTNq1CiTkZFhjDk/qPrCAZiPPvqo6dy5s/X+xx9/NH369DHe3t6mbt265v333zcREREMqobtuNrnOzc31zz66KMmICDABAUFmfHjx5shQ4Y4faZXr15tmjZtary9vU2rVq3M+vXrjSSzdOlSp2345ZdfjK+vr3n44YdLaW+hLDmMueABCwAA4Dc7cuSIGjRooO3bt1uX3OC6CEQAAJSi3NxcnTx5Uk888YSSk5O1efPmiu4SrgJjiAAAKEWbN29WrVq1tH37ds2bN6+iu4OrxBkiAABge5whAgAAtkcgAgAAtkcgAgAAtkcgAgAAtkcgAlBpvfbaa4qIiJC7u7scDofWr19/TfOvX79eDodDDodDR44cKZM+ugK7bCfwWxCIAEg6/8vjhV+a7u7u8vf3V1RUlIYPH66dO3dWdPcusnPnTk2YMEHHjh1TvXr1FBMTc8nflxs2bJgcDoe6dOlSrn0cM2aMHA6HQkNDncobNWokh8Oh8PDwYsuHDh1ant0EIAIRgAt4eXmpffv2CgwM1MGDB7Vw4ULFxMTo3XffreiuOdm7d6/T661bt7rc04ALf/wzLS1NBw4ckCSlpKTo0KFDkqQff/xRhw8ftl4Xlhf+sCiA8kMgAuCkVq1a2rp1q3744Qdt27ZNERERysvL00MPPaT9+/dLkn744QfddtttqlOnjnx8fOTj46MWLVpo5syZMsYoMzNTVatWlcPhcApSu3btss5Cbd269ZJ92L17t/r376/g4GB5eXmpfv36mjhxos6ePSvp/BmfwYMHW+2rVKlyyctB9erVs378c8OGDdb6L7y8tm/fPnXr1k0+Pj5q0qSJVqxY4VS/f/9+DRw4UDVr1pSXl5eaNm2qt95667L7sjAQSdKmTZuc/hsSElJsedH5Tpw4oREjRig8PNzaDy+88ILy8vKc1vPhhx+qffv28vX1lb+/v3r16qWkpKTL9u3xxx+Xw+GQr6+vvv7668u2BWyhAn9HDYAL6dy5s5FkIiIinMqXLVtmJBlJ5oknnjDGGPPNN98YSaZ27dqmbdu2JiQkxGrzxhtvGGOMGTVqlJFkYmNjrWVNnjzZSDKNGze+ZD/27t1rqlataiSZqlWrmqZNmxqHw2EkmVtvvdUYY8zzzz9v6tevb60zJibGxMTEmBMnTly0vH79+pkaNWoYScbf399qm5iYaNatW2ctw8fHxzRq1Mj4+PhYbU+ePGmMOf+DoYGBgUaSqV69umnRooXVp+eee+6y+zUyMtJIMkOGDDHGGPPII48YSWbatGlGkhkxYoQxxpgxY8YYSSYkJMQYY8zPP/9s6tSpY/WlVatWxsPDw0gyw4cPt5b/yiuvWNvQuHFjEx4ebiQZPz8/s3fvXmOMcdrO5ORk8+yzzxpJxtfX16xZs+ay/QfsgkAEwBhz6UB08uRJ68v0tttuM8YYk56ebpKTk602+fn5plOnTkaS6dixozHGmJ07d1rz7du3zxhjTIsWLYwk8+KLL16yH0OGDLHC0LFjx4wxxsyYMcNa1tq1a40xxixYsMAqu5KhQ4caSaZz585O5UWDQuGvnX/++edW2b/+9S9jjDHDhg0zkkyLFi3MmTNnjDHGzJw50wpSmZmZV9yeyMhIY4wxbdq0MV5eXiYjI8P4+vqaRo0aGWOMadmypZFkBgwYYIwxZsqUKUaSCQ0NNWlpacaY/4VTh8NhDh48aM6cOWN8fX2dgllubq5p166dkWT+8Ic/XLSdhcHL19fX2pcAjOGSGYDLKigouKjMw8ND06dPV0REhDw9PeXu7q6NGzdKOn+ZR5Latm2rmJgYSdL8+fP1/fffa/fu3XI4HE6Xuy60fft2SecvG9WpU0eSdP/991v1O3bsKJ0Nu0Bhn5o1a2aVpaamSpK2bdsm6fylPD8/PzkcDo0bN06SdPbsWX333XeXXG7heKDk5GTt3btX3333ndq1a6eAgADFxsbq4MGD2rdvn3bv3i3pf5fLCteZmpqqkJAQORwO9evXT5JkjFFCQoL27NmjX3/9VZI0efJkORwOeXp6WvuouMuSc+fOlST97W9/U9euXa9xLwG/Xx4V3QEArq3o2JbCsDBu3DhrbFCjRo1UvXp1HT58WD///LPy8/Ot9g8//LASEhL0wQcfyN/fX5LUtWtX1a1btxy34OoEBQVJOh/2CpkLfuqxRo0aatCgwUXzuru7X3K5RccRTZs2TQUFBVZZp06dtGbNGk2bNs1a14UDqv39/Z1CWiFfX1+n902bNr3oLrvg4OCL5qtataqysrL02muv6dZbb5WPj88l+w7YSgWfoQLgIoq7ZLZ9+3YTERFhJBl3d3ezZ88eY8z/Ln316NHDGGPM2bNnTevWrS+a/+zZs6Z69erWmBZJZuHChZftR9FLZsePHzfG/PZLZg899JCRZNq1a+dUfuHYGmOMSU5OtsoWLFhgjPnfJbcGDRpY44qMMeann34yH3300RXXXzjGqnAM0IoVK5zWX1geEBBg8vPzjTH/G29VrVo1p8uTmZmZZtGiRcYYY86cOWONeXrsscdMQUGB1W7nzp1m8+bNF23nZ599Zvz9/Y0kc/vtt5vc3Nwr9h+wAwIRAGPM/wKRl5eXiYmJMbVr17YGDnt4eJh33nnHanv//fc7DeStUaOGFXwuHIP0+OOPW239/PzML7/8ctl+XM2gamOuLRDNmjXLatuiRQsTExNjfv3116sORPv37zcBAQHW2Js2bdqYunXrGnd394u2tzgDBgywlunm5mZOnz5tjDkfGL28vKy6Xr16WfOkpaWZ6667zvqbtG7d2tSvX994eno6bfPLL79szR8eHm5at25t/S0mT55sjLk4+K1evdpazuDBg52CFGBXjCEC4CQnJ0fbtm1Tenq6GjZsqKFDhyohIUEPPPCA1eb1119X3759VbVqVf3yyy+aMGGC7rjjjmKXN3r0aDkcDklS//79VbVq1cuuv2nTpoqPj9ddd90lLy8vHTx4UPXq1dPTTz+tzz//vETbNGLECA0YMECBgYHavXu3EhISnC7tXUlUVJTi4+M1cOBA+fr6as+ePSooKFCvXr30wgsvXHH+opfNmjdvbl2eq1Klijp06FBsu5o1a2rr1q0aPny4goODtWfPHp09e1Y333yzZsyYYbWbOHGiFi1apPbt2+v06dM6dOiQQkJCNHr0aPXv37/Y/nTv3l3z58+Xw+HQBx98YI2HAuzMYcwFF8kBoBRlZ2crNDRUGRkZWrNmjbp161bRXQKAizCoGkCZ+cMf/qA9e/YoIyND0dHRhCEALoszRADKTOFt4O3bt9eiRYvUsGHDiu4SABSLM0QAygz/3gJQWTCoGgAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2B6BCAAA2N7/A97RxQ88xMEjAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a countplot for the number of days of the week.\n",
    "sns.countplot(data = foodhub_df, x = 'day_of_the_week') \n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the output.\n",
    "plt.title('Total Number of Days of the Week', fontsize=12, fontweight='bold')\n",
    "plt.xlabel('Day of the Week', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Number', fontsize=10, fontweight='bold')\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rating\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 4 unique ratings. The values are Not given, 5, 3, 4.\n"
     ]
    }
   ],
   "source": [
    "# Check for unique ratings in the dataset.\n",
    "ratings = foodhub_df['rating'].nunique()\n",
    "\n",
    "# Get the names of the days of the week.\n",
    "ratings_values = foodhub_df['rating'].unique()\n",
    "\n",
    "# Convert the unique rating values to a string.\n",
    "rating_value = ', '.join(ratings_values)\n",
    "\n",
    "# Output the unique ratings and value names.\n",
    "print(f'There are {ratings:,} unique ratings. The values are {rating_value}.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Countplot of Ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a countplot for the number of ratings.\n",
    "sns.countplot(data = foodhub_df, x = 'rating') \n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the output.\n",
    "plt.title('Total Ratings', fontsize=12, fontweight='bold')\n",
    "plt.xlabel('Rating Value', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Number', fontsize=10, fontweight='bold')\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Food Preparation Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Food Preparation Time Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram for the food preprations times.\n",
    "plt.figure(figsize=(10, 4))\n",
    "sns.histplot(foodhub_df['food_preparation_time'], edgecolor='black', kde=True)\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the histogram.\n",
    "plt.title('Histogram for Food Preparation Times', fontsize=12, fontweight='bold')\n",
    "plt.xlabel('Food Preparation Time', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Number', fontsize=10, fontweight='bold')\n",
    "\n",
    "# Show to plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Food Preparation Time Boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2tl544YVCfa1atcrj+XVfjrp160qS/vjjD0mn9gSMGTNG1157rVq0aGFNL2md7n1J0tChQ9WrVy916NBBKSkpRfZRmtfh2f6fViTuy963b99CrwNjjNLS0tS1a1cfVglUfOz5AMqAw4cP68cff1Rubq5++uknzZs3z5pXsMLs3bu3nnrqKeXk5Gjq1KkaPny4rrzySn366ac6cOCAJOmSSy7R5ZdfLklKSUnRAw88IOnUJ4LTp09Xjx49lJGRob/97W9av369QkNDC9XyzDPPKDAwUJUqVdIzzzxjTe/Vq1exy9CnTx/NmDFDkvT888/L5XIpJibG44sE77jjDmujy/1T1Dlz5ujqq69WUFCQmjZt6vHJZkk1b95cTZo00aZNm3TgwAHdfffd6tevn1asWGF9b4rT6dRtt9121n2fLjIyUjt37pR06pPvli1bKiIiQk2bNj2rfvz9/XXXXXfp3Xff1cmTJ3XDDTdo8ODBiomJ0b59+7RhwwZNnTpVn3zySYm/hLE40dHR6tq1q+bMmaOsrCzdfvvtGjJkiLZv3653333XanfXXXdZP9966y1Jp75zpnr16mrQoIHeeOMNZWVlSZK6du1qHfrStm1brVixQpmZmbrzzjs1ePBgrV27VpMmTTrn2ksrKipK3bt31+zZs7V9+3bddNNNuv/++xUWFqbdu3dr9erVmjp1qpYtW6batWt73Hf37t3q27ev+vTpo/nz51uHXLlcLnXr1k2SlJCQoK1bt+rYsWMaPXq0mjVrpjfffNP60OBsuB8O+dZbb8npdGrFihXWBwinc/8fmjJliurUqSOHw6HWrVtbh/ud7mz/TyuSLl26KDY2VkeOHNHEiRMVFRWlLl26KC8vT7t27dLSpUu1du1abdy40delAhWbnSeYAPjTmU44l2SuuOIKjxOnz+ZSu/369bPmvffee8YYY8aMGWNNe/jhh6227ieVul/1puBWrVo1c/jwYau9t0t43nHHHV6X5fRLeLqftOx+c7+06pnG7fSTbEt7qV1vJ756435ydsGtU6dOZ+y3YLr71aeOHz9e7KV2SzImZ3Op3e3bt1+wS+0uWbKkyJOkGzRoYP1+LpfaLUn7ohR3qd2C286dO40xnmPZpEmTIpfnpZdesvp2P+G54BYTE+Nxydyi+i7qNbd7924TEhJSqD/3E93dx2/dunVFvh8UPN75+D91f28o6NeYM5/s7s35+p6PAt4uXOGtvlmzZnm91G5J/n8AnDsOuwLKmODgYF122WUaNmyYFi5c6HHi9IABAzRv3jx1795dUVFRCgwMVPXq1XXvvfdq1apVat26tSRp9uzZmjBhgqRTJ7Y++OCDkqQnnnjCOinz/fff1/z58ws9/pdffqnBgwcrNjZWwcHB6t69u5YsWXLGwzD8/Pz0xRdf6P3331ebNm1UqVIluVwuNWzYUE8//bSWL1/u8UltTEyMvv32W7Vo0ULBwcHnNGYF2rRpo1WrVqlv376qUaOGAgMDFRkZqW7dumnu3Ll6+OGHz8vjDB8+XP3791f16tXP+RPiypUra9myZXrxxRd1+eWXKzg4WCEhIWrQoIF69+6tL7/8UldeeeV5qVs6dbjQr7/+qkGDBlmflIeHh+vqq6/WV199pdGjR3u0HzNmjL7++mt16tRJ4eHhcjgcql27tgYOHKhff/3V43Crjh07avbs2briiivkdDqVkJCgMWPGeOxB84X4+HitXr1a//znP9W4cWMFBQUpLCxMjRs31r333qsZM2aoVq1ahe7Xpk0bzZkzx9qTkJCQoH//+98aNmyY1WbIkCF66aWXlJCQoJCQEHXu3FkLFixQXFxcqeqcO3eu2rRpo+DgYNWrV0/vvvuu/v73vxfZvmnTppo4caKaNGnidU/H6c72/7Si6dGjh3755Rfdc889qlmzphwOh2JiYtS8eXM9/vjjhS7WAOD88zPG5jPFAJQ5nTt31uLFiyVJO3fuLHT4CXCxWLRoka655hpJp84LKAjxAIDzgz0fAAAAAGxB+AAAAABgC8IHAAAAAFtwzgcAAAAAW7DnAwAAAIAtCB8AAAAAbFHqbzjPz8/X/v37FRYWViG/CRUAAABAyRhjdOLECVWvXl3+/t73b5Q6fOzfv7/IL2UCAAAAcHHau3evatas6XV+qcNHWFiY9QDh4eGl7QYAAABAOZeamqpatWpZGcGbUoePgkOtwsPDCR8AAAAAzng6BiecAwAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGwR6OsCAF85dOiQUlJSfF0GAOA8i4iIUNWqVX1dBoAiED5wUTp06JD+3z33Kic7y9elAADOM4fTpc8/m0gAAcogwgcuSikpKcrJzlJG3U7KD4rwdTnABeWfkazgnUuUUedq5QdX9nU5wAXln5ki7VislJQUwgdQBhE+cFHLD4pQfqUYX5cB2CI/uDKvdwCAT3HCOQAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYItDXBQBAWZWXla78nGxfl3HO/NJTFZiVpey0VBlT/t/2/R1OBbhCfF0GAKAUyv9aCAAugLysdGVsWqJQZwXYQZybJWOMAg/+JgW6fF3NOTuZna/gJlcTQACgHCJ8AEAR8nOyFer0V48beyksItLX5ZwTv9wMOY5tV050PZnAYF+Xc05OpBzX7JnTlZOTTfgAgHKI8AEAxQiLiFREdKyvyzgnfjnpcuYdU3ZUjIyDDXYAgO9UgOMJAAAAAJQHhA8AAAAAtiB8AAAAALAF4QMAyqhqa3/Wg12by3kyVZLUcO509bu1g4+rAgCg9DjhHABKofPY59Ro3kxt7NFbiY8+6zGvw9v/0qUzv9bmLjdq0T9ePG+Pub1TV+1p0/G89VdSoQf/UMsvPlL1NSsVcvyY0qJjte3aHvr1rgeU73BIklp+9p5aff5BofvmuIL0yYzl3vs+fEAdxr2s6mt/UW5QsLZ0uVEr/jZYJoDVEwBURLy7A0ApnYiNU73Fc/TTQ/9QnitIkhSQnaX6C7/XiSrVzvvj5bmCrMexU+TeXfLLz1fio88qpXq8onZt09VvvKDAzEwt7/+4JGlt777a2POvHvf7y1P9daTRpV779cvLU7fnHlFGZLSmvz5BIUlHdc2rzyk/IFAr/zb4gi4TAMA3CB8AUEpH6zdW+IF9qrN0vrZd21OSVOfH+ToZW02pcdU9G+fnq/nX49Vk9hSFHD+mlBoJWnX3A9rZsYvVpNbKRLV//1WFHjmkQ42bakuXGz26aDh3utq//6omTP1RkhS+f6/affBvVfl9nRyZGToeX0cr7xusP6640rpPn3u7a1PXmxS54zclrPpVWaHhWt3n79rUo3eJl3Nv66u0t/VV1t8nqtXUun27dMl3k63wkRscotzgPy/jG7V9s6L27FDi4GcL9Veg5q/LFLlnh2aN/kAZkdE6Vk/6+d4Bavvxm/rlnoetvSoAgIqDcz4A4Bxs7tpLjf43w/q70f+ma/MNNxVq12LSx2r4w3dKHPysvv5witbdereuHTNM1db9IkmqdPigbnjhCe1u20nfvPuVfu9+i9p+8laxjx2Yka49bTrouzEf6pt3J2lvq6vUbfijCj18wKNds2lf6midOpr65gRtvPF2dRj3L0Xs3WXNv/Gf96vz2OfOarmdaSeVFRbhdX6TOdOUXDNBB5te4bVN1Y3rlFS7vjIio61p+1q1lyv9pCJ3bz+regAA5QPhAwDOwdZreyrut9UKPbRfoYf2K27jGm29rqdHG//sbLWY9LEWPz5C+1q114lqNbXlhl7ael1PNZn1jSTp0u++Vmq1mlr+4BNKqVVb267tqc1dCocYd0n1GmlTz946Xru+Umsk6Je+A5VaraYSli32aLe3VTttvvZapVavqTW336fM8MqqvvZna/7J2GpKjyr5FymG/7FHl06fpI09bityfkB2luovmK3fu95cbD8hx496BA9JyqgcZc0DAFQ8HHYFAOcgs3KU9rTpqEbzZkjGaE+bjsqMiPRoE7F/jxxZmer5zEMe0/1zc3S0XmNJUuW9O3W4cVOP+YeaNCv2sQMz0tXqs/cVvzJRIUlH5J+Xp4DsLIUe8dzzcax2/T//8PNTRmSMgpOTrEkLn3ypxMsbcvSQegwbqB1Xd9HvXsJH7aUL5MhI15YzhCcAwMWH8AEA52hz11666p3RkqQfBz1TaL4jM0OS9P2L45QWXcVjXp7DWerHbffha6qxermWP/C4UqvXUq7TpS4v/UMBOTke7fIDPd/qjZ/kZ/LP+vFCjh3WjU8+oEOXXK4lj3o/TKvJnGna07Zjob0ap0uPjFHs5g0e0wpCUXpkzFnXBwAo+zjsCgDO0d5WVykgN0f+ebna17J9ofnH4+sq1+FU6OGDSq0R73FLqxInSUquVUdVTtsQr/r7+mIft+rGNdrS5SbtuupaJdVpoPSoGIUd2n/+FsxNyNFDuvGff9fRBpdo0RMjJf+iVx9hB/9Q9bU/6/eut5yxz0OXNFPUrm0KctsLU/PXZcoKCdXx+LrnrXYAQNnBng8AOEcmIEBffTTN+v10OSGVtK73vWr3wVj5mXwdvLSFnGknVXXjGuWEVNKWLjdp41/+qmZTP9OVH72mTd1uVezWjWo4b0ahvtyl1ohXnaXztfvKq2Xkp9YT35WfMWdd/zWvPKu0mCpeL28bcvSQbvrn33WiSnUte2CIglKOW/Myojz3UDT637dKj4rxuDpWgdpLF6jNJ2/p64+/lSTtu6KdjsfX1bWvDNPy+x9TyPFjaj3hHW288XblO0u/RwgAUHYRPgDgPMipFFrs/J/7DlRGRKSaT/pE4Qf3KbtSmI7Wb6LVd90vSTpZpZrmPvtvtf/gVV06fZKONLpMP/cbpM6vjfDa50/9n1Dn10ao15B+ygyvrLW395Mz/eRZ1x565ICMv5/X+TV/Xa6I/XsVsX+v7rm7q8e8D/635s8/8vPVaO4Mbe5yU5EhzJl2QpH7dll/m4AAzXnhLXUc97JuHtL31JcMXn+jfu474KyXAQBQPvgZU4qPySSlpqYqIiJCKSkpCg8PP991ARfUli1b1L9/f6VdcpPyK3FsOQrLOZksx56VuuP/9VNEdMmvBFUW+eWky3lks7JjG8k4Qs58hzIs5dgRffX5BOXEt5EjtLKvy0EZ5J92VJU2ztCHH36ohg0b+roc4KJR0mzAOR8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALbgez4AoBgn3L5Qr7zyy82QIzlZOQFHZQKDfV3OOakIzwcAXMwIHwBQBH+HUyez8zV75nRfl3LucrPkSNmrnIhaUqDL19Wcs5PZ+Qp28A3oAFAeET4AoAgBrhAFN7laOTnZvi7lnPmlJ8mZuk+5cZfKhET5upxzFuxwKsBVvr8sEQAuVoQPAPAiwBVSITZy/f1y5XK5lFspXPmVKvu6HADARYwTzgEAAADYgvABAAAAwBYcdgVbpaSkKD093ddl6PDhw8rKylJ2WqqM8f2/gT/HsAMo5/Ky0pVfBs6R8ktPVWBWlg4fPqywsDBfl6OQkBBFRET4ugygzPD9VhcuGikpKXr77beVk5Pj61KUkpIiY4wCD/5WJq7+czI7X8FNriaAACiX8rLSlbFpiUKdZeCAitwsGWM0ZcqUMrHR73A4NGjQoDJRC1AWED5gm/T0dOXk5OjWW29VbGysT2tJSkpSQkKCcqLr+fx7D06kHNfsmdOVk5NN+ABQLuXnZCvU6a8eN/ZSWESkT2vxy82Q49h2denSRVFRvr2625EjRzR16lSlp6cTPoD/Q/iA7WJjY1WtWjWf1uB0OlW5cmVlR8XIONjgB4DzISwiUhHRvv1wyS8nXc68Y4qLi1N0dLRPawFQWBnYPwoAAADgYkD4AAAAAGALwgcAAAAAWxA+AAAAANiCE85RZuxN2avhi4ZrzrY5Opp+VNXCqunmRjfr+U7PKzrkz5MGp26aqvd/eV+rDqxSUkaSVj+4Ws3jmp/VYx3PPK7Pkz7XhoNblZR9SKGOSDWPukY31Rqg4MBT14U/mZOsT7YO1R/pW5WWm6wwR5SaRXbWzfGPKDgw1GvfaTkpmrRrtNYfXyI/+alF1PW6vc6TCgrgxHYAF6ekww5991mcfvslTCdTAxQRlavL26eo592HFBqeZ7Wb+VlV/bKoso4fcSjQYRRfP0O97juoOo3P7vuh3nzzWj37bGUdPSpFRkrXXy+NGSNVr35qfmam9NBD0qpV0qZN0l/+In37bQmWI0l65BFp5kzJ31+67TbpzTelUO+rBACnYc8HyoQdx3eo1UettDVpq7687UttG7xN7/d8X/N3zle7j9spKSPJapuWnaYO8R005voxpX68g2kHlZyXrNtqDdLzzb9R3/ov6LfkpZq4faTVxs/PX5dHddaAxm9oZIvp6lv/Bf2eskJf7Hip2L4/2TZUB9K369Em72tg43HaemKV/rv9hVLXCgDl2ZEDTo16pIEO/+HU/c/s1ovjf1efwfu0eXWoXnmsgdJSA6y2VWtk6c6Bf+i5D7boH//epui4bL35TF2dSA4o5hEKa9z4oD7++KQ2b5amTJG2b5d69/5zfl6eFBwsDR58KpiU1N13S7/9Js2bJ333nbRkidS//1mVBlz02POBMmHg7IFyBjg19//NVbDj1PduxEfEq0W1Fqr3Vj0Nmz9M7/3lPUnSPZffI0nalbyr1I/XJLqJBsUOUnblRjKOEMUG1VKv+EEav3WY8kyuAvwCVSkwXJ3ibrfuE+2qrk5xt2ve/k+99nsgfYd+S16qZ5r+Vwmhl0qS7qz9tN7+fZBuy35clZ1VSl0zAJRHk96uocBAo8GjdsjpMpKkqCo5qlUvQ8/1a6zpE+LUZ/AfkqQ21yZ73Ld3//1aOidaf+wMVuMWJ0v8mF27blSrVvUUHS0lJEhPPy3dfLOUkyM5HFKlStJ7p1YpWrpUSk4urrdTNm2S5syRfv5ZatXq1LRx46QePaSxY//cqwKgeOz5gM8lZSTpf9v+pwGtBljBo0BcaJzubnq3vvrtKxljStxnv2/7qfOEzmdVR0buSQUFhCrAr+hMnpx9WKuT5qtBeEuvfew4uU4hAWFW8JCkxpXbyk/+2nliw1nVAwDlXVpqgDauClOnG49ZwaNARFSu2lybrF8WV1ZRb++5OX5KnB2t4Ep5qlk3w5r+73/W04SxtUpcQ1KS9N//Su3bnwoepbVsmVS58p/BQzq118TfX1qxovT9Ahcb9nzA57Ye2yojoyaxTYqc3ySmiY5nHteR9COqUqlkew6qhVZTvskvcQ0nc45r9r6P1KHqrYXm/WfL01p7fJFy8jPVLLKT7qk33Gs/qdlHFebw/Ebdgr0oqTlHS1wPAFQEh/e7ZIyf4uIzi5wfF5+p9JPROpESqPDKuZKkdcvD9PGoBGVn+Ss8KlePjtqu0Ig/zwuJis1WRFTOGR975MgQffyxlJ4uXXnlqcOkzsXBg1KV01ZBgYFSVNSpeQBKhj0fKDPOtGfDGeAscV+jrh+libdMLFHbjNyTevv3R1QtpK5urPlQofl/rf0PDWv2pR5u9IaOZO7V5F1jS1wHAEBF7tlwFxj454dFjZqnadi7W/TP17fp0lap+ujlBKUm//lZ6X1P7tUtfzvz1v6gQRlavVqaO1cKCJDuvffMdQC48Agf8Ln6UfXlJz9tOrqpyPmbjm5SbEisKgdVPu+PnZmXpnGbBigooJIeavSaAvwL75OPcMYoLriOLo/qrLvrPqclhyYrJftIkf2FO2N0IifJY1qeyVVabqrCHTHnvX4AKMtiq2fJz8/o4J6gIucf3BOksIgchYT+GT5cQfmqUiNbdZuk697H98k/QPppTlSR9y9OdLRRw4ZSly7SpEnS7NnS8uWlXhTFxUmHD3tOy809dVhXXFzp+wUuNoQP+Fx0SLS61Ouid39+Vxk5GR7zDp48qP+u/6/6Ne933h83Iz9Db2x5TAH+Dg1o9IYc/q4z3sfo1AoyJz+7yPl1Q5spPe+Edp/caE3bnLJSRvmqE3bZ+SkcAMqJ0PA8NbnipBZ/F63sLD+PeSlJgVq5oLKuvOF4sX0YI+Xk+BXb5kzy/y/bZGWVvo927U6dmL5q1Z/TFiw41XfbtudUHnBRqRDhIzMzU1u2bFFmZtHHlKLse7v728rKy1LXz7tqye4l2puyV3O2zVGXz7qoYXRDPd/peattUkaS1hxco41HTm3gbz66WWsOrtHBk3/uhn/mh2d077R7vT7eiewTGntorLLzM3VvvRHKyEtTSvZRpWQfVb45dWzx+uOJ+unwt/ojfZuOZv6h9ceX6L87Xla9sOaKCaohSdp5Yr2Gr75Zx7MOSZKqhdTVpZWv0uc7XtDOE+u1LXW1Ju0crVbRXbnSFYCL0h0D9yk3x19vDa2rresrKemwQ7/9HKY3n6mrqjWz1fPuU++fWZn++vaTOO3YFKJjhxzavTVYE/9dS8lHHWrZMdnqb/wrtTTtE++7GnZuDtUPPzTW+vUB2r37VEC46y6pXr1TAaLAxo3SmjWn9lykpJz6fc2aP+evXCk1biz9cepCXGrSROrWTXrggVPzli6VBg2S7ryTK13Bd8rjNnCFOOF8z5496t+/vz788EM1bNjQ1+WgFBpEN9DPD/ysEYtG6PbJt+tw2mEZGd3a5FZ9dstnCnH8+QV9MzbP0H3T77P+vnPKnZKk4Z2Ga0TnEZKkAycPaE/KHq+Pt/bwWu3I3iFJem71jR7zXmoxSzFBNeT0D9KPh6Zq8q6xys3PUaSrqlpEXaeuNf587Oz8TB3K3KU8k2tN+1v9f2nSzlF6Y+OD8vPz1xVR1+n2Ok+VfnAAoByrWiNbT7+1Rd99HqePXk7QieRAGeOnFlcl674n98gZdOpEDH9/o4P7XFr2Ym2lpQaoUlieEhqm6x//3qbqtf/cZZF0xCm/Yj46dbrytWpVgmbPDld6ulSt2qnQ8OyzksttB3ePHtLu3X/+3aLFqZ8F54Wkp0ubN5+6PG+B//73VOC47ro/v2TwrbfOdYSA0iuP28AVInygYqhdubYm3DzB+nv4wuF6bflrWndona6seaU1vV/zfmc8DMu9n6J0qNlB4xPGKzv21Pd8FKVRRGs92bT4k9YbRbTW++3WeEyr5IjQ/Q1HF3s/ALiYxMTlqN8/9lp/z5xYVT9MjdW+ncGq2+TUt5c7nEYPPb/bWxeWJ17dXuz8GrXT9dRT/9ONN96o6Ohor+127Sr+cTp3LnyCelSU9MUXZywRQDEIHyizRl4zUrUr19byfcvVpkYb+Rf3URcAoNy48d5Diq6arZ2bQlS7Ubr8eXsHLhqED5Rp97W478yNAADlTvuuxZ9oDqBi4rMGAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABswQnnsN2RI0d8XYKSkpKUnJysnICjMoHBPq3lRAonXQKoGMrC+5lfboYcyck6ePCgsrOzfVpLWVjfAWUN4QO2CQkJkcPh0NSpU31dilJSUrRs2TLlRNSSAl1nvsMFdjI7X8EOp6/LAIBS8Xc4dTI7X7NnTvd1KVJulhwpe7V7925FRET4uho5HA6FhBT9fVLAxYjwAdtERERo0KBBSk9P93Up2r59u5YvX67cuEtlQqJ8XY6CHU4FuFg5ASifAlwhCm5ytXJyfLunQZL80pPkTN2n2267TfXq1fN1OQoJCSkTIQgoKwgfsFVERESZeBM+ceKEXC6XciuFK79SZV+XAwDlXoArpEx8iOLvlyuXy6UqVaqoWrVqvi4HwGk44RwAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQJL2jArK0tZWVnW36mpqRekoHOxe/duX5eAcoLXCgBUbLzP42JQHl/nJQ4fo0aN0siRIy9kLefs5Zdf9nUJAACgDGCbACibShw+nnnmGT3++OPW36mpqapVq9YFKaq0hg0bpoSEBF+XgXJg9+7drJgAoAJjmwAXg/K4PVPi8OFyueRyuS5kLecsISFBDRs29HUZAADAx9gmAMomTjgHAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYokKEj/j4eH344YeKj4/3dSkAAACALcrjNnCgrws4H4KCgtSwYUNflwEAAADYpjxuA1eIPR8AAAAAyj7CBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsE+roAwJf8M1N8XQJwwflnJHv8BCoy3teBso3wgYtSRESEHE6XtGOxr0sBbBO8c4mvSwBs4XC6FBER4esyABSB8IGLUtWqVfX5ZxOVksInZABQ0URERKhq1aq+LgNAEQgfuGhVrVqVlRMAAICNOOEcAAAAgC0IHwAAAABsQfgAAAAAYAvCBwAAAABbED4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADYgvABAAAAwBaEDwAAAAC2IHwAAAAAsAXhAwAAAIAtCB8AAAAAbEH4AAAAAGALwgcAAAAAWxA+AAAAANiC8AEAAADAFoQPAAAAALYgfAAAAACwBeEDAAAAgC0IHwAAAABsQfgAAAAAYIvA0t7RGCNJSk1NPW/FAAAAACh/CjJBQUbwptTh48SJE5KkWrVqlbYLAAAAABXIiRMnFBER4XW+nzlTPPEiPz9f+/fvV1hYmPz8/Epd4PmQmpqqWrVqae/evQoPD/dpLRcTxt03GHffYNx9g3H3DcbdNxh332Dczw9jjE6cOKHq1avL39/7mR2l3vPh7++vmjVrlvbuF0R4eDgvGh9g3H2DcfcNxt03GHffYNx9g3H3Dcb93BW3x6MAJ5wDAAAAsAXhAwAAAIAtKkT4cLlcGj58uFwul69Luagw7r7BuPsG4+4bjLtvMO6+wbj7BuNur1KfcA4AAAAAZ6NC7PkAAAAAUPYRPgAAAADYgvABAAAAwBaEDwAAAAC2KDfhY9SoUWrdurXCwsJUpUoV3Xzzzdq8ebNHm8zMTA0cOFDR0dEKDQ3VbbfdpkOHDvmo4orhTOOelJSkRx55RI0aNVJwcLDi4+M1ePBgpaSk+LDq8q8kr/cCxhh1795dfn5++vbbb+0ttIIp6bgvW7ZM1157rSpVqqTw8HBdffXVysjI8EHFFUNJxv3gwYO65557FBcXp0qVKumKK67QlClTfFRxxfDee++pWbNm1hertWvXTt9//701n3XqhVHcuLNOvXDO9HovwDr1wis34WPx4sUaOHCgli9frnnz5iknJ0c33HCD0tLSrDZDhgzRzJkzNXnyZC1evFj79+/Xrbfe6sOqy78zjfv+/fu1f/9+jR07Vhs2bNCECRM0Z84c3X///T6uvHwryeu9wBtvvCE/Pz8fVFnxlGTcly1bpm7duumGG27QypUr9fPPP2vQoEHy9y83b6dlTknG/d5779XmzZs1Y8YMrV+/Xrfeeqtuv/12rV692oeVl281a9bU6NGjtWrVKv3yyy+69tpr1atXL/3222+SWKdeKMWNO+vUC+dMr/cCrFNtYMqpw4cPG0lm8eLFxhhjkpOTjcPhMJMnT7babNq0yUgyy5Yt81WZFc7p416Ur7/+2jidTpOTk2NjZRWbt3FfvXq1qVGjhjlw4ICRZKZNm+abAiuoosa9bdu25tlnn/VhVRVfUeNeqVIlM3HiRI92UVFR5qOPPrK7vAotMjLS/Oc//2GdarOCcS8K69QL5/RxZ51qj3L7UV3BLsioqChJ0qpVq5STk6Prr7/eatO4cWPFx8dr2bJlPqmxIjp93L21CQ8PV2BgoF1lVXhFjXt6err69Omjd955R3Fxcb4qrUI7fdwPHz6sFStWqEqVKmrfvr2qVq2qTp066ccff/RlmRVOUa/39u3b66uvvlJSUpLy8/M1adIkZWZmqnPnzj6qsmLJy8vTpEmTlJaWpnbt2rFOtcnp414U1qnnX1HjzjrVPuXylZyfn6/HHntMV111lS677DJJp44Hdjqdqly5skfbqlWr6uDBgz6osuIpatxPd/ToUb344ovq37+/zdVVXN7GfciQIWrfvr169erlw+oqrqLGfceOHZKkESNGaOzYsWrevLkmTpyo6667Ths2bFCDBg18WXKF4O31/vXXX+uOO+5QdHS0AgMDFRISomnTpql+/fo+rLb8W79+vdq1a6fMzEyFhoZq2rRpuuSSS7RmzRrWqReQt3E/HevU86u4cWedap9yGT4GDhyoDRs28Gmjzc407qmpqerZs6cuueQSjRgxwt7iKrCixn3GjBlasGABx7tfQEWNe35+viTpwQcf1H333SdJatGihebPn69PPvlEo0aN8kmtFYm395nnnntOycnJ+uGHHxQTE6Nvv/1Wt99+uxITE9W0aVMfVVv+NWrUSGvWrFFKSoq++eYb9e3bV4sXL/Z1WRWet3F3DyCsU88/b+O+bds21ql28vVxX2dr4MCBpmbNmmbHjh0e0+fPn28kmePHj3tMj4+PN6+99pqNFVZM3sa9QGpqqmnXrp257rrrTEZGhs3VVVzexv3RRx81fn5+JiAgwLpJMv7+/qZTp06+KbYC8TbuO3bsMJLMZ5995jH99ttvN3369LGzxArJ27hv27bNSDIbNmzwmH7dddeZBx980M4SK7zrrrvO9O/fn3WqzQrGvQDrVHsUjDvrVHuVm3M+jDEaNGiQpk2bpgULFqhOnToe81u2bCmHw6H58+db0zZv3qw9e/Z4PY4SZ3amcZdOfTpzww03yOl0asaMGQoKCvJBpRXLmcb96aef1rp167RmzRrrJkmvv/66xo8f74OKK4YzjXvt2rVVvXr1QpeB3bJlixISEuwstUI507inp6dLUqErigUEBFh7o3B+5OfnKysri3WqzQrGXWKdaqeCcWedajOfRp+z8PDDD5uIiAizaNEic+DAAeuWnp5utXnooYdMfHy8WbBggfnll19Mu3btTLt27XxYdfl3pnFPSUkxbdu2NU2bNjXbtm3zaJObm+vj6suvkrzeTyeuzHHOSjLur7/+ugkPDzeTJ082W7duNc8++6wJCgoy27Zt82Hl5duZxj07O9vUr1/fdOzY0axYscJs27bNjB071vj5+ZlZs2b5uPry6+mnnzaLFy82O3fuNOvWrTNPP/208fPzM3PnzjXGsE69UIobd9apF86ZXu+nY5164ZSb8CGpyNv48eOtNhkZGWbAgAEmMjLShISEmFtuucUcOHDAd0VXAGca94ULF3pts3PnTp/WXp6V5PVe1H14ozw3JR33UaNGmZo1a5qQkBDTrl07k5iY6JuCK4iSjPuWLVvMrbfeaqpUqWJCQkJMs2bNCl16F2fnb3/7m0lISDBOp9PExsaa6667zmNDjHXqhVHcuLNOvXDO9Ho/HevUC8fPGGMuxB4VAAAAAHBXbs75AAAAAFC+ET4AAAAA2ILwAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAJRSv3795OfnJz8/Py1atMjX5VwUdu3aZY15586dfV1OiZXXugHgfCN8ACizRowYYW2wFXWrXLmyr0ssMfeNT/dbUFCQGjRooMGDB+vw4cO+LrNMmDBhgkaMGKERI0YoOTnZ1+UUqXPnzsW+Nt1vEyZM8HW5AFBmBPq6AAC4mGVlZWnbtm0aN26cpk2bphUrVqh69eq+LsunJkyYoMWLF0s6tXfJPWRWq1ZNiYmJkqSIiAhflFcq5bVuADjfCB8AyoXu3btr6NChHtMCA8vvW1hiYqKMMfrtt9/09NNPKyUlRfv27dO//vUvvf3228XeNy0tTZUqVbKp0tLLz89Xdna2goKCzlufLpdLHTp0OG/9lda4ceOUkpJi/f3II49ozZo1kqShQ4eqe/fu1ryGDRuWmboBwNc47ApAuVClShV16NDB43bllVd6tPn111/117/+VXFxcXI6nYqLi1Pv3r21atWqQv1lZ2drzJgxat68uSpVqqSQkBBdfvnlGj16tLKzswu1f/vtt1WvXj0FBwerTZs2WrBgwTktT4cOHdSxY0c99NBDGjJkiDW94NPx088RWLJkidq1a6fg4GANHDjQar9u3TrdddddqlatmpxOp2rUqKG///3v2rdvn8fjuR/CNn78eL3++uuqV6+egoKC1LJlS82bN8+j/ZIlS/TXv/5VDRo0UOXKleV0OlW9enXdfvvtWrdunde+P/nkE7300ktKSEiQw+HQ8uXLlZaWpocfflitWrVS1apV5XQ6FRERoXbt2unjjz+2+lm0aJH8/PysvR6SVKdOHavvXbt2FXvuRGpqqoYNG6YmTZooODhYYWFhatu2rT744AMZYzzaFvRRu3Ztbd26VTfddJNCQ0MVFRWlhx56SJmZmcU+f02bNvV4LbrvzWjQoIHHvCpVqnit233sPv74Y40cOVLVqlVTeHi47rrrLiUnJyspKUn33HOPIiIiiq1v+vTpuv766xUZGSmXy6VGjRpp5MiRysjIKHZZAMBWBgDKqOHDhxtJRpLp27dvsW2nT59uHA6H1d795nA4zPTp0622mZmZ5uqrry6yrSRz9dVXm6ysLKv9q6++WmSfTZo0sf5euHBhsfXt3LnT4/7uXn/9dWt648aNC7WvXr26CQoKKjQWs2fPNi6Xq8hliIuLMzt27ChyLBs1alTk8ixZssRqP2rUKK/jExISYjZu3Fhk33Xr1vVou3DhQnPgwAGvfUkyI0eONMYYs3DhwmLb7dy502NcOnXqZNWQlJRkGjdu7PW+d955p8eYF0wPDw830dHRhdoPGzas2OfzdJ06dbLuO378+GKff/e63ceuXr16hero1q2badOmzRnre+6557wue8eOHT1ezwDgS+z5AFAufPrpp4VO5O3Xr5+kU4ch3X///crJyZEkPfzww5o9e7YGDBggScrJydH999+vtLQ0SdIbb7yhJUuWSJJq1aqlL774Ql9++aXi4+MlnfrU//XXX5ckHT9+XM8//7xVxyOPPKJZs2bpjjvu0KZNm85pmYwx2rhxo9577z1rWtOmTQu1279/v2rWrKnPP/9cs2fP1s0336z09HT17dtXWVlZCgwM1Msvv6y5c+fqySeflCQdPHjQWv7Tbdu2TS+88IK+++47de3a1Rqjxx57zGrTpk0bjRs3TjNmzNDChQs1b948jRkzRpKUnp5ujc/pduzYobvvvluzZs3SxIkTVaNGDYWEhOiFF17Q119/rblz52rhwoWaNGmSGjRoIEl69dVXlZ2drRYtWigxMVHNmze3+ps8ebISExOVmJioatWqeR3LoUOH6vfff7fGcOrUqfrPf/6jyMhISdKkSZP01VdfFbpfamqqYmNjNWXKFL344ovW9A8++MDrY10ou3bt0iuvvKKvvvpKYWFhkqQ5c+Zo48aN+s9//uPxOnGv7+eff7Zqr1atmj7++GPNmTNHPXv2lHRqb5q35wsAbOfr9AMA3rh/KlzUrWAPwNSpU61pLVu29OijZcuW1rxp06YZY4xp1qyZNW3mzJlW25kzZ1rTL7/8cmOMMV999ZU1rXXr1lbb3NxcEx8fX+o9H0XdnE6nWblyZaH2/v7+5vfff/fob9q0adb87t27m8TEROtWu3ZtI8n4+fmZI0eOFBrLu+++2+onOTnZhISEWPP27NljjDEmLS3NjBgxwjRt2tRjfsGtRYsWRT5PV111VZHLP3PmTNOlSxcTExNjAgICCvW3du1aq637XoSdO3d6HceCPQh5eXkmMjLSmr5+/Xqr/bhx46zpvXr1sqa7P/bq1aut6e57T5KTk4t9Tt2djz0fffr0sab37NnTmv7cc89Z0y+99NJC9T366KPWtKFDh1qvA/fX82WXXVbiZQGAC6n8nq0J4KJS1AnnVatWlSRt2bLFmta2bVuPNm3atLHO+Sho5619mzZtrN8L2uzYscOa1rp1a+v3gIAAtWzZUnv27CndAp2mefPmeu211zweo0CDBg3UqFEjj2nuy/D999/r+++/L3Q/Y4x+//33Qic6uy9zRESEGjVqpNWrV0s6tby1atXSXXfdpRkzZnit19slcP/yl78UmjZ16lTddtttXvsqrr+SOHLkiI4fPy5JCgkJ0WWXXWbNK+o5dRceHu6xpyU6OtqjJjuvTOVea1RUlPV7q1atrN9jYmKs3wvqc1+uf/3rX/rXv/5VqO+CvUIA4GuEDwDlQsEJ52fLz8/vgrQtTXt3BSeWu1wu1apVS3FxcV7bFoSs0ig41Kw4py/Hnj17rOARGhqqV155RZdccokkWSdL5+fnl7hW96t39evXT3369FFwcLBeeOEF60R3b/2drdOX5UzPUcFhWQXcr6BmTjtJ/UJzDzr+/n8eFR0eHl5k+7OpLzc3V1lZWXK5XKUvEADOA875AFDuNWzY0Pp95cqVHvPc/y5o5639ihUrCrWtW7euNe2XX36xfs/Ly/P4+2wVXAmpdevWxQYPqegNaPdl6Nu3r4wxhW5paWnWOR3u3Jc5JSVFmzdvtv6uW7eu/vjjD+vvrl276uGHH1anTp1KtOFaVK3u/Y0bN05dunRR+/btPaa7c9/wLkkoiY2Ntb4LJC0tTb/99ps1r6jntKJxX67x48d7fS0QPACUBez5AFDu3XDDDYqOjtaxY8f0yy+/aNCgQerZs6dmz55tBYSYmBh16dJFktSnTx/rcrEDBw7UiRMn5Ofnp6efftrq86677pIkdenSRUFBQcrMzNTKlSv12GOPqWvXrpo0adJ5O+SqNLp06aLY2FgdOXJEEydOVFRUlLp06aK8vDzt2rVLS5cu1dq1a7Vx48ZC9/3yyy/VuHFjtWjRQm+//ba1d6RFixaqVauWAgICrLYLFizQl19+qYCAgEKHvZVUQkKCdWjQ888/r65du+qzzz4rsjbJc2/ERx99pB49eig4ONjj8CN3/v7+uvPOO/X+++9Lku6++24NHz5cx48f1/Dhw612Bc9pRdOnTx+9+eabkqQhQ4YoKSlJzZo1U3JysrZv3665c+cqISFBn3zyiY8rBQBxwjmAsutsLrX77bffntWldjt27Oj1xO/TL7U7evToQm38/f09Lit7LpfaPVN79xOU3c2aNcvrpXYlmYSEBKut+1i6n3BfcAsMDPRYBvcTngtuV1111Rn7Lupk68mTJxfqKygoyONiAO6P7X6S+OmP521cjh07dsZL7ebn51vti1oOY4o/2b045+OEc/f79e3bt8ix8VZfcZfaLcn/DwDYhcOuAFQIvXr10rJly9S7d29VqVJFgYGBio2N1a233qqffvpJN910k9XW5XJp3rx5Gj16tJo1a6bg4GAFBQWpadOmGjVqlObOnSun02m1f+qpp/Tmm2+qdu3acrlcat68uaZPn66OHTv6YlEtPXr00C+//KJ77rlHNWvWlMPhUExMjJo3b67HH39ckydPLvJ+Q4YMsb400el0qkWLFvruu+88vvzus88+U9++fRUTE6PKlSvrnnvu0cyZM0tVZ+/evfXBBx+oQYMGCgoKUuvWrTVnzhyPE8PdPfjgg3rqqacUHx/vcQhWcaKiorR8+XI988wzatSokVwulypVqqTWrVvrvffe0xdffHFO5+iUdQWXTu7WrZuio6PlcDhUo0YNdejQQaNHj9bIkSN9XSIASJL8jLH5jDoAgO1GjBhhbYCOHz/e+o4UAADsxJ4PAAAAALYgfAAAAACwBeEDAAAAgC045wMAAACALdjzAQAAAMAWhA8AAAAAtiB8AAAAALAF4QMAAACALQgfAAAAAGxB+AAAAABgC8IHAAAAAFsQPgAAAADY4v8D1O7LUIZYAf4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create boxplot for food preprations times.\n",
    "plt.figure(figsize=(10, 4))\n",
    "box_plot = sns.boxplot(x=foodhub_df['food_preparation_time'])\n",
    "\n",
    "# Calculate quartiles and median\n",
    "Q1 = foodhub_df['food_preparation_time'].quantile(0.25)\n",
    "Q3 = foodhub_df['food_preparation_time'].quantile(0.75)\n",
    "median = foodhub_df['food_preparation_time'].median()\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the output.\n",
    "plt.text(x=median, y=-0.3, s=f'Median: {median}', color='red', ha='center', bbox=dict(facecolor='white', alpha=0.5))\n",
    "plt.text(x=Q1, y=-0.2, s=f'Q1: {Q1}', color='green', ha='center', bbox=dict(facecolor='white', alpha=0.5))\n",
    "plt.text(x=Q3, y=-0.2, s=f'Q3: {Q3}', color='blue', ha='center', bbox=dict(facecolor='white', alpha=0.5)) \n",
    "plt.xlabel('Food Preparation Time', fontsize=12, fontweight='bold')\n",
    "plt.title('Boxplot for the Food Preparation Time', fontsize=12, fontweight='bold')\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Delivery Time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Histogram of Delivery Times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot a histogram for the delivery time.\n",
    "plt.figure(figsize=(10, 4))\n",
    "sns.histplot(foodhub_df['delivery_time'], edgecolor='black', kde=True)\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the barchart output.\n",
    "plt.title('Histogram for Delivery Times', fontsize=12, fontweight='bold')\n",
    "plt.xlabel('Delivery Times', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Number', fontsize=10, fontweight='bold')\n",
    "\n",
    "# Show to plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Boxplot of Delivery Times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create boxplot for delivery times.\n",
    "plt.figure(figsize=(10, 4))\n",
    "box_plot = sns.boxplot(x=foodhub_df['delivery_time'])\n",
    "\n",
    "# Calculate quartiles and median\n",
    "Q1 = foodhub_df['delivery_time'].quantile(0.25)\n",
    "Q3 = foodhub_df['delivery_time'].quantile(0.75)\n",
    "median = foodhub_df['delivery_time'].median()\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the output.\n",
    "plt.text(x=median, y=-0.3, s=f'Median: {median}', color='red', ha='center', bbox=dict(facecolor='white', alpha=0.5))\n",
    "plt.text(x=Q1, y=-0.2, s=f'Q1: {Q1}', color='green', ha='center', bbox=dict(facecolor='white', alpha=0.5))\n",
    "plt.text(x=Q3, y=-0.2, s=f'Q3: {Q3}', color='blue', ha='center', bbox=dict(facecolor='white', alpha=0.5)) \n",
    "plt.xlabel('Delivery Time', fontsize=12, fontweight='bold')\n",
    "plt.title('Boxplot for the Delivery Time', fontsize=12, fontweight='bold')\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K9CqA5H_JY_U"
   },
   "source": [
    "---\n",
    "### **Question 7**: Which are the top 5 restaurants in terms of the number of orders received? [1 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "PaJZ3wlOJY_U"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Restaurant Name  Number of Orders\n",
      "              Shake Shack               219\n",
      "        The Meatball Shop               132\n",
      "        Blue Ribbon Sushi               119\n",
      "Blue Ribbon Fried Chicken                96\n",
      "                     Parm                68\n"
     ]
    }
   ],
   "source": [
    "# Create a new DataFrame, top_restaurants_df and retrieve the top 5 restaurants with the highest number of orders.\n",
    "top_restaurants_df = foodhub_df['restaurant_name'].value_counts().reset_index()\n",
    "\n",
    "# Print readable column names for the DataFrame foodhub_df.\n",
    "top_restaurants_df.columns = ['Restaurant Name', 'Number of Orders']\n",
    "\n",
    "# Print only the top 5 restaurants without the index column.\n",
    "print(top_restaurants_df.head(5).to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t2mHHjh0ZWs/PiGGHJK1atYKrqyuEEAgICEh3mdShJTQ0FMDHXzoXLlz4ITX6+/tLfz98+FD6lWlgYAAHB4c0p/vWrFkT4mMn/DS3mJgY9O/f/7N1q/LFkvpxUtd05cqVdJdRVaFChVCiRAnp/0WLFqWZn5CQgOXLl0v/p/Ql+FR6z6lgwYLS3//884/0d+raU1hbW0sf0MbGxnj//v1n2zM5ORne3t4ZemwhBGxsbDBlyhT4+voiJCQEjx8/loLal/o6ZMSXXpPExET8999/6S73tVozIzP7x5eWT/26pefhw4fpthCkXsf3hDpTU1MsWrRIapl98eIFVq9eLc1P/Tz8/PzSfc89evQIgGqvf+ofRZ+Gh9QDG06cOBHNmzdHtWrV0gwLkFrK59bbt2+RmJgI4OOI4ffu3fvqc09vv0j92IMHD0a7du1QrVo1xMXFfXVdGfW15y2EgKOjI+bMmYNz587hzZs3uHr1qjQ/M+8fueNhLJIoFApMnDjxq83czs7OUotCt27d0Lp1a2zZskUtTcYZ8ccff8DOzg758+fHzJkzpekeHh7Q1dVF6dKlUaJECdy6dQtnzpxBt27d0LZtW+jq6uLJkyfw9/fH3r17VR7F+FOdOnXCjRs3AHz8wHv//j0UCgXGjx8vLdOxY8dMPcasWbPQrFkzAMCGDRugUCjQtm1bxMTEYOnSpdIHtYWFRZpftN/SrFkz6bDCkCFDMGfOHMTFxUkte6lpaWmhY8eOWLlyJaKjo9GgQQMMGzYM1tbWePHiBW7duoU9e/Zgw4YNGbrExMWLFzFs2DC0bt0aLi4usLa2xo0bN/DhwwcAHwev+15t2rTBuHHjkJiYiD179sDT0xOVKlXCpk2bEBISAuBji1np0qW/+zEyKjP7R//+/TF79uzPXpPmzZt/9THPnz+PUaNGoVu3bqhduzb09fVx+PBhbN26NcPr+BIdHR2MHj0aAwYMAPDxfThkyBDo6Oigc+fO2L9/PwCga9eumDRpElxcXBAWFobAwEAcPnwYHh4e8PT0VOn1T90KsnXrVmhra0NbWxvVqlWDo6OjNG/p0qXQ09PDlStXsH79+nTrd3Z2RkBAAGJjY9GpUyfUqFEDK1euVLnDNoA0j71hwwYULFgQDx8+xIwZM1ReV3q+9ry3b98OLy8vtGjRAk5OTjAzM8OpU6ek5TPz/pG9H9pDiLKNTzsop0hKShLOzs5pOtul7qB87Nixzzrj6ejopLnPl8bZSfHpOBYpUp9anHqckNQd9lxcXD57fGNj4zSdJr926nnKLYWqHYdTxMXFierVq39x/Z+eWvy9j7Nw4UKhpaX1xccxNzf/rBPjtzp2vnnzRuTJk+ezdaXetqlfl3fv3n311PPUj/Ol1zbFuXPnvrqe2bNnf3ObfKmDshDff+r5t8ZUykgtn556rsr+kbpTcHrDK5QoUSLN6czpSb2PpXdr27ZthjqDf+nU8w8fPghra2tp3tatW6V5Xzv1PPU+r8rrn7oz96fv3adPn4pcuXJ9Nq9q1arpvs9Wr16d7udG3rx5P9uXvrUPR0VFidy5c3/1sVN/fn1pf/3S/ve1571ly5avbr/t27d/8/X9WfEwFqWhra0tncadngYNGmDx4sXImzcv9PX1UbFiRRw7dgxVq1b9IfUtX74c48aNQ+7cuaGvr49q1arBz88PRYsWlZYpV64crl27hgEDBqBgwYLQ09ODubk5SpQogQEDBsDX1zfTdejr6+PEiROYM2cOSpUqBUNDQxgYGKBkyZKYPXs2jh8/Dj09vUw/zqhRoxAQEIAePXqgQIEC0NfXh5GREUqUKIGxY8fizp07Kl+008rKCmfPnkWTJk2QK1cuWFpaom/fvti9e3e6y5ubm+PSpUuYPn06SpcuDUNDQ+TKlQsuLi5o06YNtm/fLp1m/C2FCxfGuHHjUKlSJdjZ2UFHRwfGxsZwc3PDihUrMG7cOJWey6cGDRqEEydOwMPDA5aWltDR0YGDgwO6deuGgICANB2ys1Jm9g9fX1907doVZmZmMDExQYcOHXDy5MlvjoDcpEkT/PHHH6hTpw7y5csHPT09mJqaomrVqli3bh127tyZqcN1hoaGGDRokPT//Pnzpb83bdqEzZs3o2bNmjAzM4Oenh7y58+PunXrYunSpdL9VHn9mzRpggULFqBQoUKfnTiQP39+HD9+HBUrVoShoSEKFSqElStXok+fPunW3qdPH0yYMAG2trYwNDREnTp1cO7cORQqVEjl7WBiYoITJ06gTp06MDY2Rp48efD777/j999/V3ld6fna865cuTKGDx+OcuXKwdraGtra2jAzM0P16tWxc+dOaVgP+pxCiAz2jCIioixRoEABPH36FAAy3FmViDKOLTtEREQkaww7REREJGsMO0RERCRr7LNDREREssaWHSIiIpI1hh0iIiKSNY6gjI9DcgcHB8PExETtw8YTERFR1hBC4P3793BwcPjqRWQZdgAEBwcjX758mi6DiIiIvsPz58+RN2/eL85n2MHHETGBjxvrS1fhJiIiouwlKioK+fLlk77Hv4RhB/9/ZVtTU1OGHSIiohzmW11Q2EGZiIiIZI1hh4iIiGSNYUcmZs+eDTc3N5iYmMDW1hYtWrTA/fv30yyzZs0a1KpVC6amplAoFIiIiPhsPf/++y/q168Pc3NzWFlZoV+/foiOjv5Bz4KIiEj9GHZk4syZMxg8eDAuX76MEydOIDExEQ0aNEBMTIy0zIcPH9CoUSNMnDgx3XUEBwejXr16cHZ2xpUrV+Dj44Pbt2+jR48eP+hZEBERqR8vF4GPvbnNzMwQGRkpmw7KYWFhsLW1xZkzZ1CjRo00806fPo3atWvj3bt3MDc3l6avWbMGU6ZMQUhIiDRewc2bN1GqVCkEBgbC2dn5Rz4FIiKir8ro9zdbdmQqMjISAGBpaZnh+8THx0NPTy/NwEyGhoYAgPPnz6u3QCIioh+EYUeGlEolRowYgapVq6JEiRIZvl+dOnUQGhqK+fPnIyEhAe/evcP48eMBACEhIVlVLhERUZZi2JGhwYMH49atW9ixY4dK9ytevDg2bdqEhQsXIleuXLC3t4eTkxPs7Oy+Ogw3ERFRdsZvMJkZMmQIDh06BD8/v68Onf0lnTp1QmhoKF6+fIm3b99i6tSpCAsLQ8GCBbOgWiIioqzHEZRlQgiBoUOHYu/evTh9+jScnJwytT47OzsAwIYNG2BgYID69euro0wiIqIfjmFHJgYPHoxt27Zh//79MDExQWhoKADAzMxM6mQcGhqK0NBQPHz4EMDHM61MTEyQP39+qSPz8uXLUaVKFRgbG+PEiRMYM2YM5syZk+asLSIiopyEp55DHqeef+m6IN7e3tI4OVOnTsW0adO+uky3bt1w+PBhREdHo2jRovj111/RtWvXrCqbiIjou2X0+5thB/IIO0RERD8bjrNDREREBIYdIiIikjl2UM5qX+hLQ+ngEVUiIsoCbNkhIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWdNo2Jk9ezbc3NxgYmICW1tbtGjRAvfv30+zTFxcHAYPHgwrKysYGxujdevWePXqVZplnj17hsaNGyNXrlywtbXFmDFjkJSU9COfChEREWVTGg07Z86cweDBg3H58mWcOHECiYmJaNCgAWJiYqRlRo4ciYMHD2L37t04c+YMgoOD0apVK2l+cnIyGjdujISEBFy8eBGbNm3Cxo0b8dtvv2niKREREVE2oxBCCE0XkSIsLAy2trY4c+YMatSogcjISNjY2GDbtm1o06YNAODevXsoVqwYLl26hEqVKuHo0aNo0qQJgoODYWdnBwDw8vLCuHHjEBYWBj09vW8+blRUFMzMzBAZGQlTU1P1PimFQr3rk7PssysSEVEOkNHv72zVZycyMhIAYGlpCQAICAhAYmIi6tWrJy1TtGhR5M+fH5cuXQIAXLp0CSVLlpSCDgA0bNgQUVFRuH37drqPEx8fj6ioqDQ3IiIikqdsE3aUSiVGjBiBqlWrokSJEgCA0NBQ6OnpwdzcPM2ydnZ2CA0NlZZJHXRS5qfMS8/s2bNhZmYm3fLly6fmZ0NERETZRbYJO4MHD8atW7ewY8eOLH+sCRMmIDIyUro9f/48yx+TiIiINENH0wUAwJAhQ3Do0CGcPXsWefPmlabb29sjISEBERERaVp3Xr16BXt7e2kZf3//NOtLOVsrZZlP6evrQ19fX83PgoiIiLIjjbbsCCEwZMgQ7N27F6dOnYKTk1Oa+eXLl4euri58fX2laffv38ezZ89QuXJlAEDlypVx8+ZNvH79WlrmxIkTMDU1haur6495IkRERJRtabRlZ/Dgwdi2bRv2798PExMTqY+NmZkZDA0NYWZmht69e2PUqFGwtLSEqakphg4disqVK6NSpUoAgAYNGsDV1RVdu3bFvHnzEBoaismTJ2Pw4MFsvSEiIiLNnnqu+MJp2d7e3ujRoweAj4MKjh49Gtu3b0d8fDwaNmyIlStXpjlE9fTpUwwcOBCnT5+GkZERunfvjjlz5kBHJ2NZjqeeZxM89ZyIiFSQ0e/vbDXOjqYw7GQT3BWJiEgFOXKcHSIiIiJ1Y9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZU0vYeffunTpWQ0RERKR2KoedLVu2oFevXrh58yZevXqFUqVKwdraGo6Ojrh161ZW1EhERET03VQOO6tXr8bWrVuRL18+rF69Grdu3YIQAs+fP8eUKVOyokYiIiKi76Zy2Hnw4AHy588Pc3NzXLx4EdbW1rh06RJMTU1x+fLlrKiRiIiI6LupHHaioqJgbm4OALh37x7Kly8Pd3d3ODs7s+8OERERZTsqhx1bW1vcuXMHs2fPxvPnz1GyZEkAQHh4OCwtLdVeIBEREVFmqBx2GjdujLi4OEyePBkA0KxZM4SHh+PFixdwdXVVe4FEREREmaGj6h0WLFiAXLlyITAwEE2bNkW1atVw9epVtG/fHk2aNMmKGomIiIi+m0otO4mJifj7779RpkwZ7Nu3D3379gUAuLm5YcuWLWjfvr1KD3727Fk0bdoUDg4OUCgU2LdvX5r5PXr0gEKhSHNr1KhRmmXCw8PRuXNnmJqawtzcHL1790Z0dLRKdRAREZF8qRR2dHV10bdvX8yePRtaWpkfjzAmJgalS5fGihUrvrhMo0aNEBISIt22b9+eZn7nzp1x+/ZtnDhxAocOHcLZs2fRr1+/TNdGRERE8qDyYazSpUsjODhYLQ/u4eEBDw+Pry6jr68Pe3v7dOfdvXsXPj4+uHr1KipUqAAAWLZsGX755RcsWLAADg4OaqmTiIiIci6Vm2fGjh2LN2/eoFu3bvD398fTp0/x7Nkz6aZup0+fhq2tLYoUKYKBAwfi7du30rxLly7B3NxcCjoAUK9ePWhpaeHKlStqr4WIiIhyHpVbdtq1aweFQoE///wTf/75Z5p5CoUCSUlJaiuuUaNGaNWqFZycnPDo0SNMnDgRHh4euHTpErS1tREaGgpbW9s099HR0YGlpSVCQ0O/uN74+HjEx8dL/0dFRamtZiIiIspeVA47ACCEUHcd6erQoYP0d8mSJVGqVCkUKlQIp0+fRt26db97vbNnz8a0adPUUSIRERFlcyqHHT8/v6yoI0MKFiwIa2trPHz4EHXr1oW9vT1ev36dZpmkpCSEh4d/sZ8PAEyYMAGjRo2S/o+KikK+fPmyrG4iIiLSHJXDTs2aNbOijgx58eIF3r59i9y5cwMAKleujIiICAQEBKB8+fIAgFOnTkGpVMLd3f2L69HX14e+vv4PqZmIiIg067vOH79//z66d++OIkWKoFmzZrh8+TJ+//133Lp1S6X1REdH49q1a7h27RoAICgoCNeuXcOzZ88QHR2NMWPG4PLly3jy5Al8fX3RvHlzODs7o2HDhgCAYsWKoVGjRujbty/8/f1x4cIFDBkyBB06dOCZWERERPSRUNG1a9eEiYmJ0NLSEgqFQlSuXFn8999/QqFQiMGDB6u0Lj8/PwHgs1v37t3Fhw8fRIMGDYSNjY3Q1dUVjo6Oom/fviI0NDTNOt6+fSs6duwojI2NhampqejZs6d4//69SnVERkYKACIyMlKl+2UIwFtGb0RERCrI6Pe3QgjVeht7eHjg2LFjqFChAv755x9UqlQJFy9ehLW1Nezs7HD79m21B7KsFhUVBTMzM0RGRsLU1FS9K1co1Ls+OftBHd+JiEgeMvr9rfJhrAsXLiBPnjy4dOlSmun58uXD8+fPVa+UiIiIKAupHHaSk5NhbGwMbW3tNNPDwsKgVCrVVhgRERGROqgcdlxdXfHgwQPMmDEDwMcmpF9//RXBwcEoUaKE2gskIiIiygyV++xs3boV3bp1gyKdviibNm1Cly5d1Fbcj8I+O9kE++wQEZEKsqzPTpcuXTBnzhwYGhpCCAEhBAwMDDBz5swcGXSIiIhI3lRu2UkRGxsrnXlVvHhxGBoaqrWwH4ktO9kEW3aIiEgFGf3+/q5rYwGAoaFhmquNExEREWVHGQo7n5559SXqvuo5ERERUWZlKOx855EuIiIiIo3LUNjx9PSU/k5ISMDSpUuRN29eNG7cGABw5MgRPHnyBMOHD8+aKomIiIi+U4bOxvL09JRu79+/h7GxMf79918sWLAACxYswD///AMzMzOEh4dndb1E2crZs2fRtGlTODg4QKFQYN++fWnmT506FUWLFoWRkREsLCxQr149XLlyRZr/5MkT9O7dG05OTjA0NEShQoXg6emJhISEH/xMiIjkS+VTz7ds2QI9PT0YGBhI0wwMDKCrq4udO3eqtTii7C4mJgalS5fGihUr0p1fuHBhLF++HDdv3sT58+dRoEABNGjQAGFhYQCAe/fuQalUYvXq1bh9+zb++OMPeHl5YeLEiT/yaRARyZrKp55bW1vj3bt3qFatGpo1awYAOHjwIM6ePQtLS0u8efMmSwrNSjz1PJvI4X3DFAoF9u7dixYtWnxxmZR97eTJk6hbt266y8yfPx+rVq3C48ePs6hSIiJ5yLJTzwcMGIBZs2bh/PnzOH/+fJp5/fv3V71Sop9EQkIC1qxZAzMzM5QuXfqLy0VGRsLS0vIHVkZEJG8qH8aaMWMGlixZgjx58kgjKOfJkweLFy+WrpdFRP/v0KFDMDY2hoGBAf744w+cOHEC1tbW6S778OFDLFu2jD8ciIjUSKXDWEqlEi9evAAA5MuXD9HR0QAAExOTrKnuB+FhrGxCpoexYmJiEBISgjdv3mDt2rU4deoUrly5Altb2zTLvXz5EjVr1kStWrWwbt26H1g5EVHOlGXXxipYsCCqVq0KhUIBExOTHB90iLKakZERnJ2dUalSJaxfvx46OjpYv359mmWCg4NRu3ZtVKlSBWvWrNFQpURE8qRSnx0tLS04OjpCT08vq+ohkj2lUon4+Hjp/5cvX6J27dooX748vL29oaWl8m8QIiL6CpU/VadOnYrAwECsXbs2K+ohylGio6Nx7do1XLt2DQAQFBSEa9eu4dmzZ4iJicHEiRNx+fJlPH36FAEBAejVqxdevnyJtm3bAvgYdGrVqoX8+fNjwYIFCAsLQ2hoKEJDQzX4rIiI5EXlU8+dnJwQEhKCxMREGBoawsbGBor/9UtRKBR49OhRlhSaldhnJ5vIgX12Tp8+jdq1a382vXv37vDy8kKnTp1w5coVvHnzBlZWVnBzc8PkyZPh5uYGANi4cSN69uyZ7rp5mRYioq/L6Pe3ymHna03sCoUCycnJqqwuW2DYySb45U5ERCrIsnF2Ul8ni4iIiCi7U7llR47YspNNcFckIiIVZFnLTmhoKHbv3o379+8DAIoUKYK2bdvC3t7++6slUjPFNIbMjBKeDJlEJG8qhZ3Nmzdj4MCBiIuLSzN93LhxWL16Nbp27arW4oiIiIgyK8Onnl+5cgW9e/dGbGysdJmIlFtcXBx69eqFK1euZGWtRERERCrLcNhZsGABkpOT4ezsDG9vb/z333+4fv06vL294ezsjOTkZCxcuDArayUiIiJSWYYPY124cAH6+vrw8/NDnjx5pOklS5ZEnTp14OLignPnzmVJkURERETfK8MtO2/fvoWzs3OaoJMiX758cHFxQXh4uFqLIyIiIsqsDIcdU1NTPHnyBO/fv/9sXmRkJIKCgtR/2jYRERFRJmU47FSsWBEfPnyAh4cHzp8/j/fv3yM6Ohrnzp3DL7/8gtjYWFSsWDErayUiIiJSWYbDztChQyGEwKVLl1CzZk2Ym5vDzMwMtWrVwuXLlwEAw4YNy7JCiYhSnD17Fk2bNoWDgwMUCgX27duXZv6ePXvQoEEDWFlZQaFQSBdqTe3Ro0do2bIlbGxsYGpqinbt2uHVq1c/5gkQ0Q+V4bDTqFEjTJs27bPTzlNu06ZNQ8OGDbOyViIiAEBMTAxKly6NFStWfHF+tWrVMHfu3C/Ob9CgARQKBU6dOoULFy4gISEBTZs2hVKpzMrSiUgDVBpUcMqUKWjcuDG2bt2aZgTlLl26oFy5cllSIBHRpzw8PODh4fHF+SkDnD558iTd+RcuXMCTJ0/w33//SX0NN23aBAsLC5w6dQr16tVTe81EpDkqXy6iXLlyDDZElKPFx8dDoVBAX19fmmZgYAAtLS2cP3+eYYdIZjJ8GIuISC4qVaoEIyMjjBs3Dh8+fEBMTAx+/fVXJCcnIyQkRNPlEZGaMewQ0U/HxsYGu3fvxsGDB2FsbAwzMzNERESgXLly0NLixyKR3Kh8GIuISA4aNGiAR48e4c2bN9DR0YG5uTns7e1RsGBBTZdGRGrGsENEPzVra2sAwKlTp/D69Ws0a9ZMwxURkbplKOzUqVMHxYsXx7Jly9CrVy8UKlQIkyZNyuraiIjSFR0djYcPH0r/BwUF4dq1a7C0tET+/PkRHh6OZ8+eITg4GACks0ft7e1hb28PAPD29kaxYsVgY2ODS5cuYfjw4Rg5ciSKFCny458QEWUphRBCfGshLS0tuLu749KlS9DS0kKlSpVw8eLFH1HfDxEVFQUzMzNERkaq/5IXCoV61ydn394VM0wxjds9o4Sn+rb7j3L69GnUrl37s+ndu3fHxo0bsXHjRvTs2fOz+Z6enpg6dSoAYPz48di4cSPCw8NRoEABDBgwACNHjoSC71miHCOj398ZCjtmZmZISkrCiBEjMHv2bOTNmxd9+vRJd9nffvvt+6vWEIadbIJhRyNyYtghIgLUHHbq1auHU6dOZegXT3JysmqVZgMMO9kEw45GMOwQUU6V0e/vDPXZWb16NUaNGoU7d+7g8ePH0NPTk457ExGlYLbPODVmeyL6hgyFnUKFCmH//v0APvbfKVu2rKz67BAREZF8qXzqeVBQUJoh1omIiIiyM5WHCnV0dERgYCBq164NExMTmJiYoE6dOjh37lxW1EdERESUKSq37KRcJC8pKQkpfZtPnz6NevXqwc/PD1WqVFF7kURERETfS+WWnd9//x2JiYnInz8/Bg4ciIEDB8LR0RGJiYn4/fffs6JGIiIiou+mcsuOv78/rKyscP36dek0r8jISBQqVAiXL19We4FEREREmaFyy05cXBwsLS3TnM9uZmYGS0tLxMfHq7U4IiIiosxSuWWnUKFCuHfvHkaPHo2OHTsCALZt24aHDx/C1dVV7QUSERERZYbKLTu9evWCEAKLFy+Gu7s73N3dsWTJEigUCvTq1SsraiQiIiL6biqHnZEjR0qhRgghnZHVq1cvjBw5Ur3VEREREWWSyoextLS0sG7dOkycOBEBAQEAgPLly6NgwYJqL46IiIgos1QOOykKFizIgENERETZnsqHsYiIiIhyEoYdIiIikjWGHSIiIpI1hh0iIiKSNZXCTmJiIrS1tWFnZyedck5ERESUnal0Npauri5y584Nc3NzKBSKrKqJiIiISG1UPow1fPhw3L9/H8ePH8+KeoiIiIjUSuVxdo4cOQJtbW14eHigSJEisLOzk1p5FAoFfH191V4kERER0fdSOeycOXNG+vvevXu4d++e9D8PbREREVF2o3LY6datG0MNERER5Rgqh52NGzdmQRlEREREWeO7r43l5+eHy5cvw8LCAp06dUJERATs7Oygr6+vzvqIiIiIMkXls7FiY2NRv3591KtXD5MnT8bmzZtx8uRJODk5YfHixVlQIhERZVfv37/HiBEj4OjoCENDQ1SpUgVXr15Ns8zdu3fRrFkzmJmZwcjICG5ubnj27JmGKqafkcphZ/LkyfD19YUQQhpYsHHjxtDT08Phw4fVXiAREWVfffr0wYkTJ7BlyxbcvHkTDRo0QL169fDy5UsAwKNHj1CtWjUULVoUp0+fxo0bNzBlyhQYGBhouHL6magcdnbt2gVDQ0Ncu3ZNmqavrw9HR0c8ePBApXWdPXsWTZs2hYODAxQKBfbt25dmvhACv/32G3Lnzg1DQ0PUq1cPgYGBaZYJDw9H586dYWpqCnNzc/Tu3RvR0dGqPi0iIlJRbGws/v77b8ybNw81atSAs7Mzpk6dCmdnZ6xatQoAMGnSJPzyyy+YN28eypYti0KFCqFZs2awtbXVcPX0M1E57Lx+/RqFCxdGqVKl0kzX1dVFRESESuuKiYlB6dKlsWLFinTnz5s3D0uXLoWXlxeuXLkCIyMjNGzYEHFxcdIynTt3xu3bt3HixAkcOnQIZ8+eRb9+/VR9WkREpKKkpCQkJyd/1kpjaGiI8+fPQ6lU4vDhwyhcuDAaNmwIW1tbuLu7f/bDliirqRx2cufOjQcPHuDRo0fStGvXruHu3btwcHBQaV0eHh6YMWMGWrZs+dk8IQQWL16MyZMno3nz5ihVqhQ2b96M4OBg6Y1y9+5d+Pj4YN26dXB3d0e1atWwbNky7NixA8HBwao+NSIiUoGJiQkqV66M6dOnIzg4GMnJydi6dSsuXbqEkJAQvH79GtHR0ZgzZw4aNWqE48ePo2XLlmjVqlWaMduIsprKYad58+aIjY1FiRIloFAo8N9//6FixYoQQqB58+ZqKywoKAihoaGoV6+eNM3MzAzu7u64dOkSAODSpUswNzdHhQoVpGXq1asHLS0tXLly5Yvrjo+PR1RUVJobERGpbsuWLRBCIE+ePNDX18fSpUvRsWNHaGlpQalUAvj4vTFy5EiUKVMG48ePR5MmTeDl5aXhyulnonLYmT59OkqXLo34+HgIIRAfH4+kpCSULFkS06ZNU1thoaGhAAA7O7s00+3s7KR5oaGhnx331dHRgaWlpbRMembPng0zMzPpli9fPrXVTUT0MylUqBDOnDmD6OhoPH/+HP7+/khMTETBggVhbW0NHR0duLq6prlPsWLFeDYW/VAqj7NjamoKf39/bNu2TTq90M3NDR07doSenp7aC8wKEyZMwKhRo6T/o6KiGHiIiDLByMgIRkZGePfuHY4dO4Z58+ZBT08Pbm5uuH//fpplHzx4AEdHRw1VSj+j7xpUUFdXF927d0eTJk0AAFZWVmotCgDs7e0BAK9evULu3Lml6a9evUKZMmWkZV6/fp3mfklJSQgPD5funx59fX0OfkhEpAbHjh2DEAJFihTBw4cPMWbMGBQtWhQ9e/YEAIwZMwbt27dHjRo1ULt2bfj4+ODgwYM4ffq0Zgunn4rKh7EAYPny5XBwcICtrS1sbW3h4OCAZcuWqbUwJycn2Nvbp7mKelRUFK5cuYLKlSsDACpXroyIiAgEBARIy5w6dQpKpRLu7u5qrYeIiD4XGRmJwYMHo2jRoujWrRuqVauGY8eOQVdXFwDQsmVLeHl5Yd68eShZsiTWrVuHv//+G9WqVdNw5fQzUYiUkQEzyNPTEzNmzMCnd1MoFJg8ebJK/Xaio6Px8OFDAEDZsmWxaNEi1K5dG5aWlsifPz/mzp2LOXPmYNOmTXBycsKUKVNw48YN3LlzRzrV0cPDA69evYKXlxcSExPRs2dPVKhQAdu2bctwHVFRUTAzM0NkZCRMTU0zfL8M4UVTM061XfGrFNO43TNKeKpxu3OzZ5gad3ein1ZGv79VDju2trZ4+/YtqlWrhjZt2gAA9uzZgzNnzsDa2vqzw0pfc/r0adSuXfuz6d27d8fGjRshhICnpyfWrFmDiIgIVKtWDStXrkThwoWlZcPDwzFkyBAcPHgQWlpaaN26NZYuXQpjY+MM18Gwk00w7GgEw45mMOwQZV6WhZ2UkYqDgoKgra0N4GM/GScnJ0RFRSEyMjJzlWsAw042wbCjEQw7mqHOsDNNob4zYeXOU3hqugRSo4x+f6vcZ6dly5YQQkCR6lMt5e9WrVp9R6lEREREWSdDZ2Nt3rxZ+rtChQrYs2cP6tSpk+YwVmRkJMqXL581VRIRERF9pwwdxtLS0krTkpMeIQS0tLSQlJSktuJ+FB7GyiZ4GEsjeBhLM3gYSzN4GEteMvr9neFxdjLStSdlaHAiIiKi7CJDYYchhoiIiHKq7xpUkIiIiCinUPlyEcnJydiwYQP8/Pzw6tWrNIe3FApFmhGPiYiIiDRN5bAzbNgweHl5Afi8H8+3OjETERER/Wgqh52dO3cCAKpWrYqCBQsy4BAREVG2pnLYyZUrF2xsbHD27NmsqIeIiIhIrVTuoDxlyhQEBQVhx44diI6OzoqaiIiIiNTmuy4XUahQIXTu3BlmZmbQ1taWbjo6KjcUEREREWUpldNJt27dcO/evQwNMkhERESkaSqHndOnT0OhUKBTp04oUKAAW3OIiIgoW1M5qRQpUgQJCQnYsmVLVtRDREREpFYq99mZNGkSnj59ijlz5uDWrVt49uxZmhsRERFRdqJyy067du2gUCgwadIkTJo0Kc08hUKRI696TkRERPL1XR1u2DmZiIiIcgqVw463t3dW1EFERESUJVQOO927d8+KOoiIiIiyhMphZ/PmzV+d361bt+8uhoiIiEjdVA47PXr0+OLFPxUKBcMOERERZSvsoExERESypvI4O0qlMs0tIiICa9asgZ6eHg4fPpwVNRIRERF9N5XDzqdMTU3Rp08fVKlSBRMnTlRHTURERERqo/JhrE9HSU5OTsaDBw9w7do1xMfHq60wIiIiInVQOew4OTl9cV7ZsmUzVQwRERGRuqkcdr7UOTl//vxYuXJlpgsiIiIiUieVw46fn1+a/xUKBWxtbeHi4gJtbW21FUZERESkDiqHnZo1a2ZFHURERERZIsNh51sjJ6fgoIJERESUnWQ47Hxt5OTUGHaIiIgoO1FpnB0hxFdvRERElPVevnyJLl26wMrKCoaGhihZsiT++ecfaX50dDSGDBmCvHnzwtDQEK6urvDy8tJgxZqV4Zadu3fvfjbt4cOH8PT0xH///QchBIoUKaLW4oiIiCitd+/eoWrVqqhduzaOHj0KGxsbBAYGwsLCQlpm1KhROHXqFLZu3YoCBQrg+PHjGDRoEBwcHNCsWTMNVq8ZGQ47qYNMaGgofv/9d6xfvx6JiYnIly8fPD090aNHj6yokYiIiP5n7ty5yJcvH7y9vaVpn46Bd/HiRXTv3h21atUCAPTr1w+rV6+Gv7//Txl2VDqMFRERgQkTJsDZ2RleXl4wMzPDwoULERgYiF69ekFLK9NXnyAiIqKvOHDgACpUqIC2bdvC1tYWZcuWxdq1a9MsU6VKFRw4cAAvX76EEAJ+fn548OABGjRooKGqNSvD6WT27NkoWLAg5s2bBx0dHUydOhVBQUEYOXIk9PT0srJGIiIi+p/Hjx9j1apVcHFxwbFjxzBw4EAMGzYMmzZtkpZZtmwZXF1dkTdvXujp6aFRo0ZYsWIFatSoocHKNSfDh7EmTZoknY1lZ2cHHx8f+Pj4pFlGoVDgwoUL6q2QiIiIJEqlEhUqVMCsWbMAfLxU061bt+Dl5YXu3bsD+Bh2Ll++jAMHDsDR0RFnz57F4MGD4eDggHr16mmyfI34rstFPHz4EA8fPvzsDKyMnJpORERE3y937txwdXVNM61YsWL4+++/AQCxsbGYOHEi9u7di8aNGwMASpUqhWvXrmHBggUMO19To0YNhhkiIiINq1q1Ku7fv59m2oMHD+Do6AgASExMRGJi4mf9aLW1taFUKn9YndlJhsPO6dOns7AMIiIiyoiRI0eiSpUqmDVrFtq1awd/f3+sWbMGa9asAQCYmpqiZs2aGDNmDAwNDeHo6IgzZ85g8+bNWLRokYar1wyVD2MRERGR5ri5uWHv3r2YMGECfv/9dzg5OWHx4sXo3LmztMyOHTswYcIEdO7cGeHh4XB0dMTMmTMxYMAADVauOQw7REREOUyTJk3QpEmTL863t7dPMw7Pz44D4xAREZGssWWHiIjoO/CknYzT9PUz2bJDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESylq3DztSpU6FQKNLcihYtKs2Pi4vD4MGDYWVlBWNjY7Ru3RqvXr3SYMVERESU3WTrsAMAxYsXR0hIiHQ7f/68NG/kyJE4ePAgdu/ejTNnziA4OBitWrXSYLVERESU3ehouoBv0dHRgb29/WfTIyMjsX79emzbtg116tQBAHh7e6NYsWK4fPkyKlWq9KNLJSIiomwo27fsBAYGwsHBAQULFkTnzp3x7NkzAEBAQAASExNRr149admiRYsif/78uHTp0lfXGR8fj6ioqDQ3IiIikqdsHXbc3d2xceNG+Pj4YNWqVQgKCkL16tXx/v17hIaGQk9PD+bm5mnuY2dnh9DQ0K+ud/bs2TAzM5Nu+fLly8JnQURERJqUrQ9jeXh4SH+XKlUK7u7ucHR0xK5du2BoaPjd650wYQJGjRol/R8VFcXAQ0REJFPZumXnU+bm5ihcuDAePnwIe3t7JCQkICIiIs0yr169SrePT2r6+vowNTVNcyMiIiJ5ylFhJzo6Go8ePULu3LlRvnx56OrqwtfXV5p///59PHv2DJUrV9ZglURERJSdZOvDWL/++iuaNm0KR0dHBAcHw9PTE9ra2ujYsSPMzMzQu3dvjBo1CpaWljA1NcXQoUNRuXJlnolFREREkmwddl68eIGOHTvi7du3sLGxQbVq1XD58mXY2NgAAP744w9oaWmhdevWiI+PR8OGDbFy5UoNV01ERETZSbYOOzt27PjqfAMDA6xYsQIrVqz4QRURERFRTpOj+uwQERERqYphh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkTTZhZ8WKFShQoAAMDAzg7u4Of39/TZdERERE2YAsws7OnTsxatQoeHp64t9//0Xp0qXRsGFDvH79WtOlERERkYbJIuwsWrQIffv2Rc+ePeHq6govLy/kypULGzZs0HRpREREpGE5PuwkJCQgICAA9erVk6ZpaWmhXr16uHTpkgYrIyIiouxAR9MFZNabN2+QnJwMOzu7NNPt7Oxw7969dO8THx+P+Ph46f/IyEgAQFRUVNYVSt+mzu0fp75VyR33e81Q7+7OHT6juL9rRlZt95T1CiG+ulyODzvfY/bs2Zg2bdpn0/Ply6eBakhiZqbpCn5KZnO43TWBu7tmzDGbo+kSfkpmWbzDv3///quPkePDjrW1NbS1tfHq1as001+9egV7e/t07zNhwgSMGjVK+l+pVCI8PBxWVlZQKBRZWm92EBUVhXz58uH58+cwNTXVdDk/DW53zeB21wxud8342ba7EALv37+Hg4PDV5fL8WFHT08P5cuXh6+vL1q0aAHgY3jx9fXFkCFD0r2Pvr4+9PX100wzNzfP4kqzH1NT05/izZDdcLtrBre7ZnC7a8bPtN0z0mqU48MOAIwaNQrdu3dHhQoVULFiRSxevBgxMTHo2bOnpksjIiIiDZNF2Gnfvj3CwsLw22+/ITQ0FGXKlIGPj89nnZaJiIjo5yOLsAMAQ4YM+eJhK0pLX18fnp6enx3Ko6zF7a4Z3O6awe2uGdzu6VOIb52vRURERJSD5fhBBYmIiIi+hmGHiIiIZI1hh4iIiGSNYYeIcjSlUqnpEogom2PYkZnU/c2TkpI0WMnPjf3+f4zr169j7ty5mi7jp8J9m3Iino0lM0IIKBQKbN++HSEhIRg1apQ0jbJGyvYNDAyEgYEBgI/XWVMqldDS4u+JrHTlyhU4OTkhJCQEzs7OMDIy0nRJspb6s+TQoUMwNDSEjY0NSpUqpeHK5O/Tz3F+rquGn8Qy4evri7///lva+c+cOYMiRYoAAN8QWUyhUODo0aPw8PDAiBEjUKNGDRw7dgxaWlo8xJLF3N3doVAoMHXqVIwYMQIfPnzQdEmylfrL1cvLC6NGjcLChQsxZ84c7NixQ8PVyVvqbX/r1i1ER0ez5V5FDDsy8ezZM7Rr1w67du0CAAQFBUFH5+OYkam/cNmQp37//PMPVq5cia1bt2Lr1q2YMmUKmjRpggsXLrBlJwt8GiAtLS0xZMgQJCQkYMyYMQw8WSTly9bX1xcnT57EvXv34O3tjcqVK2Pfvn3YuXOnhiuUr5Rt/8cff6BDhw5o0KAB1q5di+fPn2u4spxDNiMo/6xSEn/Pnj2ho6OD/v37w87ODtWqVUNMTAxevHiBN2/eICIiAsWLF4eNjY2mS5aVFy9ewNPTE0lJSahUqRKEEOjVqxcePHiAvXv3omrVqpouUVYSEhKgp6cHANizZw+USiUKFSqEunXrQk9PD6tXr8b48eMxe/ZsHtLKArdu3cKSJUvw6tUrAICdnR1atGgBpVKJTZs2QUdHB61bt9ZwlfKRukXn2bNnOHToEM6fP489e/bAz88P79+/R+fOnZE3b14NV5r98WdnDpb6jRAcHIyuXbvijz/+QPPmzeHp6Yn9+/dj4MCBGDt2LObPn4/o6GgNVyw/WlpaqFmzJm7fvo2NGzdKr4elpSXi4+M1XJ283Lp1C7NmzQIAbNq0CZMmTcKWLVswa9YseHt7o3r16hg4cCCePHkCT09PtmKqwafbsHDhwmjfvj1y5cqFP/74A/Hx8ciXLx+aN2+Opk2bolKlShqqVH5Sf74fO3YM/v7+qF27NszNzdGrVy/88ssvuHnzJtauXYuXL19quNrsjy07OVjKG2HJkiXw9fXFxo0b0aNHD5iYmKBfv36oV68eunbtCiEEPnz4wF+6apDyAfTgwQPo6elBS0sLY8eOhYGBATZv3oyHDx/Cw8MD27Ztw8yZMzVdrqxcv34dd+/exbhx4/Do0SPcuXMHcXFx2LZtG3x8fKBQKNCjRw9MnDgR+fPnZ1+1TEr9Zbtjxw4kJSVBqVSiW7duSExMhJ+fH1asWIFBgwahQIEC6Nu3r3TonDIvZduvXLkSS5YsgYWFBZKTk1GhQgU0atQIHTt2RHx8PM6fPy+dGEFfxj0zh1u9ejW2b9+OLVu2wNLSEgDQunVrxMbGolu3bjAyMkKrVq0YdNREoVDgyJEjmDx5MgoVKoQrV65gwYIFGDZsGJKTkzFnzhxcuXIF69atQ4UKFZCcnAxtbW1Nl52jpZzV1rlzZ+jq6uLAgQMIDAzEu3fvYGlpiWbNmkEIgZ07d0JHRwddunTRdMmykPrH1J49e9C7d28MGjQIenp66NixI5KTk3HgwAHo6elhyJAh3M+zwMmTJ+Hr64t79+4hNDQUS5YswYEDBwAAjRo1Qo8ePdCqVSuYmppquNLsj2EnB0tISEBAQABWrlwJFxcXxMXFwcDAAEqlEl26dIG2tjaKFy+u6TJl5f79+xg9ejS2bNmCEiVKwNfXFy1atICZmRmGDh0KAwMDnDp1Cvfv30eFChX4BZBJQgipk/fjx4/Rrl076OvrIzw8HMuXL8eQIUNgY2ODZs2aQVdXF7Vr19ZwxfJy+fJlHDt2DGfOnMHMmTNRt25dtGrVCnp6eujRowf09fVRp04dADzrUx1St6ZdunQJW7duxZMnTxAfH4/cuXOjZ8+e2LBhA/7880/o6OigXr16DDoZxD47Ocinx8/19PTw6tUr7NmzBwCkpsydO3ciICAAHTt2lE4/J/UICwtDoUKFUKFCBRgYGKBx48aYNm0ajhw5Ah0dHbRo0QKVKlXCwYMHERkZqelyc7TUH/zLli3DmDFjEBYWhubNm6Nr164IDAzE8uXL8ebNG9ja2qJr167IkyePhqvO2T79jDEwMEDp0qXx22+/4dy5c9i1axf09PSwcuVK3Lx5E126dIGDg4OGqpWX1Pv7qlWrsHHjRtSoUQPW1tZYvHgxoqOjUaRIEXTv3h1OTk4oUaKEhivOWRh2cojUb4SEhARpeps2bfD27Vvs3r0bwMegM336dKZ9NUivg2uBAgXw4sWLNOOKmJiYSGNe5M6dG127dsWqVatgZmb2w2qVo9R9Fnbt2oWVK1fCxsYG0dHR6NixI7p06YJ///0X69evh1KpZMuCGqRsQ29vb5w+fRra2tq4ePEizp8/j7/++gv6+vrw9vbGmjVrYGVlpeFq5SVl2/v7++PUqVNYsmQJevXqhfbt2+PevXtYvnw53r9/D1dXV0yZMgX29vYarjhn4WGsHODTwbwuXLiAQoUKoVatWmjTpg2ePn2KZcuWYe3atQgPD8fu3bvh4uKi4apzvpRtfvnyZbx//x7GxsaoXLkyunbtikOHDuG///5Do0aNsGbNGixatEi6n62traZKloWU/V2pVCIhIQG+vr6YPHkyYmJiMHfuXPz1118wMzPDyZMn8eHDB7i7u3M8o0y6desWXrx4gUaNGgEA9u3bh/nz56Nw4cKoUqUKgoKCMGbMGOTJkwe7d+/Gtm3bkC9fPg1XLT/+/v5o0KAB6tatK03r1q0btLW1sX//fujp6WHkyJHsCP49BOUYy5cvF7Vq1RJnzpwR5cqVE9WqVRN79+4VQggRGRkp7t+/L968eaPZImXg+fPnYvTo0UIIIS5evCjs7e1Ft27dhKurq9ixY4cQQogDBw4IDw8P0a9fP3HgwAFNlitbz549E0IIMXLkSFG9enXRsGFDsWjRInH9+nXRuHFj8fTpUw1XKA8xMTFi3LhxokOHDsLHx0cIIYS7u7sICAgQQgiRnJws9u/fL+bMmSPmz58v7t27p8lyZUWpVH42zdPTUzg5OYkLFy5I0xITE8XWrVvFy5cvf2R5ssJ4mEP8+++/2LVrFw4ePIhNmzbBzMwMDRs2xPz58/H+/Xt07dqVh67UJCwsDH5+fujevTtMTExw+PBhlCtXDnv27MGvv/6KxMREdOnSBU2bNpUGuRO8To1aXb9+HT179sTKlSvx+++/IyAgAK6urrCxscGBAwfw/PlzGBoaarrMHE8IgVy5cqFr167Yvn07duzYASEE3NzcYGNjg7dv38LY2BhOTk5o1KiRNKAjZV7qa+c9fvwYycnJcHFxwdSpUxEbG4v+/ftj9erVqFy5MnR0dNC5c2cNV5zDaThs0Rekl/hfvnwpDh8+LOrUqSOEEOLevXuiePHiol27duLdu3c/uEL5CQkJEYGBgUIIIfz9/UWLFi1E0aJFRXx8vBBCiKSkJLFnzx5hY2MjvLy8NFmq7Hy6vz979kzMmTNH1K5dW/j6+gohhIiNjRVr1qwRJUuWFDdv3tREmbKSepvHxsaKkJAQMWHCBPHLL78IXV1dUbFiRVG9enVRv359UbVqVfH69WsNVitff/zxh6hWrZrw8PAQ7dq1k6ZPmDBB5MmTR1y+fFmD1ckHD3RnU+J/nWOfPHkijY7p4OCAkJAQqWPgvXv3UK5cOaxYsQLm5uaaKlUW7t69i2bNmuHixYv48OEDypUrh4kTJ0JHRwdDhgwBAGhra6N58+ZYtWoVz3JTs5RWsevXrwP4eNX4Ll26oHnz5pg2bRouXrwIPT09hIaGYufOnTwTJZPEJ/0ABw4cCHt7e/To0QOlSpVC27Zt0a9fP/j6+uL48ePYu3cvLzWjJkFBQfjvv/+gVCqxYcMG7NmzBwcPHkThwoWxe/duafiEWbNmoU+fPtzu6qLptEVpXb16VYSFhQkhhFiwYIEoUqSIaNy4sRg2bJgQQojLly+L2rVri0aNGonixYuLW7duabJcWbh7964oX768WL169Wfzrl69KurWrSsGDRr02bz0Wt9INY8fPxbBwcFCiI8tlXXq1BHz5s2T5j958kQ0btxYuLq6iqtXr3Kbq5mXl5dwc3MT9+/fl6bdvXtXTJ48WbRq1UocO3ZMCMF9XV0OHz4sSpQoIUqVKiXq1asnFi5cKKKiosS8efNE8+bNRVRUlLC3txc1atTQdKmyw5adbCQhIQErVqxA8+bNsWfPHgQEBGDbtm2YNGkS/P39MXz4cLi7u2Pp0qVo06YN9u7dy0EDMykuLg7Tp09Hnz590K9fPyQlJeHOnTvYunUrduzYgQoVKmDevHkICAhA375909yXfXQy5/Dhw+jcuTMePHiAxMRE2NjYoGvXrrh48SIWL14MAHB0dETZsmXxyy+/wMbGhts8k2JiYgB8bNl59+4dDh06BC8vLxQuXBiJiYkAgKJFi6Jnz54oVaqU1ILG7Z55Pj4+mDp1KtasWYPr16/DwsIChw8fRlhYGM6cOYNt27bBxMQEHTp0QEhICJ49e6bpkuVF02mLPko5i+rp06di+PDhokiRImLDhg1CiI898W/fvi0qVaokevToockyZWno0KFi5cqVIikpSYwfP160atVKlCxZUpiZmUktOleuXBFXrlzRcKXyceTIEVGmTBnp7J8UMTExYtOmTaJx48bi119/FVu2bBFubm48C0UNrl69KubMmSMSExOFEB/7oLVs2VKcPXtWCPH/rTc7d+4UT58+FcnJyRqrVW4iIiKEkZGRWLRokTTt8ePHolmzZuLSpUvCxsZGPHnyROzYsUN07dpVhIeHa7BaeWLYyQYOHz4sunXrJnW6fPTokWjbtq1wcXERb9++lZa7fv26qFu3rggODmazshoolUqRlJQkZs6cKVq2bCns7OxEkyZNxO7du0VcXJy4ceOGqFChgoiKitJ0qbISHBwsqlevLvz8/IQQQkRFRYm7d++KrVu3ijNnzgghhDh+/Lho0qSJ8PDwENevX9dgtfIRGhoqQkJCxPXr18WNGzeEEEJ07txZNG3aVERHRwshhPj7779FpUqVxJMnTzRZqiz5+vqKsmXLivPnzwshhPj1119F165dhRBC9O/fX5QoUUKULFlS/Pvvv5osU7YUQqQzTCz9MEeOHMH48eOxYMEC1K5dG7q6ugCA0NBQjB8/Hi9evMC2bdukgepSTnUm9YmIiEBgYCAeP36M9u3bIykpCTo6Ojhy5AgWL16Mv//+GyYmJpouUzZSTqsdMWIEHBwcMGfOHNy7dw8vX77EixcvMHv2bAwYMADAx8MuvIht5on/dUiOjY1Fw4YNUbhwYfz+++9wcHBAnTp1kJSUBFtbWzx9+hQbNmxAyZIlNV2yLJ0+fRpDhw5F8eLFERsbi927d0uf50FBQTAzM5Mu6ExqpuGw9VO7deuWKFOmjHR4JC4uTsTExIh//vlHCCHE69evRe/evUX58uWlTsuUOSlN+ClN9F9qITt79qwoV66cOHTo0A+r7Wfx7t070bp1a9GuXTthbm4uevfuLfbt2yfi4+OFl5eX6N27t0hKStJ0mbKQev9O2aZBQUGiefPmom/fviIkJEQIIcSFCxfEhQsXxPPnzzVS58/k/PnzwtLSUpw8eVII8f+fSZS1GHY0yN/fX7Rq1UoIIUR0dLSYOXOmqF27trC3txeVKlUSQnxs8h82bBhHi80kpVIpQkNDRZEiRcTDhw+FECLdPgnPnz8X27dvF0WKFJFGRuYhQ/V79uyZOHbsmPjrr7/STJ85c6bo06cP+4uoQUJCgvT31q1bxdixY8X+/fuFEB/H7GratKkYOHCguHv3rqZK/Gn5+fmJsmXLftZnjbIOz8bSIF1dXcTHx2PgwIFwc3PDw4cP0apVK9y7dw+RkZFYsGABcufOjYULFyJ//vyaLjdHUygUsLOzQ6lSpVCjRg08efIEWlpaUCqVaZazsrKCsbExNm3ahKZNm3Jk5CwghEC+fPnQoEEDtG7dWpq+ZcsW7NmzB6NGjeK1rjLp8uXL6Nu3L+Lj47Fx40ZMnz4d+vr6GDBgAJYuXQoHBwd4eXnh9u3bWL9+fZqLC1PWq1WrFubOnSuNlkxZj312NGz79u24e/cujI2N0bNnT1haWkJbWxtjx45FkSJF0Lt3b02XmOM9ffoUkydPxpYtWwAAvXv3xsGDB+Hv748CBQqkGbYdAAPOD3b37l34+Phg3bp12LVrF4dTUIPw8HDUrFkTpUuXRv78+dG3b184OTnhyJEj+O2339CjRw8MGTIEoaGhSExM5EU9NeTDhw/IlSuXpsv4KfDaWBqS8oXasWPHz+bt2LEDvr6+6NOnjwYqkx99fX1cuHABbdq0wV9//YX169ejd+/eqFixYrqBh0FHPVKHxsTERKnzfUoH8BT29vYwNzfH/v374ezsrJFa5eLs2bO4efMmmjRpgvPnz6Nx48a4fPkymjRpgvz58+OXX36BQqHAkCFDoKuri/79+2u65J8ag86Pw5adHyz1F8CnLQgvXrzAvn37sHr1auzYsYO/cNUoJCQETZo0Qb58+bBv3z4AH1t4fHx8cO7cORQsWFCzBcpM6n17yZIluHHjBpKTk7Fu3Tro6Oh8FnjYmpZ5Pj4+mDJlCsaMGYOiRYuiVKlSePfuHZo0aQJXV1fMmTNHutTM8ePH4eLiAicnJw1XTfRj8MD4D/ClPPnph/uzZ8/w/Plz7Ny5k0FHzXLnzo1Dhw7h6dOnaNGiBQBg/fr1qFWrFipUqMDj5mqWsm+fPHkSGzduRNOmTfHmzRuUKFFCCjpJSUmfLU/f5+TJkxg8eDCWLl2Kdu3aoVSpUhBCwMLCAvv378f169cxfvx4hIWFAQAaNGjAoEM/FbbsZLHUv1g3btyI27dvw9HREeXKlUOVKlU+Wz4uLg4GBgY/ukzZSdnu169fh7a2NpKTk1G6dGmEhITAw8MDzs7O+OuvvwAAN2/e5LgiWWD9+vU4fvw4PD094erqCgBo0aIFAgMDce3aNemwFmWep6cnXFxc0KVLl89azQAgMjISlSpVQt26dbF06VJ2AKefDvf4LJYSdFatWgVvb2/Uq1cPs2fPxpkzZ9Isl5I5GXQyLyXoHD58GN26dcOmTZswaNAg7Nq1C7lz54aPjw+uX7+Opk2bAgBb0bJIdHQ0du/ejUuXLknT9u3bB1tbWyno87eWevz77794/PgxAHwWdC5fvoz379/j4sWLGD16NIMO/ZTYQTmLCSHw8OFDHDt2DAcPHsSuXbtQokQJjB07FkqlElFRUTA3N2czvhopFAqcOnUK06ZNw7Fjx7Bz505ERERg5syZSExMROfOnXH27Fk8evQIAPjhrwaf9rlJTk7G8OHDoaWlhZEjR6JAgQKoW7cuAMDPzw8vXrwAwMNX6lK+fHnExsZCqVRCoVBAoVBIr8k///yDu3fvomfPnrCwsNB0qUQawbCTxZRKJaysrFC0aFHMnDkTN2/exL59+6CtrY2lS5ciX758aNmypabLlJ3Hjx9j8eLFCAgIwMaNG7Fz506sWrUKI0eORFxcHHr37o3cuXNrukxZSB10Vq1ahRs3buD+/fuYMWMGhgwZAi0tLXTu3Bne3t7w8PAAAOTNm1eTJctOtWrV0KpVK+k0cx0dHSgUCmzbtg0bNmzAzp07NV0ikUYx7GShrVu34u7du5g4cSJOnTqFx48f482bNwCAnTt3Yu3atVK/Efp+KYdCUv+a7dOnjxRq1q1bhxIlSqBQoULo0KEDihQpouGK5SV10Nm0aRMWLVqEPXv24Ndff8X48eMxePBgREVFYfDgwbh16xYMDQ3ZoqMGycnJ0NbWBgDUq1cPa9aswcCBA/HgwQPo6OjA1tYWGzZswO7du+Hi4qLhaok0ix2U1ejcuXN48+YNmjdvDi0tLcyaNQuOjo7o3LkzXrx4ATc3N7i5ucHS0hI3btzAli1b2F8kk1J3xjxx4gSuXLmC+Ph49O3bF3ny5EH37t0BAP369cOgQYOwefNmlCtXjqc6q5lSqUS3bt0wdOhQuLu7A/h4yvmiRYtw/fp1mJub4927dzyMkkn+/v5Yvnw5Nm/eDOBj4Ek9PtSZM2fwzz//4ObNm3B1dUWzZs1QtGhRTZZMlC2ws4Ia3bhxAwMGDJDGcQkKCpLOOMmbNy/u37+Pdu3aoXXr1tizZw+DTiaFhoaiTJkyiIiIwO3bt9GzZ0/Y2NjA19cXU6ZMwbFjx9C7d2/Ex8fj119/xcyZM1GuXDkA7CuSWZ/+RoqPj0dgYCCuXr0qTRs+fDjKlSuHmJgYAGDQUQMLCwvcvn0bffv2BQBoa2unueRJzZo1MXr0aGzcuBFjx45l0CH6Hx7GUqPBgwdDR0cHY8eOhbW1NUxNTREYGIiQkBA8fvwYCQkJqFu3LvuKqIm9vT0KFSqE4sWLo0uXLli9ejUaN26M7t27Y8qUKdi8eTN27NiB2rVr49WrV7Czs2OLjhqk3oYvXryAgYEBrK2tMWXKFIwePRq5c+dG69at8eeff+LJkyfQ19fXcMU5X8o2d3FxwaxZszB06FAMHz4cS5YskYZWSDmkRUSf42GsTEr5EEp9uYE//vgDCxcuhJGREfLly4e8efMiODgYSqUSXl5eHBI/kz79YB83bhzmz5+PdevWoVevXgCA2NhYuLu7Y/fu3eyjo0b3799H3rx5YWRkhPnz5+PAgQN4/fo1OnXqhJo1ayIsLAz9+/eHh4cHbt++jT///JMtmGo0f/58XLp0Cbly5cKlS5dQrVo1bNq0CcDn7wsi+n9s2cmE1L9wb9++DQAoWbIkRo4cCSsrK0yePBm9e/eWrn/16QUnSXXx8fE4ceIEKlWqhMDAQBw/fhxz585FREQEJk+ejLp168LR0RH37t2DUqmEsbGxpkuWBSEE4uLi0KJFC9SvXx8tWrTAgQMHcODAAVy4cAFnz57FwYMHMX/+fJQvXx4KhQKGhoawt7fXdOmycfXqVWzbtg3+/v7Q1dVFYGAgunXrhmHDhmHp0qUMOkRfwbCTCSlBZ+nSpdi+fTvy58+Ply9f4uTJk+jWrRuio6MxZMgQGBsbo2nTpgw6ahAdHY3g4GA0atQI0dHR0im1q1evRnx8PEqUKIFOnTohOjoaU6dORZ48eTRcsTykhJcDBw6gXbt2uH79Ojw8PGBhYYEmTZrA2toavXr1QsuWLVGtWjVNlytL8fHx0NLSQlJSEnR1dVGwYEH88ssv+P3336FQKLBkyRJNl0iUbfHbN5M2bNiAPXv24Pjx4yhUqBACAgJQunRpvH//HoMGDcKcOXNQrFgxTZcpG1ZWVrC2tsaNGzdgZ2cHa2trad7GjRvRpUsXrF27FlOnTkWbNm04Qq8apGzD5ORkuLi44O+//8aHDx9w6tQpREVFAQAqVaqEsmXLIjg4WJOlykZ6+23p0qVRqFAh7Nq1C1FRUdDW1oadnR1mzpyJESNG/PgiiXIQ9tnJhMjISKxduxY9evTApk2bcOrUKRw+fBiurq6Ij4/H9evXeRglCyQnJ8PX1xf+/v4ICAjA5MmTUb58eTx9+hSOjo64f/8+++moSepDtUFBQVAoFChQoACCg4Ph4eGBypUro3HjxoiOjsb48eNx+vRpXmAyk1Jv8xUrVuDFixd4+vQpFi5ciJ07d+LatWt49+4d3NzcsH79evj6+qJgwYIarpooe+NhLBV8eiaPmZkZ+vXrh9jYWJw9exY7duwAALRp0wY+Pj54/fo1w44apGz3gIAABAcHo3DhwmjQoAEKFSqEyMhITJs2DR4eHti8eTPWrFkjXdSTZ15lzq1bt5AnTx5YWFhg3rx52Lt3L8LCwtC8eXP06NEDPj4+aNmyJQ4ePIjevXvj6NGjDDpqkLLPLlmyBPv27YO3tzeKFy+OEiVKYOLEibhw4QLOnz+P6OhoHDlyhEGHKAPYspNBqTsXHzhwAEFBQWjevDkKFCiAly9fonjx4tizZw9ev36NHTt2wNvbm+OKqNHhw4cxYcIEuLu7IzAwEK1bt0b//v0REhKC7du3Y9++fZgyZQoaN26s6VJzPKVSieTkZFSqVAnFihVDz549MXPmTBw4cAA3btzAkSNH8PbtWyxfvhxPnz5F9+7dsWPHDvaPyqTU4fz169cYMWIEtm3bhsWLF+PEiRPYs2cPdHR0pI7IPOGBSAWCvunMmTNi/vz5QgghVq5cKQoXLixat24tXFxcxJEjR4QQQixcuFCULFlSVKxYUfz777+aLFd2Tp8+LUqXLi1CQ0PFjh07hJOTk2jTpo1YsmSJiI2NFUIIERUVJYQQQqlUarJUWXj69KkQQoiwsDBRtWpV4ebmJiZOnCjNv3nzpihevLg4ePCgEEKIhIQEjdQpJ//9958YMWKE9P+LFy9EkyZNRJ8+fUTTpk2l/Xz69Oli8eLFQgju60Sq4GGsDIiPj8fcuXMRHR2N9+/f4+LFi7CyssLcuXPx+++/AwBGjRqF7t27Q0dHB2ZmZhquWF709PSwfv16/Pvvv1i4cCGOHz+OFStWYNGiRYiMjMTEiRNhZGQEgCMjZ4YQAm/evEHLli1x4sQJWFtbY//+/WjTpg38/Pzw9u1bWFpaokSJEqhcuTLevn0LANLlOuj7CCFgYWGBcePG4eLFiyhTpgzy5MmD/Pnzw8/PD/v374eBgQG2bNmCXbt2Yffu3QC4rxOpgp9SX3H16lU4Ojqifv36+Pvvv9GzZ0/Y2dkhMjISVlZWGDduHBQKBUaPHg0dHR3Ur19f0yXLgkg1UGNycjIqV66MxMREDB48GEOGDIGzszOqVauG169fo3Xr1hxfRA1StrmNjQ3Onj2LEydOICgoCCNHjsRff/2FRo0aYejQoWjSpAmUSiWOHz+O8ePHA+CXbmakbHdHR0e8evUKK1aswNu3b7F//3707dsXiYmJ+OWXX9CwYUOcP38e27dvZ+d7ou/AA75fcPToUXTo0AEnT55EYmIiatSoga1btyI0NBQHDhxAXFwcAGDs2LHo168fryqsJikf/ocOHUL//v3RokULHD9+HLq6urCyssLZs2exdu1azJ07F/369YOrq6umS5aFiIgIAEBiYiKMjIygVCoxevRoeHl5wcrKCkePHsWrV68wdOhQPHr0CMeOHUOhQoU0W3QOJ1L10Vm+fDnmzp2LCRMmwNLSEp07d0bx4sWxZs0azJ07F+3bt8f+/fs5GjXR99LgIbRs6/Dhw6Js2bLi3Llzn807c+aMcHJyEgsXLhQfPnzQQHXyd+jQIeHm5ibu3Lkj6tatK+rWrSuEEOLIkSNi5MiRolq1auLAgQMarlI+jhw5ImrVqiVatGghpk2bJsLDw4UQQhw9elRoaWmJlStXCiE+9uFp2LChePbsmSbLlYXQ0FARHx8vhBDC399ftGzZUkRGRgohhLh3755o166daN26tYiJidFkmUSywbDzidjYWNG+fXtx8uRJIYQQ7969E1evXhVjx44Vu3btErGxseLy5cvCzMxMLF++XMPVykNKB9fk5GQRHx8vBg8eLF68eCH27dsnqlSpIoKCgoQQQsTFxQkhhHj//r0Qgh001eHo0aOibNmy4siRI2LmzJmiffv24syZM9L8Q4cOCX19fbFgwQIhxMfXiDLn4MGDom7duuL169fi3LlzwtLSUnTu3Fnan5VKpbh//75o3Lix6NixozSNiL4fw84nYmNjRbVq1cTWrVtFVFSU6NOnj2jVqpUoU6aMqFGjhpg6daoQ4uMZQoGBgRquNue7ffu2aNCggbh+/boQ4mOg6dGjh+jYsaOoVq2aePz4sRBCiB07dogBAwaIuLg4fuGqydWrV0WePHnEli1bpGk9e/YUS5YsEUII6Qyg/fv3CysrKxEeHs4v3Uzy8fER7u7uwsfHR5o2ffp0UahQIXHt2jVpmlKpFIGBgeLFixeaKJNIdhh20rFlyxaRP39+YWdnJ3r27CmOHj0qhBBi165dokGDBlLzM2Xe0KFDhUKhEJUrVxZXrlwRQgixYcMG4eLiIrZt2yaE+HjosHjx4tJp/qQeR48eFQ0bNhTLli2TWs/atm0rihUrJipXriwaN24s/vzzTxEbG8tDtmpw8eJFYWRkJM6ePSuE+Hi4KuV08wEDBogKFSqkCTxEpD48GysdXbp0QcWKFREaGooaNWpAqVQCAGJjY2FsbIykpCTo6elpuEp56NevH6Kjo5E7d24MHDgQq1evRtu2bREYGIjp06fj0KFDuHHjBubOnQsPDw+OiqxGjRo1QlxcHLZv347ExETcvXsX4eHh2LNnDwICAhAQEIDNmzejVq1acHBw0HS5OZoQAi9fvoSbmxsePnwIZ2dn9OvXTxoEc9WqVRg+fDhat26NvXv3SqOAE5F6cATlDNq2bRv++OMPeHt7o0SJEpouJ0cTn4wU26FDBzRu3BgGBgbw9vbGpk2bULx4cfz333+Ij4+Hqakpz7pSs9Svwd69e7F+/XoEBQVh7969KFy4sLTchw8fkCtXLk2VKSsxMTHw8fHB5s2bcf78ecyYMQMDBw5EYmIidHV1AQBjxozBoEGDeNkNIjVjy843vH79GuvXr8eff/6JHTt2MOhk0r179zB+/Hi0a9cOVatWhaOjI6ZPn441a9Zg9OjRePXqFXr37o1FixahSpUqmi5XthQKhRR4WrZsCUNDQ2zYsAFHjx6FUqlE0aJFAQCGhoYarlQ+jIyM4OHhAeBjiEwZvkJXVxfx8fHQ19fH/PnzNVkikWwx7HyDlZUV6tSpg44dO6JAgQKaLidHS05Oxrp163Dw4EF8+PABM2bMwJQpU5CYmAgbGxuYmppK/w8aNAhnzpyBqakpD1upwZcO/6VcX6lRo0aIj4+Hl5cXkpOT4ezsDB0dHW57NcuVK5d0OHb79u2Ijo7GlClToK+vr+nSiGSNh7Hoh3r69CnmzJkDOzs7xMfHI3fu3Lhw4QJ2796NiRMnYvr06Xj79i0iIiLg7Oys6XJlIXXQefXqFZKSktJctDP1/MOHD0uXK6CsExMTg2PHjmHVqlWoX78+xo4dq+mSiGSNYYd+mJQv1cDAQMydOxeWlpbw8PCAu7s7tm7ditKlS8Pd3V3TZcrWkiVLcOzYMbx+/Rru7u6YP3++1B+HHb/VJ/W2TN0fJykpKc11xKKjo+Hr64vy5csjb968GqmV6GfBsEM/VMoXwcOHDzF37lxoa2ujf//+KFu2rKZLk7W1a9dix44dOHToEIYNG4bg4GAcOnRI+lJm2FGP1NtxyZIluHHjhnT4VkdH57PAw+1O9GPw2lj0Q6V0jHV2dpYuJLl8+XKcPn1as4XJmFKpxJs3b7Bt2zasW7cOz58/x759+6BQKODv7w+AF/NUl5TtePLkSWzcuBFNmzbFmzdvUKJECSnoJCUlfbY8EWUthh3KMuk1Gqb8khVCoFChQhg9ejSEELCxsdFAhT8HLS0thIWFoXLlyjh27Bh8fHygq6sLLy8vzJw5E9HR0ZouUVbWr1+PtWvX4s8//0SLFi1w6NAhFC1aFKVLl0ZiYmKalh0i+jEYdihLpISao0ePYvr06Rg0aBDCwsKkX7IpgcfFxQUrVqzg1ZyzwM6dOzFs2DAAQKdOnZA/f35UqlQJAODt7Y01a9Zg1qxZMDY21mSZshMdHY3du3fj0qVL0rR9+/bB1tZWGk6BvQeIfiz22aEsc/jwYfz2229YtWoVWrRoAQ8PD6xevZq/bLPIp/0/Ll26hJ49e6JDhw6YOnUqdu7cCS8vL2hpaSE5OZkhUw0+3ebJycnQ1tbGsmXLMGnSJOzduxd169aV5r948YKdkYk0gN86lCXev38Pb29vHD58GP/88w+cnJwwbdo0Bp0fIOULt1KlSti8eTN69+4NbW1tTJkyBe3bt0dYWBgMDQ3ZopNJqYPOqlWrcOPGDdy/fx8zZszAkCFDoKWlhc6dO8Pb21saTJBBh0gzeBiLsoRSqYSenh68vLwwZ84cbNy4EXnz5sWmTZuwYMECTZcnK0+fPgXw8dCgj48P6tevjw8fPkChUMDNzQ2rVq3C2rVrpQ7hNjY2DDpqkDrobNq0CV27dkW5cuXw66+/4uDBgxg8eDCGDx+OwYMH48OHDzx0RaRBDDuUaUII6WKpKczMzJA7d25s2LAB8+bNg4uLCy5cuIB58+ahdOnSGqpUfkJCQjBkyBDMmTMHAFCkSBGYm5ujc+fOUuApXbo0qlevjhMnTiAsLIxfumqkVCpx4cIFLFmyBFWqVMGCBQvQvn17DB06FBEREZgwYQICAgKQK1cunnlFpEEMO5QpKV+oWlpaOHr0KCZNmoTZs2cjLi4OvXv3RoMGDTB48GBMmjQJ/fv3x7x581C/fn1Nly0b+vr6aNmyJa5cuYKlS5fCyckJixcvhr6+Ptq1a4fk5GT4+vrCyMgIx48fh42NDb90M+HToBgfH4/AwEBcvXpVmjZ8+HCUK1cOMTExAAALC4sfWiMRfY4dlOm7RUVFoXnz5pgxYwZsbW3RrFkztGnTBnfu3EFUVJQ0aN327dthbm4Oe3t7jpCsJg8fPoSBgQHMzMxgaGiI7du3Y/fu3ahXrx6GDRuGly9fYtCgQXj06BGEENi2bRtb1DIpdR+dFy9ewMDAANbW1jh06BBGjx6NWbNmoXXr1vjzzz+xYMECnDhxAtbW1hqumogAhh36To8fP8aNGzdw9+5d7N+/H46OjhgzZgwqVKiAsLAwDB8+HK9fv8a+ffvYP0TNDh8+jEmTJsHe3h4lSpTAjBkzEBMTgyNHjmDnzp2oX78+hg8fDgC4du0aHBwcYGtrq+Gqc7b79+8jb968MDIywvz583HgwAG8fv0anTp1Qs2aNREWFob+/fvDw8MDt2/fxp9//skz3YiyEYYdUtm9e/fQrVs3TJgwAY0aNcLatWsxceJELF++HD169IAQAm/fvkW/fv0QEhKCCxcuQKFQ8PCJGhw/fhzjxo3D6tWrkZiYiN9++01qOdPT08PWrVvx119/oXz58pgyZYqmy83xhBCIi4tDuXLlUL9+fbRo0QKenp44cOAALly4gLNnzyI5ORnz58/HkydPoFAoYGhoCHt7e02XTkSp8DxgUsn9+/fRvn17zJo1C40bNwYAdO/eHREREVi4cCEKFCiAWrVqwdraGmvWrMGLFy+gpcWuYepw8eJFdOjQAVu3bkXFihXx+vVr3L59G4MHD8b79+/RvXt3dOnSBXFxcfDz80N4eDgsLS01XXaOlhJeDhw4gHbt2uH69evw8PCAhYUFmjRpAmtra/Tq1QstW7ZEtWrVNF0uEX0Bv4Uow+7fv49mzZrh5s2bcHJyAvDxSs5mZmYYNmwYunXrhjFjxuDEiRMAAGtra5QpU0aDFcuLlZUVjIyM8OzZM9y7dw8tWrTAmDFjMGPGDLRo0QKzZ8/G06dP0bZtW6xatYpBJ5NSGr2Tk5Ph4uKCv//+Gx8+fMCpU6cQFRUFAKhUqRLKli2L4OBgTZZKRN/Aw1iUIbdv35bCzIcPHzBmzBj8/fffqFWrltRxMyIiAsuXL8fOnTtx5swZWFhY8NCVmt24cQPt27dHeHg4xo8fj5EjRwIAwsLC0LNnTyxevBjOzs4arjLnS90ZOSgoCAqFAgUKFEBwcDA8PDxQuXJlNG7cGNHR0Rg/fjxOnz4t/QAgouyHLTuUIXFxcejXrx86dOiAXr16YcKECWjdujXOnj0rXefK3NwcgwcPxv79+2FpacmgkwVKlSqF/fv3w9raGvHx8YiNjQUAnDt3DqGhoTAyMtJwhTnfrVu3EBERAQCYN28eOnXqhHr16mH06NF4+/YtfHx8cO3aNQwYMAB3797F0aNHGXSIsjm27JDKUn71Llq0CDNnzsS+fftQvXr1z64TRFnnxo0baNu2Lfr06QNLS0t4eXlh48aNPAMoE5RKJZKTk1GpUiUUK1YMPXv2xMyZM3HgwAHcuHEDR44cwdu3b7F8+XI8ffoU3bt3x44dO5AnTx5Nl05E38CwQ5myePFijB07FidOnEDNmjU1Xc5P5ebNm2jQoAF0dXXh4+MDV1dXTZeUoz179gz58+fHmzdv0KJFCyQkJKB+/fqYOXMmgI8tPh06dMCcOXPQpEkTJCYmQldXV8NVE1FG8GwsypQRI0YgKSkJCQkJmi7lp1OyZEmcP38eCoUCBQsW1HQ5OZYQAm/evEHLli2lgQD379+PNm3awM/PD2/fvoWlpSVKlCiBypUr4+3btwDAi9oS5SBs2SG14WEsymlS77MxMTE4ceIEgoKCMHLkSLx9+xaNGjWCi4sLmjRpAqVSiUmTJuHUqVMoVKiQhisnIlWwgzKpDYMO5TQpHZETExNhZGQEpVKJ0aNHw8vLC1ZWVjh69ChevXqFoUOH4tGjRzh27BiDDlEOxJYdIvopHT16FPPmzYO5uTnKli2LoUOHwsLCAj4+PmjcuDGWL1+OgQMH4s2bN+jSpQvWrl2LfPnyabpsIvoObNkhop+Oj48PJk2ahLFjx8LNzQ137tzBzZs3AQCNGjXCgQMHMHLkSCxcuBDW1tY4cuQIgw5RDsawQ0Q/lX/++Qd9+vTBqFGj4OHhgYkTJyJXrly4du0agI9jSjVu3Bi7du3C7Nmz8e7dOx6iJcrheDoBEf1U3rx5gxIlSiAiIgJPnjxBgQIFEB0dDS8vL+zYsQOWlpbo1KkTWrVqhefPn8PQ0FDTJRNRJjHsENFPpVGjRoiLi8P27duRmJiIu3fvIjw8HHv27EFAQAACAgKwefNm1KpVCw4ODpoul4jUgB2UieinkfpU871792L9+vUICgrC3r17UbhwYWm5Dx8+IFeuXJoqk4jUjC07RPTTSLmOm0KhQMuWLWFoaIgNGzbg6NGjUCqVKFq0KADw0BWRzDDsEJFsfWmgS6VSCS0tLTRq1Ajx8fHw8vJCcnIynJ2doaOjww7JRDLDsENEspQ66Lx69QpJSUnIkycPFApFmhae5s2bQ0dHB2XKlOElIIhkin12iEjWlixZgmPHjuH169dwd3fH/Pnzpf44vMQJ0c+B4+wQkWytXbsWBw4cwN9//42yZcviyZMn7I9D9BNi2CEiWVIqlXjz5g22bduGdevW4fnz59i3bx8UCgX8/f0B8HpuRD8Lhh0ikiUtLS2EhYWhcuXKOHbsGHx8fKCrqwsvLy/MnDkT0dHRmi6RiH4Qhh0ikpWdO3di2LBhAIBOnTohf/78qFSpEgDA29sba9aswaxZs2BsbKzJMonoB2IHZSLK0T7tZHzp0iX07NkTHTp0wNSpU7Fz5054eXlBS0sLycnJWLFiBYoXL67BionoR2PYIaIcLSXsJCcnQ1tbG0IIXL16Fb1790a7du0wZcoUAEBYWBgMDQ3ZokP0E+JhLCLKkZ4+fQrgYydjHx8f1K9fHx8+fIBCoYCbmxtWrVqFtWvXYvz48QAAGxsbBh2inxTDDhHlOCEhIRgyZAjmzJkDAChSpAjMzc3RuXNnKfCULl0a1atXx4kTJxAWFgY2YhP9vBh2iCjH0dfXR8uWLXHlyhUsXboUTk5OWLx4MfT19dGuXTskJyfD19cXRkZGOH78OGxsbHiaOdFPjH12iCjHePjwIQwMDGBmZgZDQ0Ns374du3fvRr169TBs2DC8fPkSgwYNwqNHjyCEwLZt21C6dGlNl01EGsYLwRBRjnD48GFMmjQJ9vb2KFGiBGbMmIFffvkFwMfTzYUQGD58OPbv349r167BwcEBtra2Gq6aiLIDhh0iyvaOHz+OyZMnY82aNUhMTMRvv/2GqKgomJubo2vXrlAoFPjrr78QFRWFKVOmoEyZMpoumYiyEYYdIsrWLl68iA4dOmDr1q2oWLEiXr9+jdu3b2Pw4MF4//49unfvji5duiAuLg5+fn4IDw+HpaWlpssmomyEYYeIsjUrKysYGRnh2bNnuHfvHnr16oUxY8agSZMm8PPzw+zZs1GlShW0bdsW7dq1g6mpqaZLJqJshh2UiSjbu3HjBtq3b4/w8HCMHz8eI0eOBPBxoMCePXti8eLFcHZ21nCVRJRd8dRzIsr2SpUqhf3798Pa2hrx8fGIjY0FAJw7dw6hoaEwMjLScIVElJ3xMBYR5QiFCxfG9u3b0bZtW2hra8PS0hJeXl7YtGkTcufOrenyiCgb42EsIspRbt68iQYNGkBXVxc+Pj5wdXXVdElElM0x7BBRjvPo0SMoFAoULFhQ06UQUQ7AsENERESyxg7KREREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0QyVKtWLSgUCummo6MDe3t7tG3bFkFBQWp/vCdPnkiPdfr0abWvXxMKFCgAhUKBqVOnfnPZqVOnSs+/fv36aebZ29tDoVCgR48eWVMoEX0Tww6RjOnp6cHd3R0FCxbEq1ev8Ndff6Fp06aaLkuthBBITEzUdBmSkydP4tSpU5oug4hSYdghkrHcuXPj8uXLePDgAbp27QoAuH37Nt6+fSstExkZieHDh8PR0RF6enrImzcvRo0ahQ8fPkjL3L9/H82aNYOtrS309fWRN29eeHh4wN/fHxs3boSTk5O0bO3ataFQKFCrVi0AwJ9//omKFSvC2toaurq6sLCwQMOGDeHv7y/d5/Tp01LLyJMnT6TpKdM2btwIANi4caM0zcfHB8WLF4euri4uXLiAa9euoW7dusidOzf09fVhZGQENzc3bN26Nc02Sbn/woUL0aVLF5iYmCBPnjyYMWMGgP9vpXr69CkAYNq0adJ9MmrixIlfnT9+/HgUL14c5ubm0NXVhYODA7p3746QkBBpmdStRUePHkXhwoVhZGSEzp07IyYmBjNmzICNjQ1y584NT0/PNOvPyGtK9FMRRCQ7NWvWFACEo6OjNK1r164CgDAzMxMJCQlCCCHi4+NFmTJlBABhYGAgSpUqJQwMDAQAUadOHaFUKoUQQpQtW1YAEBYWFqJs2bIid+7cAoDw9vYWhw4dktYBQBQrVky4u7uLgQMHCiGEGD58uDAwMBCFCxcWpUuXFvr6+gKAMDExESEhIUIIIfz8/KT7BwUFSTWnTPP29hZCCOHt7S1N09PTEwUKFBAFChQQfn5+Yu/evUJLS0s4OjqKsmXLCgsLC2nZQ4cOfbZOXV1dkTt3bmFtbS1NO378uAgODhbu7u5CT09PABB58uQR7u7uwt3d/Yvb29PTU1pHyrbas2ePEEIIOzs7AUB0795dWr506dLCzMxMlChRQhQtWlQoFAoBQLi5uaW7TmNjY1GkSJE029jQ0FAULFhQmubj46PSa0r0M2HYIZKhlLCjp6cn3N3dhYuLiwAgLC0tpS9hIYTYuHGjtNyDBw+EEEJcu3ZN+gI9efKkEEIIY2NjAUCcP39euu/jx4+lYBIUFCTdx8/PL00tDx48EDExMdL/gYGB0rLr1q0TQnxf2Bk3bpy0XFJSkggJCRGhoaHStNjYWOHs7CwAiC5duny2zsqVK4v4+HgRFhYmdHV1P1uno6OjACA8PT2/ub1TB5OjR48KAMLV1VUkJyenG3Zu3LghkpOTpf/Xrl0r3f/hw4efrXPr1q1CCCGqVq0qTTt//rxITk6W6kypPaOvKdHPhIexiGQsISEBV65cQWBgIADA1dUVVatWleanHEpKSEhA4cKFoVAoUKZMGWn+5cuXAUDq51O7dm0UK1YMrVu3ho+PD3Lnzv3NGt69e4fmzZvD0tISWlpacHFxkeYFBwd/93MbMWKE9Le2tjYUCgVGjx4NBwcH6OjowNDQEA8fPvzi47Rr1w56enqwtraGra0tAODVq1ffXU+KWrVqoUGDBrhz5w62bNmS7jLXrl2Dm5sbjI2NoVAo0LdvX2leerWmbP8CBQoAACwsLFC1alVoaWnB0dExTe0ZfU2JfiY6mi6AiLKOo6MjHj9+jBMnTqB58+Y4f/48+vbti/3796dZTk9PD2XLlv3s/hYWFgCAzZs3o1mzZjh9+jTu3LmDI0eOYM+ePbh16xZWrFjxxcePjo5Gw4YNERERAQMDA5QtWxa6urq4cuUKACA5ORkA0vSHSZkWGRn51edmZ2eX5v8uXbrg5MmTUCgUcHV1hbGxMe7cuYP3799L60zN3Nxc+ltH5+NHoRDiq4+ZUbNmzcKJEyfg6en5Wefp8+fPo3v37hBCwMrKCq6uroiOjsbdu3cBIN1aTU1N09SZ8j/w/9vu09q/9ZoS/UzYskMkc1paWmjYsCEGDx4MADhw4ACuXr0KAHBzcwPw8Qt25cqVuHz5Mi5fvozTp09jzJgx6NSpEwDg3LlzaNmyJby8vHD27FmpQ+zZs2cBALly5ZIeLyYmRvr7/v37iIiIAABs2LABAQEBWLx48Wc1prSsAMCDBw8AALt37/7q8/q0w3BKi0Xfvn1x69YtHDlyBMbGxl9dx9ekPKfUzyejypcvjzZt2uDp06cIDw9PM+/KlStSMLl58yb8/f3RrVu3767zUxl9TYl+Jgw7RD+J0aNHQ09PD8DHlgcA6NixI0qVKoXk5GS4ubmhRIkSKFKkCMzNzdGmTRspqHTt2hUWFhYoUqQIypYti99++w0AUKpUKQCAjY0NrKyspGXd3d2xbNkyFCxYEEZGRgCA3r17o1SpUmjRosVntbm4uCB//vwAgE6dOqF27dpSOMuolFrWrVuH4sWLo1ChQoiLi1NpHakVLVoUALB06VK4ubmhZ8+eKt1/+vTp0NbW/mKdAFCyZEkUK1YM8+fP/+46P5XR15ToZ8KwQ/STcHBwkE4/379/P27fvg19fX2cOXMGw4YNQ758+fDgwQO8e/cOFSpUwMyZM6VDRT179kTx4sXx5s0b3LlzB/b29ujXrx+WL18O4GMry9q1a+Hs7IyoqCj4+/vj6dOnsLCwwO7du+Hq6gqlUgk9PT0cPHjws9p0dHSwc+dOlC1bFnFxcQgPD8fevXtVen4bN25E7dq1YWBggA8fPmDx4sVpgoWqZsyYgUqVKkFLSwv//PMPbt68qdL9ixQpku5AgvXr18fcuXPh4OCA2NhYFC1aFKtWrfruOj+V0deU6GeiEOo6SE1ERESUDbFlh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZO3/ALaIp4SWosYqAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To better visualize the number of orders for the top 5 restaurants, a bar chart was created.\n",
    "\n",
    "# Create a list of colors to use for the bar chart restaurant names.\n",
    "colors = ['red', 'green', 'blue', 'purple', 'black']\n",
    "\n",
    "# Create bar chart of the top 5 restaurants with the highest number of orders.\n",
    "restaurant_bars = plt.bar(top_restaurants_df['Restaurant Name'].head(5), top_restaurants_df['Number of Orders'].head(5),color=colors)\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the bar chart output.\n",
    "plt.xlabel('Restaurant Name', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Number of Orders', fontsize=10, fontweight='bold')\n",
    "plt.title('Number of Orders for Top 5 Restaurants', fontsize=12, fontweight='bold')\n",
    "plt.xticks(rotation=45, fontsize=8, horizontalalignment='right')\n",
    "\n",
    "# Add order numbers to the histogram bars for readability.\n",
    "for restaurant_bar in restaurant_bars:\n",
    "    yval = restaurant_bar.get_height()\n",
    "    plt.text(restaurant_bar.get_x() + restaurant_bar.get_width()/2, yval + 0.05, yval, ha='center', va='bottom')\n",
    "\n",
    "# Display the bar chart.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tnjhWgUfJY_U"
   },
   "source": [
    "#### Observations:\n",
    "\n",
    "The top 5 restaurants with the highest number of orders is shown in the chart below. \n",
    "\n",
    "Restaurant Name -- Number of Orders\n",
    "1. Shake Shack -- 219\n",
    "2. The Meatball Shop -- 132\n",
    "3. Blue Ribbon Sushi -- 119\n",
    "4. Blue Ribbon Fried Chicken -- 96\n",
    "5. Parm -- 68"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gWPMqGq0JY_U"
   },
   "source": [
    "---\n",
    "### **Question 8**: Which is the most popular cuisine on weekends? [1 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The most popular cuisine type on weekends is American with 415 orders.\n"
     ]
    }
   ],
   "source": [
    "# Get the most popular cuisine type and the number of orders.\n",
    "\n",
    "# Filter the DataFrame to only include orders made on weekends.\n",
    "weekend_orders_df = foodhub_df[foodhub_df['day_of_the_week'] == 'Weekend']\n",
    "\n",
    "# Get the most popular cuisine type and the number of orders for weekends.\n",
    "cuisine_counts = weekend_orders_df['cuisine_type'].value_counts()\n",
    "most_popular_cuisine = cuisine_counts.idxmax()\n",
    "number_of_orders = cuisine_counts.max()\n",
    "\n",
    "# Print simple output of the most popular cuisine type and the number of orders for weekends.\n",
    "print(f'The most popular cuisine type on weekends is {most_popular_cuisine} with {number_of_orders} orders.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bpQ270TUJY_U"
   },
   "source": [
    "#### Observations:\n",
    "The most popular cuisine on the weekend, Saturday and Sunday, is American with 415 orders."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "88xgTqkxJY_U"
   },
   "source": [
    "---\n",
    "### **Question 9**: What percentage of the orders cost more than 20 dollars? [2 marks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percentage of orders costing more than $20 is 29%.\n"
     ]
    }
   ],
   "source": [
    "# Query foodhub_df DataFrame for orders with a cost greater than $20.\n",
    "expensive_orders = foodhub_df[foodhub_df['cost_of_the_order'] > 20]\n",
    "\n",
    "# Calculate the % of expensive orders vs total orders.\n",
    "percentage_expensive_orders = (len(expensive_orders)/len(foodhub_df)) * 100\n",
    "print(f\"The percentage of orders costing more than $20 is {percentage_expensive_orders:.0f}%.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the expensive vs total orders a histogram provides an easier view to consume.\n",
    "sns.histplot(foodhub_df['cost_of_the_order'], bins=30, edgecolor='black')\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the bar chart output.\n",
    "# Added vertical line at $20 for reference.\n",
    "plt.axvline(20, color='red', linestyle='dashed', linewidth=2)\n",
    "plt.xlabel('Cost of Order', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Number of Orders', fontsize=10, fontweight='bold')\n",
    "plt.title('Histogram of Order Costs with $20 Threshold', fontsize=12, fontweight='bold')\n",
    "\n",
    "# Show histogram.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "Orders greater than 20 dollars is 29 percent across all restaurants. The histogram shows that the majority of orders in the 12 dollars to 17 dollars range. To potentially increase orders over 20 dollars incentives could be offered to existing customers with some sort of discount. New customers could have special offerings such as a free item or future order discounts to increase per order sales costs. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HFyHn7hsJY_V"
   },
   "source": [
    "---\n",
    "### **Question 10**: What is the mean order delivery time? [1 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean order delivery time is  24 minutes.\n"
     ]
    },
    {
     "data": {
      "image/png": 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vx//ImzVrJv1+/Phx6fcLFy5I34js7e1Rt25dNG7cWBp/5swZFBUVSc/LfsBW5vHgUHZPRp8+fTBt2jR0795d2mOlTZWth2PHjlU4jaaV/WZvZWUl9ckp+5obNmyAEKLcIysrS+11Nm7cOAAl74u33npL+qf82muvSdM0adJE2naNGzdGYWFhuRry8/OxdOnScsuvLCy6uLigf//+AEr6n23dulV6rbJ7U/Rd2e2zaNGiSrdP6UX06tSpgylTpuCXX37B9evX8fDhQ2kPx++//15hYCyrbHgp7Xfi5+cHT09P2NnZ4cSJE/jrr7+k2ioLm2U96fOhJo0cOVLqb9e5c2d4enpWOm1V1rM+fa4YGl5nhzTu448/xqFDhzBgwAB4eHjA0tIS+/fvl8aX/aZvb2+PGzduACi5QOELL7wAW1tbtG7dGmPHjpW+BQUFBUGpVMLJyUnlwnajRo2CQqFA+/bt4e7ujsTERNy9excTJkzAuHHjsH///nKnHz+L+vXrS78fPHgQW7duhbGxsUpHQ20ZO3asdErqjBkz8OjRIygUCrz33nvSNJrsNHvt2jVER0fj7t272Lp1q0rn3CVLlkjfMMeOHYsvvvgCADBnzhw8ePAAbdq0QVpaGuLj4/H777+jfv36+P7779Wqo2/fvnB0dERqaioOHDgAoOS6PK+++qo0jYODA/r164dff/0V8fHxGDx4MCZPngxra2vcunULZ86cwc8//4zY2NgqXZBu8uTJ2L17N3JycnD69GkA2u+YXF2vvvoq3nvvPeTl5WHFihVQKBTw8/NDdnY2bty4gaioKOTk5EjrtnHjxhg+fDi8vb3h5uaG+/fvS3+bQgjk5eU9saNyx44dYWZmhtzcXGlPoJ+fHxQKBTp16oSIiAgpMD3rXp2yezN+/PFHGBsbw9jYuMavTWRpaYlvvvkGly9fhr+//xOnrcp61qfPFYOjrc5BpN80eer5Rx99VGHHZqDkdNM///xTmn/evHnlpintqFpcXCyd9lnR41lPPW/dunWFtT+pc7QQqp0+Sx+dO3eusENp2fVX9jUq62BZ0XqrTG5urnS6bkWPsqeeC1H9DsoVPRQKhUoH61JPOvX88fdSVTool5o2bZrK8oYNG1Zumiedev7465XtoFzR+7xUQUFBuVOZy54S/Kw00UG57HxVfZ896ZTox9e5sbFxpdP16dPnmdr7+CUSEhIShBBCLFmyRGX4xo0bK11PZesv29m97ONJ606Iqv19Pd5B+UnUOfX88fWsic+VZ30f0//wMBZpXP/+/fHWW2+hVatWsLe3h7GxMRwcHPDyyy9j//796Ny5szTtokWLMGXKFLi5uZU7rKBQKBAaGop169ahY8eOsLS0hFKpRLNmzfDee+/h6NGjKt/8hg0bhm3btqFFixYwNTWFl5cXQkND0bt3b2ma0o7Nz+KHH35AQEAAnJycYGdnh/Hjx2PPnj3VWDPqUSqVOHDgAFasWIE2bdrA3NwcZmZmaN26NYKDg/H7779X2nlSXXXq1IGDgwO8vb3x1ltv4cyZMxUeClq6dCn27t0r7YUxMTHBc889hy5dumDFihXVvr1E2UNWFT0HSi7Wd+bMGbz77rvw9PSEmZkZrK2t4enpiQkTJmD37t1V7uNTp04dlYszent7w8vLS71G6NAbb7yB6OhoDBs2DM7OzqhTpw6cnZ3RsWNHLFy4EF999ZU07fLly9GnTx88//zzUCqV0gUi3333XWzfvv2ZXq/sKejPPfectN4fP0ngWffsDBw4EJ988gkaN26s0k9F31RlPevL54qhUQjB3k0kD0KICvthdOrUSerfcvr06XLX6CCqSHR0tHQGzMqVK1Wu2ExEtQv37JBsxMTEYMyYMdi/fz9u3bqFc+fOYcaMGVLQad68uXQxLqLK5OTkIDk5Wbr4o7GxsU7OwiIizdHf/YJEVVRcXIywsDDpqstlWVtbY+PGjVq/RgrVPv369VO5YvikSZPw/PPP67AiIqoufvKTbDRq1AivvfYaGjduDAsLCyiVSjRp0gTTpk3DuXPn0KlTJ12XSLWIk5MT3nrrLYSEhOi6FCKqJvbZISIiIlnjnh0iIiKSNfbZQUlfj7t378La2lprl+AnIiKi6hFC4NGjR3Bzc3tin0yGHQB3796t8nU4iIiISD8kJiY+8UQChh1AugdKYmJipTfXIyKZ8PQE7t0DXF0BNe/KTkT6ISMjA+7u7tL/8cow7OB/NwS0sbFh2CGSu8WLgcxMwMoK4N87kSw8rQsKww4RGZYpU3RdARFpGc/GIiIiIllj2CEiIiJZ42EsIjIs9+4BRUWAsXFJJ2Uikj3u2SEiw+LjA7i7l/wkIoPAsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLG20UQkewlJCQgJSUFAKBctQqKoiIIY2PknT791HmdnJzg4eFR0yUSUQ1i2CEiWUtISEBzTy/k5mSrNb+ZuQWuXI5j4CGqxXQadqKjo/Hf//4Xp06dwr1797Br1y4MGTJEZZq4uDgsWLAAhw8fRmFhIVq0aIGdO3dKHzy5ubmYN28ewsLCkJeXhz59+uCrr76Cs7OzDlpERPomJSUFuTnZcBw4DyaO7lWatyA1Eal7P0VKSgrDDlEtptOwk5WVBW9vb0yaNAnDhg0rNz4+Ph5dunTB5MmTsWTJEtjY2ODixYswMzOTppkzZw727duH7du3w9bWFjNnzsSwYcPw119/abMpRKTnTBzdoXRpousyiEgHdBp2+vXrh379+lU6/oMPPkD//v3x8ccfS8MaN24s/Z6eno7169cjNDQUvXr1AgBs2LABXl5eOHr0KDp16lThcvPy8pCXlyc9z8jIqG5TiKiWGHzpEMwL8pBjosTuFj10XQ4RaYHeno1VXFyMffv2oVmzZujTpw/q1asHX19fhIeHS9OcOnUKBQUF8Pf3l4Z5enrCw8MDsbGxlS47ODgYtra20sPdvWq7tomo9gqM2oCVEasRGLVB16UQkZbobdi5f/8+MjMzsWLFCvTt2xe///47hg4dimHDhuHw4cMAgKSkJJiamsLOzk5lXmdnZyQlJVW67MDAQKSnp0uPxMTEmmwKERER6ZDeno1VXFwMAHjllVcwZ84cAEDbtm1x5MgRrFu3Dt27d1d72UqlEkqlUiN1EhERkX7T2z07Tk5OqFOnDlq0aKEy3MvLCwkJCQAAFxcX5OfnIy0tTWWa5ORkuLi4aKtUIiIi0mN6G3ZMTU3h4+ODK1euqAy/evUq6tevDwB44YUXYGJigsjISGn8lStXkJCQAD8/P63WS0RERPpJp4exMjMzcf36den5jRs3cPbsWTg4OMDDwwPvvvsuRo0ahW7duqFnz56IiIjAnj17cOjQIQCAra0tJk+ejLlz58LBwQE2NjZ4++234efnV+mZWERERGRYdBp2Tp48iZ49e0rP586dCwAICAjAxo0bMXToUKxbtw7BwcGYNWsWmjdvjp07d6JLly7SPJ9//jmMjIwwfPhwlYsKEhEREQE6Djs9evSAEOKJ00yaNAmTJk2qdLyZmRnWrFmDNWvWaLo8IiIikgG97bNDREREpAl6e+o5EVFN+NfKXuUnEckfww4RGZTBASG6LoGItIyHsYiIiEjWGHaIiIhI1hh2iIiISNbYZ4eIDMryiC9hm/sI6WbWeL/vTF2XQ0RawLBDRAalZ/wJuGam4p6Vo65LISIt4WEsIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1XlSQiAzK7hbdYZubiXQzK12XQkRawrBDRAYluOckXZcgWwkJCUhJSVFrXicnJ3h4eNSq16Xag2GHiIiqLSEhAc09vZCbk63W/GbmFrhyOa7KwUNXr0u1C8MOERFVW0pKCnJzsuE4cB5MHN2rNG9BaiJS936KlJSUKocOXb0u1S4MO0REpDEmju5QujQxmNel2oFhh4gMSuS3U1EvMxX3rRzR+811ui6HiLSAp54TkUGxyM+BdX4OLPJzdF0KEWkJww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRqvoExEBuWDPjNgVpiP3Dqmui6FiLREp3t2oqOjMWjQILi5uUGhUCA8PLzSaadOnQqFQoGQkBCV4Q8ePMC4ceNgY2MDOzs7TJ48GZmZmTVbOBHVWgebdMSvnl1wsElHXZdCRFqi0z07WVlZ8Pb2xqRJkzBs2LBKp9u1axeOHj0KNze3cuPGjRuHe/fu4cCBAygoKMDEiRMxZcoUhIaG1mTpREQ1KiEhASkpKWrN6+TkxLt4E5Wh07DTr18/9OvX74nT3LlzB2+//Tb279+PAQMGqIyLi4tDREQETpw4gQ4dOgAAVq9ejf79++OTTz6pMBwREem7hIQENPf0Qm5Otlrzm5lb4MrlOAYeov9Pr/vsFBcXY/z48Xj33XfRsmXLcuNjY2NhZ2cnBR0A8Pf3h5GREY4dO4ahQ4dWuNy8vDzk5eVJzzMyMjRfPBHppVZJ12FaVIB8YxNccGmi63IqlJKSgtycbDgOnAcTR/cqzVuQmojUvZ8iJSWFYYfo/9PrsLNy5UrUqVMHs2bNqnB8UlIS6tWrpzKsTp06cHBwQFJSUqXLDQ4OxpIlSzRaKxHVDt/u/Aiumam4Z+UIvxmbdF3OE5k4ukOpp4GMqDbR21PPT506hS+++AIbN26EQqHQ6LIDAwORnp4uPRITEzW6fCIiItIfeht2YmJicP/+fXh4eKBOnTqoU6cObt26hXnz5qFBgwYAABcXF9y/f19lvsLCQjx48AAuLi6VLlupVMLGxkblQURERPKkt4exxo8fD39/f5Vhffr0wfjx4zFx4kQAgJ+fH9LS0nDq1Cm88MILAICDBw+iuLgYvr6+Wq+ZiKi2U/cssLi4uBqohkgzdBp2MjMzcf36den5jRs3cPbsWTg4OMDDwwOOjo4q05uYmMDFxQXNmzcHAHh5eaFv37548803sW7dOhQUFGDmzJkYPXo0z8QiIqqi6p4FRqSvdBp2Tp48iZ49e0rP586dCwAICAjAxo0bn2kZW7ZswcyZM9G7d28YGRlh+PDhWLVqVU2US0Qka9U5Cyznn5NIj/mxhiojqh6dhp0ePXpACPHM09+8ebPcMAcHB15AkIhIg9Q5C6wglSd6kP7S2w7KRERERJrAsENERESyxrBDREREsqa3p54TEdUE/zfWQgHg2XsLElFtx7BDRAYlS2mh6xKISMt4GIuIiIhkjWGHiIiIZI2HsYjIoEw+vgvW+dl4ZGqB9R2H6rocqsXUvbUGADg5OcHDw0PDFVFlGHaIyKC8cSIcrpmpuGflyLBDaqvurTXMzC1w5XIcA4+WMOwQERFVUXVurVGQmojUvZ8iJSWFYUdLGHaIiIjUpM6tNUj72EGZiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjaeeE5FBueDSGPeynZBqYavrUohISxh2iMigvDk8SNclEJGW8TAWERERyRrDDhEREckaww4RERHJGvvsEJFB+XbnUjhmpyPVwpb9d4gMBMMOERmUVknxcM1MxT0rR12XQkRawsNYREREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrvKggERmU73yGwDo/G49MLXRdChFpiU737ERHR2PQoEFwc3ODQqFAeHi4NK6goAALFixA69atYWlpCTc3N0yYMAF3795VWcaDBw8wbtw42NjYwM7ODpMnT0ZmZqaWW0JEtcX6jkMR0mUc1nccqutSiEhLdBp2srKy4O3tjTVr1pQbl52djdOnT2PhwoU4ffo0fv75Z1y5cgWDBw9WmW7cuHG4ePEiDhw4gL179yI6OhpTpkzRVhOIiIhIz+n0MFa/fv3Qr1+/CsfZ2triwIEDKsO+/PJLdOzYEQkJCfDw8EBcXBwiIiJw4sQJdOjQAQCwevVq9O/fH5988gnc3NwqXHZeXh7y8vKk5xkZGRpqERHJUVxcnFrzOTk5wcPDQ8PVEFFV1ao+O+np6VAoFLCzswMAxMbGws7OTgo6AODv7w8jIyMcO3YMQ4dWvJs6ODgYS5Ys0UbJRKRnLPOyoQAgAGQpn9xvpyjzIaBQ4LXXXlPrtczMLXDlchwDD5GO1Zqwk5ubiwULFmDMmDGwsbEBACQlJaFevXoq09WpUwcODg5ISkqqdFmBgYGYO3eu9DwjIwPu7u41UzgR6ZU/vpsm3fXcb8amJ05bnJcJCAHHgfNg4li1z4iC1ESk7v0UKSkpDDtEOlYrwk5BQQFGjhwJIQTWrl1b7eUplUoolUoNVEZEhsDE0R1Klya6LoOI1KT3Yac06Ny6dQsHDx6U9uoAgIuLC+7fv68yfWFhIR48eAAXFxdtl0pERER6SK8vKlgadK5du4Y//vgDjo6OKuP9/PyQlpaGU6dOScMOHjyI4uJi+Pr6artcIiIi0kM63bOTmZmJ69evS89v3LiBs2fPwsHBAa6urnj11Vdx+vRp7N27F0VFRVI/HAcHB5iamsLLywt9+/bFm2++iXXr1qGgoAAzZ87E6NGjKz0Ti4iIiAyLTsPOyZMn0bNnT+l5aafhgIAALF68GLt37wYAtG3bVmW+qKgo9OjRAwCwZcsWzJw5E71794aRkRGGDx+OVatWaaV+IiIi0n86DTs9evSAEKLS8U8aV8rBwQGhoaGaLIuIiIhkRK/77BARERFVF8MOERERyRrDDhEREcma3l9nh4hIk94cvhCmRQXINzbRdSlEpCUMO0RkUC7wSshEBoeHsYiIiEjWGHaIiIhI1ngYi4gMSq/rx2FWmI/cOqY42KSjrsshIi1g2CEig/Kf/WvgmpmKe1aO8GPYITIIPIxFREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENERESyxrBDREREssawQ0RERLLGsENEBiXb1ByPTM2RbWqu61KISEt4BWUiMii931yn6xKISMu4Z4eIiIhkjWGHiIiIZI1hh4iIiGSNfXaIyKAERn0P29xMpJtZIbjnJF2XQ0RawD07RGRQBl86jNF//47Blw7ruhQi0hKGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1hh0iIiKSNYYdIiIikjWGHSIiIpI1tcJOfn6+pusgIiIiqhFqhR0XFxdMnz4dsbGx1Xrx6OhoDBo0CG5ublAoFAgPD1cZL4RAUFAQXF1dYW5uDn9/f1y7dk1lmgcPHmDcuHGwsbGBnZ0dJk+ejMzMzGrVRUTyFdXYB/uad0ZUYx9dl0JEWqJW2ElLS8PXX3+NLl26oHnz5li+fDkSEhKqvJysrCx4e3tjzZo1FY7/+OOPsWrVKqxbtw7Hjh2DpaUl+vTpg9zcXGmacePG4eLFizhw4AD27t2L6OhoTJkyRZ1mEZEBeL/vTMwYEoj3+87UdSlEpCVqhZ0vvvgC3bp1g0KhwLVr17Bw4UI0atQIvXr1wqZNm5CVlfVMy+nXrx+WLVuGoUOHlhsnhEBISAg+/PBDvPLKK2jTpg02b96Mu3fvSnuA4uLiEBERge+++w6+vr7o0qULVq9ejbCwMNy9e1edphEREZHMqBV23n77bURFReHevXv4+uuv8dJLL8HY2BiHDx/GpEmT4Orqivnz5yMvL0/twm7cuIGkpCT4+/tLw2xtbeHr6ysdPouNjYWdnR06dOggTePv7w8jIyMcO3as0mXn5eUhIyND5UFERETyVK2zsZycnODu7g4bGxsoFAoIISCEQGZmJj799FNMnjxZ7WUnJSUBAJydnVWGOzs7S+OSkpJQr149lfF16tSBg4ODNE1FgoODYWtrKz3c3d3VrpOIiIj0Wx11ZoqPj8eGDRuwefNm3LlzB0DJYacXXngB06dPR4sWLfDqq69iz549Gi1WUwIDAzF37lzpeUZGBgMPkYHYvWk26mY+xL9W9hgcEKLrcohIC9QKO82aNQNQEnDMzc0xevRoTJs2TeVwUteuXfHTTz+pXZiLiwsAIDk5Ga6urtLw5ORktG3bVprm/v37KvMVFhbiwYMH0vwVUSqVUCqVatdGRLVX3cyHcM1M1XUZRKRFah3GEkKgadOm+Oyzz3Dnzh2sX79eJegAQFBQEA4ePKh2YQ0bNoSLiwsiIyOlYRkZGTh27Bj8/PwAAH5+fkhLS8OpU6ekaQ4ePIji4mL4+vqq/dpEREQkH2rt2fnjjz/Qq1evJ07j6ekJT0/PJ06TmZmJ69evS89v3LiBs2fPwsHBAR4eHpg9ezaWLVuGpk2bomHDhli4cCHc3NwwZMgQAICXlxf69u2LN998E+vWrUNBQQFmzpyJ0aNHw83NTZ2mERERkcyovWdn1apV+Oeff6Rh8fHxWLVqFf74449nXs7JkyfRrl07tGvXDgAwd+5ctGvXDkFBQQCA+fPn4+2338aUKVPg4+ODzMxMREREwMzMTFrGli1b4Onpid69e6N///7o0qULvvnmG3WaRURERDKk1p6defPmIT4+Hm+88YY0zNXVFR988AGaNGmCM2fOPNNyevToASFEpeMVCgWWLl2KpUuXVjqNg4MDQkNDn714IiIiMihq7dm5du0aGjVqBAsLC2mYhYUFGjVqVO52DkRERES6pFbYMTIyQkJCgsqVkjMzM5GQkACFQqGx4oiIiIiqS62w06ZNG2RkZKBv377YunUrtm7div79+yMjIwPe3t6arpGIiIhIbWr12Zk1axZiY2Nx5MgRHDlypNw4IiIiIn2hVtgZNWoUbt++jcWLF0uHsiwtLbFkyRKMHDlSowUSEWlScM+JMC/IQ44JLyxKZCjUCjtAyRlZ06dPx8WLFwEALVu2hLm5ucYKIyKqCbtb9NB1CUSkZWqHHQAwNzcvd+VkIiIiIn2iVgfl+/fvY/z48XBzc4OxsbHKo06dauUnIiIiIo1SK5lMnjwZv/766xMvCEhEpI8apd6GcXERioyM8Y/j87ouh4i0QK2wc/jwYQDA0KFD0aJFC+7NIaJaY0vYB3DNTMU9K0f4zdik63KISAvUSikODg5wc3PDzp07NV0PERERkUap1Wdn/vz5SExMxIULFzRdDxEREZFGqbVnZ/v27SgsLES7du3QunVr2NnZSeMUCgUiIyM1VR8RERFRtVSrzw4AnD17VmUc741FRERE+kStsDNhwgSGGi1ISEhASkqKWvM6OTnBw8NDwxXpN64vIiKqiFphZ+PGjRougx6XkJCA5p5eyM3JVmt+M3MLXLkcZzD/wLm+iIioMtU6ZzwqKgpHjx6Fvb09xo4di7S0NDg7O0Op5D1nqislJQW5OdlwHDgPJo7uVZq3IDURqXs/RUpKisH88+b6IiKiyqgVdnJycjB48GAcPHgQAODr64t69ephxIgRWL58ORYsWKDRIg2ZiaM7lC5NdF1GrcH1RUREj1Mr7Hz44YflzrgaMGAATE1NsW/fPoYdMijsK0REpN/UCjvbtm2Dubk5YmNj0bZtWwCAUqlE/fr1cfXqVU3WR6TX2Feo9hkc8DmMi4tRZKTWZcaIqBZSK+zcv38fLVq0QJs2bVSGm5iYIC0tTRN1yYa63/rj4uJqoBrSNPYVqn3+tXLQdQlEpGVqhR1XV1dcvXoV8fHx0rCzZ88iLo7fUMuq7rd+qj3YV4iISH+pFXZeeeUVrF69Gq1atYJCocCZM2fQsWNHCCHwyiuvaLrGWqs63/pz/jmJ9Jgfa6gyIiIiw6FW2Pnoo48QHR2Nc+fOAQDy8vIAAG3atMGSJUs0V51MqPOtvyA1sYaqITJsY85GwCI/B9mm5tjatq+uyyEiLVAr7NjY2OD48eMIDQ3FiRMnAAA+Pj4YM2YMTE1NNVogEWmeIZ9BNuuvrXDNTMU9K0eGHSIDofZFBU1MTBAQEICAgABN1kNENYxnkBGRoVEr7EyaNKnScQqFAuvXr1e7ICKqWTyDjIgMjdr3xqroRqBCCIYdolqCZ5ARkaFQK+x069ZNJeykp6fj/PnzEEKga9euGiuOiIiIqLrUCjuHDh0qN+zy5ct48cUXMXDgwOrWRAaqOp1meRFGIiKqTLXuel6Wp6cn2rZti9WrV2PevHmaWiwZCF6AkYiIaopaYWfz5s0qz4uKinD16lXExMTAwsJCI4WRYalOp1mAF2EkIqLKqRV2Xn/99Uo7KHfv3r3aRZHhUrfTLC/CSERElVH7MJYQQuV5vXr10Lt3b3z22WfVLqpUUVERFi9ejB9//BFJSUlwc3PD66+/jg8//FAKW0IILFq0CN9++y3S0tLQuXNnrF27Fk2bNtVYHUT6iDeZVc8Nh+fwSGmJFEs7XZdCRFqiVtgpLi7WdB0VWrlyJdauXYtNmzahZcuWOHnyJCZOnAhbW1vMmjULAPDxxx9j1apV2LRpExo2bIiFCxeiT58+uHTpEszMzLRSJ5G2sY+T+saOWa7rEohIyzTWQbkmHDlyBK+88goGDBgAAGjQoAG2bt2K48ePAyjZqxMSEoIPP/xQugHp5s2b4ezsjPDwcIwePbrC5ebl5Un38wKAjIyMGm4JkWbxJrNERM9OrbDTq1evZ5pOoVAgMjJSnZcAALz44ov45ptvcPXqVTRr1gznzp3Dn3/+KR0qu3HjBpKSkuDv7y/NY2trC19fX8TGxlYadoKDg3nDUpIF3mSWiOjp1L7OTkUdlIH/XUW59Gd1vPfee8jIyICnpyeMjY1RVFSE//znPxg3bhwAICkpCQDg7OysMp+zs7M0riKBgYGYO3eu9DwjIwPu7lU/A4iIiIj0n9pXUD516hTy8vLQpk0bAMDff/8NpVKJDh06aKy4bdu2YcuWLQgNDUXLli1x9uxZzJ49G25ubtW6AalSqYRSqdRYnURUe4Ts+S8csjPwwMIGswe9q+tyiEgL1Ao7o0ePxunTp3HhwgU0a9YMAHD16lW88MILGDlyJKZNm6aR4t59912899570uGo1q1b49atWwgODkZAQABcXFwAAMnJyXB1dZXmS05ORtu2bTVSAxHJi2/CBbhmpuKelaOuSyEiLVEr7AQHB+P555+Xgg4ANGvWDO7u7lixYoXGwk52djaMjIxUhhkbG0tngzVs2BAuLi6IjIyUwk1GRgaOHTumsRoMUXVu2+Dk5MS7YRMRkV5RK+ykpKTg9u3b+OCDDzBs2DAAwK5du3D58mWNXkF50KBB+M9//gMPDw+0bNkSZ86cwWeffYZJkyYBKOkAPXv2bCxbtgxNmzaVTj13c3PDkCFDNFaHIanuKc1m5ha4cjmOgYeIiPSGWmFnwIAB2LFjB1asWIEVK1aojNPkjUBXr16NhQsXYvr06bh//z7c3Nzw1ltvISgoSJpm/vz5yMrKwpQpU5CWloYuXbogIiKC19hRU3VOaS5ITUTq3k+RkpLCsEMV4l5DItIFtcLON998g6KiIuzatUtl+LBhw/D1119rpDAAsLa2RkhICEJCQiqdRqFQYOnSpVi6dKnGXpfUv20DUWW415CIdEWtsGNnZ4edO3fin3/+wcWLFwEALVu2RKNGjTRaHBHJB/caEpGuVOsKys7OzkhOToaxsTGDDhE9E+41JCJtM3r6JBVbtmwZnJ2d0aVLF8yePRvbtm1Do0aNEBoaqsn6iIiIiKpFrbCzbt06BAUFITs7W7r7ee/evZGYmIiwsDCNFkhERERUHWodxlq1ahWMjIzw2WefYfbs2QAAR0dHPPfcczh37pwm6yMi0qgw7z6wzsvCI6WlrkshIi1RK+zEx8ejZcuWmDVrlhR2AMDBwQGXLl3SVG1ERBr3RZexui6BiLRMrcNYtra2uHv3LnJzc6VhaWlpuHr1KmxtbTVWHBEREVF1qRV2unfvjgcPHsDX1xdAyZ6ejh07IicnBz179tRogURERETVoVbY+eijj2BtbY3z589DoVAgJSUF169fh42NDRYvXqzhEomIiIjUp1afHU9PT5w8eRL/+c9/cOLECQCAj48PAgMDVW4OSkSkb2LXBEh3PfebsUnX5RCRFlQ57BQUFGDr1q1QKBRYv359ubuSExEREemTKicVExMTvPnmmwgODmbQISIiIr2nVlrx9vZGRkaGpmshIiIi0ji1ws78+fORkpKCCRMm4Pjx47h16xYSEhKkBxEREZG+UKuD8siRI6FQKLBlyxZs2bJFZZxCoUBhYaFGiiMiIiKqLrXvel56TywiIiIifValsLN79244ODggKiqqpuohIiIi0qgqhZ0hQ4bAz88Pf/31FwDA2NgYnTp1kp4TERER6Zsqd1Aue/hKCMHDWURERKTX1O6zQ0RUG80ZNA+mhQXIr2Oi61KISEsYdojIoBz1aKPrEohIy6ocds6cOYNGjRpV+lyhUCA+Pl4z1RERERFVU5XDTn5+Pm7evCk9z8vLU3muUCg0URcRERGRRlQp7HTr1o1hhohqtU4Jf0t9dnhIi8gwVCnsHDp0qIbKICLSjs/3fArXzFTcs3KE34xNui6HiLSAty0nIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWWPYISIiIllj2CEiIiJZY9ghIiIiWdP7sHPnzh289tprcHR0hLm5OVq3bo2TJ09K44UQCAoKgqurK8zNzeHv749r167psGIiIiLSJ3oddh4+fIjOnTvDxMQEv/32Gy5duoRPP/0U9vb20jQff/wxVq1ahXXr1uHYsWOwtLREnz59kJubq8PKiYiISF/o9V3PV65cCXd3d2zYsEEa1rBhQ+l3IQRCQkLw4Ycf4pVXXgEAbN68Gc7OzggPD8fo0aO1XjMR6TdeNZnI8Oj1np3du3ejQ4cOGDFiBOrVq4d27drh22+/lcbfuHEDSUlJ8Pf3l4bZ2trC19cXsbGxlS43Ly8PGRkZKg8iIiKSJ70OO//88w/Wrl2Lpk2bYv/+/Zg2bRpmzZqFTZtKvpklJSUBAJydnVXmc3Z2lsZVJDg4GLa2ttLD3d295hpBREREOqXXh7GKi4vRoUMHLF++HADQrl07XLhwAevWrUNAQIDayw0MDMTcuXOl5xkZGQw8REQkewkJCUhJSVFrXicnJ3h4eGi4Iu3Q67Dj6uqKFi1aqAzz8vLCzp07AQAuLi4AgOTkZLi6ukrTJCcno23btpUuV6lUQqlUar5gItJ77/wZCuu8LDxSWuKLLmN1XQ6R1iQkJKC5pxdyc7LVmt/M3AJXLsfVysCj12Gnc+fOuHLlisqwq1evon79+gBKOiu7uLggMjJSCjcZGRk4duwYpk2bpu1yiagWGH1uP1wzU3HPypFhhwxKSkoKcnOy4ThwHkwcq3Y0oyA1Eal7P0VKSgrDjqbNmTMHL774IpYvX46RI0fi+PHj+Oabb/DNN98AABQKBWbPno1ly5ahadOmaNiwIRYuXAg3NzcMGTJEt8UTERHpIRNHdyhdmui6DK3S67Dj4+ODXbt2ITAwEEuXLkXDhg0REhKCcePGSdPMnz8fWVlZmDJlCtLS0tClSxdERETAzMxMh5UTERGRvtDrsAMAAwcOxMCBAysdr1AosHTpUixdulSLVREREVFtodennhMRERFVF8MOERERyRrDDhEREckaww4RERHJGsMOERERyZren41FRKRJxzxawSE7Aw8sbHRdChFpCcMOERmU2YPe1XUJRKRlPIxFREREssawQ0RERLLGsENERESyxj47RGRQQre+D6esNKRY2mHsmOW6LoeItIBhh4gMSsMHd+CamQrrvCxdl0JEWsLDWERERCRrDDtEREQkaww7REREJGsMO0RERCRr7KBMREREzyQuLk6t+ZycnODh4aHhap4dww4RERE9UVHmQ0ChwGuvvabW/GbmFrhyOU5ngYdhh4iIiJ6oOC8TEAKOA+fBxNG9SvMWpCYide+nSElJYdghIiIi/Wbi6A6lSxNdl1FlDDtEZFBWdR4Di/wcZJua67oUItIShh0iMihb2/bVdQlEpGU89ZyIiIhkjWGHiIiIZI2HsYjIoNTNfADj4mIUGRnhXysHXZdDRFrAsENEBmX3pjlwzUzFPStH+M3YpOtyiEgLeBiLiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZI1hh4iIiGSNYYeIiIhkjWGHiIiIZK1WXWdnxYoVCAwMxDvvvIOQkBAAQG5uLubNm4ewsDDk5eWhT58++Oqrr+Ds7KzbYomIiJ4gLi5OrfmcnJzg4eGh4WrkrdaEnRMnTuDrr79GmzZtVIbPmTMH+/btw/bt22Fra4uZM2di2LBh+Ouvv3RUKRERUeWKMh8CCgVee+01teY3M7fAlctxDDxVUCvCTmZmJsaNG4dvv/0Wy5Ytk4anp6dj/fr1CA0NRa9evQAAGzZsgJeXF44ePYpOnTpVuLy8vDzk5eVJzzMyMmq2AUSkN8aN/g+Mi4tQZGSs61LIQBXnZQJCwHHgPJg4uldp3oLURKTu/RQpKSkMO1VQK8LOjBkzMGDAAPj7+6uEnVOnTqGgoAD+/v7SME9PT3h4eCA2NrbSsBMcHIwlS5bUeN1EpH/+cXxe1yUQAQBMHN2hdGmi6zIMgt53UA4LC8Pp06cRHBxcblxSUhJMTU1hZ2enMtzZ2RlJSUmVLjMwMBDp6enSIzExUdNlExERkZ7Q6z07iYmJeOedd3DgwAGYmZlpbLlKpRJKpVJjyyMiIiL9pddh59SpU7h//z7at28vDSsqKkJ0dDS+/PJL7N+/H/n5+UhLS1PZu5OcnAwXFxcdVExE+m7wpUMwL8hDjokSu1v00HU5RKQFeh12evfujfPnz6sMmzhxIjw9PbFgwQK4u7vDxMQEkZGRGD58OADgypUrSEhIgJ+fny5KJiI9Fxi1Aa6Zqbhn5ciwQ2Qg9DrsWFtbo1WrVirDLC0t4ejoKA2fPHky5s6dCwcHB9jY2ODtt9+Gn59fpZ2TiYiIyLDoddh5Fp9//jmMjIwwfPhwlYsKEhEREQG1MOwcOnRI5bmZmRnWrFmDNWvW6KYgIiIi0mt6f+o5ERERUXUw7BAREZGsMewQERGRrDHsEBERkawx7BAREZGs1bqzsYiIquNfK3uVn0Qkfww7RGRQBgeE6LoEItIyHsYiIiIiWWPYISIiIllj2CEiIiJZY58dIjIoyyO+hG3uI6SbWeP9vjN1XQ4RaQHDDhEZlJ7xJ+CamYp7Vo66LoWItISHsYiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1nhRQSIyKLtbdIdtbibSzax0XQoRaQnDDhEZlOCek3RdAhFpGQ9jERERkawx7BAREZGsMewQERGRrLHPDhEZlMhvp6JeZiruWzmi95vrdF0OEWkB9+wQkUGxyM+BdX4OLPJzdF0KEWkJww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyZreh53g4GD4+PjA2toa9erVw5AhQ3DlyhWVaXJzczFjxgw4OjrCysoKw4cPR3Jyso4qJiIiIn2i92Hn8OHDmDFjBo4ePYoDBw6goKAAL7/8MrKysqRp5syZgz179mD79u04fPgw7t69i2HDhumwaiIiItIXen9RwYiICJXnGzduRL169XDq1Cl069YN6enpWL9+PUJDQ9GrVy8AwIYNG+Dl5YWjR4+iU6dOuiibiIiI9ITeh53HpaenAwAcHBwAAKdOnUJBQQH8/f2laTw9PeHh4YHY2NgKw05eXh7y8vKk5xkZGTVcNRHpiw/6zIBZYT5y65jquhQi0pJaFXaKi4sxe/ZsdO7cGa1atQIAJCUlwdTUFHZ2dirTOjs7IykpqcLlBAcHY8mSJTVdLhHpoYNNOuq6BCLSMr3vs1PWjBkzcOHCBYSFhVVrOYGBgUhPT5ceiYmJGqqQiIiI9E2t2bMzc+ZM7N27F9HR0Xj++eel4S4uLsjPz0daWprK3p3k5GS4uLhUuCylUgmlUlnTJRMREZEe0Ps9O0IIzJw5E7t27cLBgwfRsGFDlfEvvPACTExMEBkZKQ27cuUKEhIS4Ofnp+1yiUjPtUq6jvZ34tAq6bquSyEiLdH7PTszZsxAaGgofvnlF1hbW0v9cGxtbWFubg5bW1tMnjwZc+fOhYODA2xsbPD222/Dz8+PZ2IRUTnf7vwIrpmpuGflCL8Zm3RdDhFpgd6HnbVr1wIAevTooTJ8w4YNeP311wEAn3/+OYyMjDB8+HDk5eWhT58++Oqrr7RcKREREekjvQ87QoinTmNmZoY1a9ZgzZo1WqiIiIiIahO977NDREREVB0MO0RERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7REREJGt6f50dIiJN8n9jLRQAnn4FLyKSC4YdIjIoWUoLXZdARFrGw1hEREQkaww7REREJGs8jEVEBmXy8V2wzs/GI1MLrO84VNflEJEWMOwQkUF540Q4XDNTcc/KkWGHyEDwMBYRERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRovKkhEBuWCS2Pcy3ZCqoWtrkshIi1h2CEig/Lm8CBdl0BEWsbDWERERCRrDDtEREQkaww7REREJGvss0NEBuXbnUvhmJ2OVAtb9t8hMhAMO0RkUFolxcM1MxX3rBx1XQoRaQkPYxEREZGsMewQERGRrDHsEBERkawx7BAREZGsySbsrFmzBg0aNICZmRl8fX1x/PhxXZdEREREekAWYeenn37C3LlzsWjRIpw+fRre3t7o06cP7t+/r+vSiIiISMdkEXY+++wzvPnmm5g4cSJatGiBdevWwcLCAt9//72uSyMiIiIdq/XX2cnPz8epU6cQGBgoDTMyMoK/vz9iY2MrnCcvLw95eXnS8/T0dABARkaGRmvLzMwseb2k6yjOz63SvAWpierP++A2AODUqVNSDc/qypUrte51Aa6vKr2uga+rjKICWALIKCpATsKFJ7+uga+rKr0u19Wzv25tXFcaqDkzM1Pj/2dLlyeEePKEopa7c+eOACCOHDmiMvzdd98VHTt2rHCeRYsWCQB88MEHH3zwwYcMHomJiU/MCrV+z446AgMDMXfuXOl5cXExHjx4AEdHRygUCo29TkZGBtzd3ZGYmAgbGxuNLbc2YNsNr+2G2m7AcNtuqO0G2HZ9absQAo8ePYKbm9sTp6v1YcfJyQnGxsZITk5WGZ6cnAwXF5cK51EqlVAqlSrD7OzsaqpE2NjY6PwNoStsu+G13VDbDRhu2w213QDbrg9tt7W1feo0tb6DsqmpKV544QVERkZKw4qLixEZGQk/Pz8dVkZERET6oNbv2QGAuXPnIiAgAB06dEDHjh0REhKCrKwsTJw4UdelERERkY7JIuyMGjUK//77L4KCgpCUlIS2bdsiIiICzs7OOq1LqVRi0aJF5Q6ZGQK23fDabqjtBgy37YbaboBtr21tVwjxtPO1iIiIiGqvWt9nh4iIiOhJGHaIiIhI1hh2iIiISNYYdoiIiEjWGHaIiIhI1hh2qoEnslFxcbGuS9CajIwMg2pvZQxpHXCblzC0z/r8/Hxdl6BxDDvV8PiHgKH8Qdy5c8dg2vq4P//8E2vXrsXOnTuRlJQEIyMjg/hnsHfvXixYsADZ2dm6LkXruM0Nb5ufOHECP/30E44ePYqHDx9CoVAYxDYHgD179uDDDz9EYWGhrkvRKIYdNf3xxx+YNm0a/vvf/2Lbtm0AAIVCIfsQsG/fPgwePBj379/XdSla98svv2Dy5Mk4f/48fv75Z7z44ouIi4uT/T+/AwcOYOHChRg6dCisrKxUxsm53QC3uSFu8127dmHs2LH4+eef8cknn2D48OG4ceOG7Lc5ULLdg4KC0Lt3b9SpI4trDv/PE++JThU6cOCAcHV1FV9++aVYsGCB6NGjh5gxY4Y0vri4WIfV1Zxff/1VdOnSRfz2228Vjpdru4UQIj8/X4waNUocPHhQCFHS1g8++EBYWVmJS5cuCSGEKCoq0mWJNeLgwYPCxMREnDlzRgghRFJSkjh06JCIjIwUycnJQgh5tlsIbnND3OaPHj0SAwYMEMePHxdCCHHz5k0xb9484enpKW7cuCGEkO/nXGRkpLCyshJxcXFCCCHu378vTp8+Lc6fPy/S0tJ0XF31ySy61TwhBGJjY7F48WJMmTIFOTk5+OeffxAQEICZM2fiyy+/hEKh0HWZGnf16lUMGDAAYWFh6Nu3LxISEvDrr7+iTp06qFevHgYPHizLdpeVnJyMCxcuoGfPnlAoFFi2bBkUCgWGDBmC6Ohond+epCY899xzKCwsxKlTp9C4cWMMHToUzz33HLKzs/Hw4UPs2LEDbm5uui6zxnCbG9Y2NzY2xr///otr167Bx8cH9evXx/Lly6FQKDB16lSEhYXBzs5O12XWiOLiYuTn5yM+Ph5NmzbF8OHDYWdnh/z8fDg5OWHVqlVwcHDQdZnq03Xaqo2Cg4NFz549xaNHj6Rh58+fF7179xbh4eE6rKzmFBYWitdff134+PiIEydOiB49eohZs2aJ1157TQwaNEj88MMPui6xxoWHh4uBAweKmJgYIUTJN7zc3FwxatQosX//fh1Xp3mFhYVCiJL3tomJiTA3Nxfr168XQgiRnJwsRowYIcLCwnRZYo3jNje8bf7111+LadOmiQsXLgghSrb51atXxSuvvCL+/vtvHVdXsyIiIoSNjY2wt7cX3377rRBCiOPHj4tXXnlFHD58WMfVVQ/77Khh+PDhaNCgAX766Sfk5OQAABo2bAhPT0/cuXNHx9XVDGNjY2zYsAFt2rRBx44dMWzYMHzxxRf48ssv4enpibt37+q6xBrXtm1beHt7Y+PGjYiJiYFCoYBSqYRCocC9e/d0XZ7GGRsbo6ioCK1atcK5c+cQFBSESZMmAQDq1asHS0tL2fdR4zY3vG3erl075OXlYdu2bbh48SIUCgWaNm2KwsJC3Lx5U9fl1ag+ffpg27ZteP/99/HGG28AAHx8fGBiYlLrO6rzMJYaGjVqhJYtWyImJgZCCIwePRpWVlZwcHDA9evXpQ8DOR7W+frrrzFixAj06dMHAGBrawsLCwukp6cDKDnMJ6d2FxcXw8io5DtB/fr18eqrr2Lnzp1YtmwZ/P394eDggHPnzmHZsmU6rlSzSrejsbExCgsL4eXlBS8vL2n8jh07cPbsWQQFBemwyppnCNs8Pz8fpqamBr3Ny35u+fj4IC0tDdu2bcNnn32Gzp07w8zMDPHx8WjTpo2OK9W8xz+z+/Tpg5dffll6vn37dly/fh0tWrTQRXmao7N9SrXE453RSp/n5+eLzz77TEycOFF06tRJLF68WLi5uUmdu2q7hw8fCiEq7ohYdp1s3rxZeHt7i8uXL2urtBqXnp5erkNe2fVw+/Zt8dtvv4mhQ4eKyZMni7Nnz2q7xBqRmZkpMjMzpecVbfuioiKxYcMG4eXlJe3ml4Nz5849sT1y3eZ79uwRU6ZMeeI0ct3m2dnZIj8/X2VY2ff8mTNnxMaNG4W/v78YMWKE1GFbDnJzc6VDlkJU/re+ceNG0bJlS1lsd4adJzhy5Ij47bffygWe0jdGYWGhSExMFKtXrxYhISHSGRq13Z49e4Sjo6O4cuVKpdPk5OSIXbt2ye4DMDw8XPj6+go/Pz8xffp08ffff0vbv6CgQGXagoIC2ZyV8ttvv4mXXnpJ9OrVS8yZM6fS6QoKCsTOnTtlE+qFKDnLsGPHjuX+fouLi8ttXzlt899//100adJEWFhYiF9++aXS6eS4zffs2SN69uwphg4dKt577z2RlZUljXv87zw3N1fk5uZqu8Qas3//fjF06FAxduxYsWLFikqnS09PF1999ZVs/q8x7FQiIiJCNG/eXJw4caLcuOLiYtmefhgRESHatWsn/Pz8xH//+98KP/BL/f333+LWrVtarrDmnDhxQjRv3lz8+eefIj4+XgwfPlxMmjRJREREqGzvixcvitTUVB1Wqln79u0TrVu3Frt37xbHjx8X9erVkzqllnXs2DHp1GO5iIiIEN7e3iI6OloIUf4bbul2l9s2j4iIEO3btxcHDhwQ//3vf8XSpUuFEOXbL8dtHhMTI5o0aSL27NkjYmNjRdu2bcUbb7xRLszdunVLJQTJwd69e0WbNm3Ejz/+KLZv3y68vLzE3r17y0138eJFkZmZKav/cww7Ffj1119F27ZtpetrlL7hS3fxl+7+O3bsmKy+7ezevVv4+PiI6OhoERoaKrp3717hdH/++af466+/tFucFkRFRYkJEyZIzzMyMsTs2bPFpEmTpMN0cXFx4v/+7/9ESkqKrsrUqDt37oiePXuKAwcOSMM++ugj8cknn6hMl5iYKBYuXCju3r2r7RJrzK1bt0SjRo3EwoULhRAl15N59913xbRp00RQUJA0nZy2eXFxsbh06ZJo2LChFPC2bdsmbG1txdWrV1WmvX37tuy2uRBC/Pzzz2Lu3LnS84cPH0qHJ/Py8oQQQpw6dUpMnz5dZGRk6KpMjbt586Z44YUXpP9rBQUF4o033hCbN29Wme7y5cti3rx5sni/l8WwU0ZxcbHIy8sTHTp0EH379hVClHwgjho1SkyYMEH4+flJh2wePnwoZs6cKZsPgvT0dNG7d2/xxx9/CCFK/hBatWolVq5cqTJdVlaWWL58ubh9+7YuyqxRZ86cEe3atZP6KwlREngGDRokZs2aJQ178OCBDqqrOUeOHBF37tyRvtV/+umnYuTIkeWmK3upBbkICQkRgwcPFuvWrRPt27cXH374odi8ebN47rnnxP/93/9J08ltm//7779CiP/tyXnzzTfF4sWLRVFRkcq3eTlu899//120aNFCZVhaWppo3bq1SsiV2x4tIYSIjo4W9+/fl7Z7YGCgymdbKTntxSzFs7HKUCgUMDU1xbZt2/Dqq68iICAA165dw8iRI9G5c2fs3r0bI0aMwF9//QV7e3t8+umnMDU11XXZGmFjY4Ndu3bB2tpaOjtj2rRp+PvvvwH8775fFhYWePfdd2VzKfGoqCjExsaioKAA8+fPR+fOndG5c2dcvHgRAGBtbY2VK1finXfeQWZmJqysrGBvb6/jqqsvKioKf/75J4QQmDp1KurVqyddCt/Ozg4mJiYAgPXr1yM7Oxtvv/12udsG1FalbTcyMsI777wDS0tLLFq0CB9++CGmTZsGAGjQoAFCQkJQUFAAExMT2WzzmJgYFBcX48033wQAFBUVwcjICG3atMGOHTuwaNEiabixsbFstvmBAwcQFRWFrKwsLF68GB06dMCAAQOwe/duGBsbw9bWFiEhIdi8ebPU9nr16um6bI04cOAAIiMjkZWVhfnz56Nu3brS57mlpaV0+ZQNGzbA2NgYEyZMqN0XD6wEr7Pz/x0/fhxxcXEoLi5Gw4YNsWPHDpw4cQKDBw/G7Nmz4ePjg7fffhtt2rSR/hHIIegcO3YMcXFxAEr+sQP/a9eLL76IsLAw7Nu3DwqFQjo9US5BZ9++fZg7dy6EEIiLi0P//v2xYsUKNG7cGK1bt0ZycjIA4OTJk8jPz5ddu42MjHD16lUMGjQI6enp0in2jo6OaN26NQ4cOIAvv/wSPXr00G3BGlS27RcuXED//v0xadIk7Ny5Uwo6AHD27FmYmprK5jIKpe02NjZGfHw8hg0bhvT0dOmzbObMmXj06BE++ugjACXX25GL/fv3Y/78+WjcuDESEhIwb948LFu2DPb29ujbty+KiooAAJcuXUJKSoqs7n9V2vYmTZogKSkJgYGBAP53WRQbGxt4eXkhMjISq1atQvv27XVZbs3S7Y4l/fDrr78KhUIh2rdvL+Li4qRdfGlpaSqnJv7www+iR48eKoc5arOy7X781PHSXdkrV64UEyZMkNWxayFKjsk3b95cnDp1SgghxN27d8XLL78s/vnnHyGEEKNHjxYvvviiGDZsmGjVqpVsTjWuqN39+vVT2f4///yzMDY2Fr6+vrI6066itr/00kvi2rVrKtP98MMPwsfHR1y8eFEXZWrc07Z5aR/E4OBgMWPGDFmdefTXX38JT09P6QrYYWFhYsaMGWLXrl3i1KlT4q233hItW7YUEydOFJ6enrK6QnJFbZ8+fboIDw+XTixZs2aNcHJyEp06dZLV33pFDD7sZGVliZEjR4odO3aIGTNmiM6dO1d4qt2aNWtEhw4dZPOGqKjdFXW2/vHHH8VLL72kcv0VObh+/boICQlRGdalSxeVS+FfvHhRHDlyRNy8eVPb5dWYitrdtWtXsX37ding7ty5UygUinIhoLZ7UttLxcTEiIYNG8rm71yIZ2u3EEJcuHBBVmdXClHS5zIqKkoIIcS9e/dE06ZNxciRI8W0adOEu7u7SElJEX/99ZfYv3+/uH79um6L1bCK2j5q1CgxdepU4eTkJG7cuCHWr18vy7/1ihh82BGi5IyUnJwcIYQQEyZMEJ07d1b5Vnf79m0xcOBAce7cOV2VWCOe1u5SSUlJ2i5NK0rPsiu9rsaoUaNEZGSkEKJkr1dpJ065eVK7IyIiRFFRkWw63j/uSW3ft2+fePjwoSy3+5PavXv3bnH//n2d1aYtmzdvVrmkwtixY8X8+fN1WJH2PN728ePHi/nz54vs7GxZfZl7EvbZAeDm5galUgkA2LRpExo1aoQpU6YgPT0da9euRWhoKHbs2CG7S4U/rd3BwcEAIJuOeo+zsLAAAJW+SHXr1kV4eDjeeecdpKWl6bC6mvOkdk+bNg03b96Eq6urLkusMU9q+6xZs/DgwQM4OTnpssQa8aR2z5s3DxkZGbosTyvGjx+PSZMmSZ1zmzVrhrp16+q4Ku14vO2NGzeGk5MTzM3NUb9+fR1Xpx3y6HGpAQqFQroP0ubNm/H222+jWbNmUCqVCA8Pl0KB3Dyt3aXTyFlpZ0xLS0vMmzcPDx48QHh4OJo0aaLjympWRe3eu3cvGjVqpOPKal5Fbd+9e7fs217Ze71x48Y6rkx7FAoFtm/fjj179mDLli26LkerDLntDDtlGBkZSf/4O3XqhLCwMERERNT+G6A9haG2u5T4/zfCS0xMxJkzZxAbGyv7oAMYbrsBw227oba7VF5eHjZs2IAvvvgCO3fuRPPmzXVdktYYctsBQCFK92uRJC0tDZMmTcLixYtld+jqSQy13aX++OMPODs7o3Xr1rouRasMtd2A4bbdUNtdXFyMI0eOwNnZGU2bNtV1OVplyG0HGHYqlZeXJ9tDV09iqO0mIiL5YtghIiIiWePZWERERCRrDDtEREQkaww7REREJGsMO0RERCRrDDtEREQkaww7RFTjGjRoAIVCgcWLFwMADh06BIVCAYVCgZs3b+q0Nk3q0aMHFAoFXn/9dV2XQkRlMOwQkYrSf9gKhQLGxsawtrZG8+bNMXHiRJw+fVojr2FjYwNfX1/4+vrq/XWdNm7cKK2PJz0AoEWLFvD19TWo2y8Q1Qa8XQQRVcjU1BTt2rXD7du3ce3aNVy9ehU//vgj1q5dizfeeKNay27fvj2OHj2qoUqfTX5+PkxNTas8X926deHr6ys9P3bsGADAycmpXKj56quvqlckEdUI7tkhogq5urri6NGjuH37No4fP4769eujsLAQ06ZNw+XLl6XpLl++jBEjRqBu3bowNTWFl5cX1q5d+8RlP34YKywsDAqFAiYmJkhNTZWmW7hwIRQKBZ577jkUFRUBAH777Td0794d1tbWMDc3R9euXREVFSXNc/PmTWnZ3333HXr37g0zMzO89957sLKykoaXOn/+vDR9RQFswIABOHr0qPR40vDHD2OVreXjjz/G4MGDYWFhgTZt2uD48eM4cuQI2rZtCysrK/Tt2xd3795Vee0ff/wRPj4+sLCwgLW1Nfr27YuzZ88+ZcsR0eMYdojoqTp06IAvvvgCAFBYWIj169cDAK5du4ZOnTphx44dKC4uRvPmzXHlyhVMnz4dS5cufeblDxkyBLa2tigsLMSOHTuk4T/99BMAYPz48TA2NsZPP/2EAQMGIDo6Go6OjnB1dcWff/6Jl156SSXwlJoxYwbOnTuHxo0bw97eHmPHjgUAfP/999I0O3fuBAA0a9YMnTp1quKaeXZBQUG4cOECjIyMcP78eQwbNgz9+vVDTk4O8vPzsX//fsybN0+a/uOPP8b48eNx8uRJuLu7w8bGBvv370eXLl0QFxdXY3USyRHDDhE9k65du0q/X7p0CQCwfPlypKeno1WrVkhMTMT58+fx+eefAwBWrFiBR48ePdOyzczMMHLkSAD/CzhnzpzBtWvXAAABAQEAgPfeew9CCEyaNAk3btxAfHw8hg4diqKiIgQFBZVbrp+fH27fvo2LFy/i/fffx7Rp0wAAsbGx0t6p0rAzYcKEqq2QKurevTvi4+MREhICALhz5w5GjhyJK1euYMGCBQAgBbbs7GwsWbIEALBkyRJcuXIFt27dQocOHZCVlYXly5fXaK1EcsOwQ0TPpLi4uNyw48ePAwAuXLgAS0tLKBQKzJ49GwCQk5ODv//++5mXXxpoDh8+jKSkJCn0dOzYEV5eXvj333+lM7e+//57GBkZwcjICLt27QLwv740ZU2dOhVmZmYAAGNjY7Rr107qf/P999/j6tWruHDhAhQKBcaPH//MtapjwIABUCgUaNCggTRs0KBBAIBGjRoBAJKTkwEAFy9eRHZ2NgBg0aJF0iG+kydPAoDW+zsR1XbsoExEzyQmJkb6vUWLFirjKuqsC5QEjGfVuXNnNGnSBNevX8e2bduwbds2AP8LQWU1atQIdevWLTc8Pz9f5bmzs3O5aaZPn45jx47hhx9+gLW1NQCgZ8+e8PDweOZa1WFjYwMAqFOnTrlhpWdzVcTLy0uarpSjo2MNVEgkXww7RPRUJ0+exJw5cwCUBJiJEycCAHx8fHDp0iXY2tri119/hYODAwAgJSUFkZGRVe4DM2HCBAQFBSE4OBhJSUlQKpUYM2YMgJKzourXr49bt26hffv22Lp1qxQcrl69ilu3bpU726qiEDFy5EjMmTMHSUlJWLlypfS6+qRly5YwNzdHTk4O+vbti08//VRqy5kzZ5CTk6PjColqFx7GIqIK3bt3D506dYK7uzs6duyIW7duoU6dOli3bp20ZycwMBA2NjaIj4+Hu7s72rVrh/r168PFxUXqh1IV48ePh0KhQFJSEoCSwzz29vbS+NK+Kjt27ICbmxvatWsHFxcXNG/eHFu2bHmm1zAzM5PCWlZWFiwtLTF8+PAq11qTLCwssHDhQgDA559/jueffx5t27aFo6Mj2rdvj99//13HFRLVLgw7RFSh/Px8HD9+HGlpaWjSpAkCAgJw7NgxlWvsNG/eHLGxsRgxYgQsLCxw8eJFFBcXo2/fvvjoo4+q/JoNGjRA9+7dpeePX4l47Nix2Lt3L7p3746cnBxcuXIF1tbWmDBhQpWu/TN16lRpT8mwYcNgZWVV5VprWmBgIDZt2gQfHx88fPgQ169fR7169TB16lQMGzZM1+UR1SoKIYTQdRFERNqUl5cHZ2dnpKenIzIyEr169dJ1SURUg9hnh4gMymuvvYaLFy8iPT0dL7zwAoMOkQHgnh0iMiilp3H7+Phg06ZNaNKkia5LIqIaxj07RGRQ+P2OyPCwgzIRERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERyRrDDhEREckaww4RERHJGsMOERERydr/A36/obvRyJ5OAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Query the foodhub_df DataFrame for orders mean delivery time.\n",
    "mean_delivery_time = foodhub_df['delivery_time'].mean()\n",
    "\n",
    "# Print the mean order delivery time and round it to the nearest minute. \n",
    "print(f'The mean order delivery time is {mean_delivery_time: .0f} minutes.')\n",
    "\n",
    "# Visualize the delivery times using a histogram to show the distribution of delivery times.\n",
    "delivery_times = foodhub_df['delivery_time']\n",
    "\n",
    "# Calculate the mean delivery time.\n",
    "mean_delivery_time = delivery_times.mean()\n",
    "\n",
    "# Create a histogram of the delivery times.\n",
    "plt.hist(delivery_times, bins=30, edgecolor='black')\n",
    "\n",
    "# Add a vertical line to indicate the mean delivery time.\n",
    "plt.axvline(mean_delivery_time, color='red', linestyle='dashed', linewidth=2)\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the bar chart output.\n",
    "plt.xlabel('Delivery Time', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Frequency', fontsize=10, fontweight='bold')\n",
    "plt.title('Histogram of Delivery Times with Mean', fontsize=12, fontweight='bold')\n",
    "plt.xticks(rotation=45, fontsize=8, horizontalalignment='right')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F35SRV-HJY_V"
   },
   "source": [
    "#### Observations:\n",
    "Query for the delivery time using the \"delivery_time\" column shows the average time is 24 minutes. To visualize all of the different delivery times a histogram provides a view of the dataset with 24 minutes marked with a dashed line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "48IjaOuDJY_V"
   },
   "source": [
    "---\n",
    "### **Question 11:** The company has decided to give 20% discount vouchers to the top 3 most frequent customers. Find the IDs of these customers and the number of orders they placed. [1 mark]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 customers:\n",
      " Customer ID  Number of Orders\n",
      "       52832                13\n",
      "       47440                10\n",
      "       83287                 9\n"
     ]
    }
   ],
   "source": [
    "# Select the top 3 customers with the highest number of orders using the value_counts() method\n",
    "# and the nlargest() method to retrieve the top 3 customers.\n",
    "top_customers = foodhub_df['customer_id'].value_counts().nlargest(3)\n",
    "\n",
    "# Convert the series to a DataFrame named top_customers_df and rename the columns for readability.\n",
    "top_customers_df = top_customers.reset_index()\n",
    "top_customers_df.columns = ['Customer ID', 'Number of Orders']\n",
    "\n",
    "# Output the top_customers_df DataFrame as a table.\n",
    "print('Top 3 customers:')\n",
    "print(top_customers_df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "Hz8-DE4Zge0j"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To visualize the top 3 customers with more details on types of orders\n",
    "# create  that contains only the orders from the top 3 customers\n",
    "top_customers_orders = foodhub_df[foodhub_df['customer_id'].isin(top_customers.index)]\n",
    "\n",
    "# Create a crosstab that counts the number of orders for each cuisine type for each customer.\n",
    "cuisine_counts = pd.crosstab(top_customers_orders['customer_id'], top_customers_orders['cuisine_type'])\n",
    "\n",
    "# Create a grouped bar chart of the top 3 customers and cuisine types.\n",
    "cuisine_bar_counts = cuisine_counts.plot(kind='bar', stacked=False, figsize=(10, 6), edgecolor='black')\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the bar chart output.\n",
    "plt.xlabel('Customer ID', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Number of Orders', fontsize=10, fontweight='bold')\n",
    "plt.title('Number of Orders for Each Cuisine Type for the Top 3 Customers', fontsize=12, fontweight='bold')\n",
    "plt.xticks(rotation=45, fontsize=8, horizontalalignment='right')\n",
    "\n",
    "# Add values on top of each bar for readability.\n",
    "# The 0s represent no orders for that cuisine type.\n",
    "for cuisine_type in cuisine_bar_counts.patches:\n",
    "    cuisine_bar_counts.annotate(str(cuisine_type.get_height()), (cuisine_type.get_x() * 1.005, cuisine_type.get_height() * 1.005))\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar plot that counts the number of orders for each top customer.\n",
    "sns.countplot(x='customer_id', data=top_customers_orders)\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the bar chart output.\n",
    "plt.xlabel('Customer ID', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Number of Orders', fontsize=10, fontweight='bold')\n",
    "plt.title('Number of Orders for the Top 3 Customers', fontsize=12, fontweight='bold')\n",
    "plt.xticks(rotation=45, fontsize=8, horizontalalignment='right')\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9tWVwfX6JY_V"
   },
   "source": [
    "#### Observations:\n",
    "The top customer, ID 52832, orders a variety of cuisine across restaurants. Customer, ID 832287, has a bit of variety but customer, ID 47449 only orders American. There are opportunities based on this information to potentially incentivize certains customers to try new cuisines with special offers, example customer 47440 could receive coupons to extend their cuisine experiences. Other customers that order more of a variety could still receive special offers but have them be focused on their most favorite and highest rated restaurant. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AfIc2jPaJY_V"
   },
   "source": [
    "---\n",
    "## Multivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iqiS0RT_JY_V"
   },
   "source": [
    "---\n",
    "### **Question 12**: Perform a multivariate analysis to explore relationships between the important variables in the dataset. (It is a good idea to explore relations between numerical variables as well as relations between numerical and categorical variables) [10 marks]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cuisine vs Cost of the Order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine the relationship between cost of the order and cuisine type.\n",
    "plt.figure(figsize=(15,7))\n",
    "\n",
    "# Create a boxplot to show the relationship between cost of the order and cuisine type.\n",
    "sns.boxplot(x = 'cuisine_type', y = 'cost_of_the_order', data = foodhub_df, palette = 'PuBu', hue = 'cuisine_type')\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the bar chart output.\n",
    "plt.xlabel('Cuisine Type', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Cost of the Orders', fontsize=10, fontweight='bold')\n",
    "plt.title('Relationship of Order Cost & Cuisine', fontsize=12, fontweight='bold')\n",
    "plt.xticks(rotation=45, fontsize=8, horizontalalignment='right')\n",
    "plt.xticks(rotation = 45)\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obversations:\n",
    "The cuisine types Korean, Mediterranean and Vietnamese have outliers which could indicate out of the ordinary order costs because of large or small orders or potentially incorrectly entered data. There are also long whiskers for the more popular cuisines that represents a high variability in the data. \n",
    "\n",
    "The median for the majority of cuisine types is closer to the bottom (aka left) that represents a positively skewed (right skewed) distribution. This shows that much of the order costs are in the lower range, approximately 15 dollars to 17 dollars. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cuisine vs Food Preparation time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine the relationship between cost of the order and cuisine type.\n",
    "plt.figure(figsize=(15,7))\n",
    "\n",
    "# Create a boxplot to show the relationship between cost of the order and cuisine type.\n",
    "sns.boxplot(x = 'cuisine_type', y = 'food_preparation_time', data = foodhub_df, palette = 'YlOrBr_r')\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the bar chart output.\n",
    "plt.xlabel('Cuisine Type', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Food Preparation Time', fontsize=10, fontweight='bold')\n",
    "plt.title('Relationship of Cuisine & Food Preparation Time', fontsize=12, fontweight='bold')\n",
    "plt.xticks(rotation=45, fontsize=8, horizontalalignment='right')\n",
    "plt.xticks(rotation = 45)\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obversations:\n",
    "Food preparation times tend to be high across the board with each restaurant. The company should investigate why food preparation time is because of large orders or lack of resources in the restaurants. There are cuisines that take less time to prepared but from analyzing other data relationship have lower net revenues. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Day of the Week vs Delivery time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine the relationship between day of the week and delivery time.\n",
    "plt.figure(figsize=(15,7))\n",
    "\n",
    "# Create a boxplot to show the relationship between day of the week and delivery time.\n",
    "sns.boxplot(x = 'day_of_the_week', y = 'delivery_time', data = foodhub_df, palette = 'RdPu')\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the bar chart output.\n",
    "plt.xlabel('Day of the Week', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Delivery Time', fontsize=10, fontweight='bold')\n",
    "plt.title('Relationship of Day of the Week & Delivery Time', fontsize=12, fontweight='bold')\n",
    "plt.xticks(rotation=45, fontsize=8, horizontalalignment='right')\n",
    "plt.xticks(rotation = 45)\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "Delivery times have a large variability on the weekdays when it is most important to deliver to customers during a work week. Weekend delivery times are lower most likely because of the additional delivery resources that are used. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run the below code and write your observations on the revenue generated by the restaurants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "restaurant_name\n",
       "Shake Shack                              3579.53\n",
       "The Meatball Shop                        2145.21\n",
       "Blue Ribbon Sushi                        1903.95\n",
       "Blue Ribbon Fried Chicken                1662.29\n",
       "Parm                                     1112.76\n",
       "RedFarm Broadway                          965.13\n",
       "RedFarm Hudson                            921.21\n",
       "TAO                                       834.50\n",
       "Han Dynasty                               755.29\n",
       "Blue Ribbon Sushi Bar & Grill             666.62\n",
       "Rubirosa                                  660.45\n",
       "Sushi of Gari 46                          640.87\n",
       "Nobu Next Door                            623.67\n",
       "Five Guys Burgers and Fries               506.47\n",
       "Momoya                                    492.13\n",
       "Chipotle Mexican Grill $1.99 Delivery     491.69\n",
       "Blue Ribbon Sushi Izakaya                 485.09\n",
       "Tamarind TriBeCa                          426.71\n",
       "Jack's Wife Freda                         416.75\n",
       "Bareburger                                399.74\n",
       "Sushi of Gari Tribeca                     375.79\n",
       "ilili Restaurant                          343.22\n",
       "Cafe Mogador                              329.72\n",
       "The Kati Roll Company                     302.14\n",
       "Westville Hudson                          292.59\n",
       "Hill Country Fried Chicken                292.39\n",
       "Sushi of Gari                             291.18\n",
       "P.J. Clarke's                             280.95\n",
       "The Smile                                 262.28\n",
       "Yama Japanese Restaurant                  256.59\n",
       "Cafe Habana                               217.31\n",
       "Empanada Mama (closed)                    213.31\n",
       "Otto Enoteca Pizzeria                     211.39\n",
       "Vanessa's Dumplings                       205.93\n",
       "J. G. Melon                               191.66\n",
       "Balthazar Boulangerie                     187.01\n",
       "Sushi Samba                               186.59\n",
       "S'MAC                                     182.58\n",
       "Melt Shop                                 177.38\n",
       "Osteria Morini                            174.24\n",
       "Boqueria                                  169.67\n",
       "Burger Joint                              154.71\n",
       "Vanessa's Dumpling House                  127.85\n",
       "Sarabeth's Restaurant                     127.82\n",
       "Cafeteria                                 125.10\n",
       "L'Express                                 124.07\n",
       "Xi'an Famous Foods                        123.27\n",
       "CafÌ© China                               119.37\n",
       "Pepe Rosso To Go                          116.96\n",
       "Junoon                                    115.25\n",
       "Delicatessen                              113.93\n",
       "Hatsuhana                                 112.78\n",
       "Sarabeth's East                           112.64\n",
       "Waverly Diner                             106.47\n",
       "Benihana                                  105.44\n",
       "Yama 49                                   101.61\n",
       "Dos Caminos                                95.22\n",
       "Pylos                                      94.25\n",
       "12 Chairs                                  88.57\n",
       "Serafina Fabulous Pizza                    87.70\n",
       "Mission Cantina                            87.45\n",
       "Sarabeth's                                 87.40\n",
       "Cho Dang Gol                               85.19\n",
       "Mamoun's Falafel                           82.77\n",
       "5 Napkin Burger                            79.84\n",
       "Lantern Thai Kitchen                       78.29\n",
       "Grand Sichuan International                78.09\n",
       "The Loop                                   77.32\n",
       "Amma                                       76.69\n",
       "Tony's Di Napoli                           76.10\n",
       "Donburi-ya                                 75.53\n",
       "Barbounia                                  74.95\n",
       "Pinto Nouveau Thai Bistro                  72.42\n",
       "Kanoyama                                   71.06\n",
       "Carmine's                                  70.96\n",
       "Joe's Shanghai _Àü£¾÷´                   70.37\n",
       "Saravanaa Bhavan                           70.19\n",
       "Vezzo Thin Crust Pizza                     68.74\n",
       "Go! Go! Curry!                             67.23\n",
       "Blue Ribbon Brooklyn                       66.12\n",
       "DuMont Burger                              65.86\n",
       "Asuka Sushi                                65.00\n",
       "Lucky's Famous Burgers                     63.83\n",
       "Tres Carnes                                62.62\n",
       "Bhatti Indian Grill                        62.23\n",
       "Dickson's Farmstand Meats                  60.93\n",
       "Bubby's                                    59.18\n",
       "Dirty Bird to Go                           55.25\n",
       "Hummus Place                               54.95\n",
       "Tortaria                                   54.28\n",
       "Song Thai Restaurant & Bar                 53.40\n",
       "da Umberto                                 52.86\n",
       "V-Nam Cafe                                 52.48\n",
       "Schnipper's Quality Kitchen                51.42\n",
       "indikitch                                  51.17\n",
       "Byblos Restaurant                          49.47\n",
       "Ravagh Persian Grill                       47.06\n",
       "Bistango                                   46.08\n",
       "Le Grainne Cafe                            45.21\n",
       "UVA Wine Bar & Restaurant                  43.80\n",
       "Friend of a Farmer                         43.65\n",
       "Olea                                       43.12\n",
       "Terakawa Ramen                             42.79\n",
       "Hot Kitchen                                42.54\n",
       "Room Service                               41.23\n",
       "Tarallucci e Vino Restaurant               40.69\n",
       "Prosperity Dumpling                        39.83\n",
       "Don's Bogam BBQ & Wine Bar                 38.91\n",
       "Five Leaves                                37.80\n",
       "Hangawi                                    37.49\n",
       "El Parador Cafe                            37.39\n",
       "Dirty Bird To Go (archived)                36.24\n",
       "Amy Ruth's                                 35.72\n",
       "Piccolo Angolo                             35.26\n",
       "Haveli Indian Restaurant                   34.82\n",
       "The Odeon                                  33.51\n",
       "Pongsri Thai                               33.23\n",
       "DespaÌ±a                                   33.04\n",
       "Kambi Ramen House                          32.93\n",
       "Emporio                                    31.43\n",
       "Izakaya Ten                                31.30\n",
       "Haru Gramercy Park                         29.83\n",
       "Lucky Strike                               29.25\n",
       "Il Bambino                                 29.25\n",
       "Rohm Thai                                  29.10\n",
       "Klong                                      29.05\n",
       "67 Burger                                  29.05\n",
       "Aurora                                     28.67\n",
       "Big Daddy's                                26.92\n",
       "Socarrat Paella Bar                        25.22\n",
       "Mira Sushi                                 24.84\n",
       "Posto                                      24.25\n",
       "Philippe Chow                              24.20\n",
       "Bukhara Grill                              23.72\n",
       "Olive Garden                               22.84\n",
       "Xe May Sandwich Shop                       22.03\n",
       "brgr                                       19.79\n",
       "Haandi                                     18.77\n",
       "Zero Otto Nove                             18.05\n",
       "Anjappar Chettinad                         16.44\n",
       "Chote Nawab                                16.15\n",
       "Cafe de La Esquina                         16.06\n",
       "Galli Restaurant                           16.06\n",
       "Paul & Jimmy's                             16.05\n",
       "Taro Sushi                                 16.05\n",
       "Spice Thai                                 15.86\n",
       "Crema Restaurante                          15.86\n",
       "Dig Inn Seasonal Market                    15.71\n",
       "Nha Trang One                              15.67\n",
       "Le Zie 2000 Trattoria                      15.47\n",
       "Moonstruck on Second                       14.94\n",
       "Pepe Giallo                                14.60\n",
       "Wo Hop Restaurant                          14.16\n",
       "Sarabeth's West                            14.12\n",
       "Hibino                                     14.02\n",
       "Sushi Choshi                               13.73\n",
       "Coppola's East                             13.05\n",
       "Hiroko's Place                             13.05\n",
       "Dos Caminos Soho                           12.66\n",
       "Alidoro                                    12.61\n",
       "Hampton Chutney Co.                        12.37\n",
       "Kori Restaurant and Bar                    12.18\n",
       "The MasalaWala                             12.13\n",
       "Rye House                                  12.13\n",
       "Frank Restaurant                           12.08\n",
       "Samurai Mama                               11.64\n",
       "Lamarca Pasta                               9.22\n",
       "Balade                                      9.22\n",
       "Gaia Italian Cafe                           8.78\n",
       "Chola Eclectic Indian Cuisine               8.73\n",
       "Woorijip                                    8.25\n",
       "'wichcraft                                  8.10\n",
       "La Follia                                   8.05\n",
       "Market Table                                6.79\n",
       "Wa Jeal                                     6.74\n",
       "Cipriani Le Specialita                      5.92\n",
       "Big Wong Restaurant _¤¾Ñ¼                  5.92\n",
       "Hunan Manor                                 5.72\n",
       "Name: cost_of_the_order, dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum the revenue generated by each restaurant and display the top 20 restaurants.\n",
    "\n",
    "# Display all rows in the DataFrame.\n",
    "pd.set_option('display.max_rows', None)\n",
    "foodhub_df.groupby(['restaurant_name'])['cost_of_the_order'].sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obversations:\n",
    "The company should consider reviewing the lower performing revenue restaurants as they could be costing more money for delivery times and the resources who deliver to customers. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rating vs Delivery time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine the relationship between day of the week and delivery time.\n",
    "plt.figure(figsize=(15,7))\n",
    "\n",
    "# Create a boxplot to show the relationship between day of the week and delivery time.\n",
    "sns.pointplot(x = 'rating', y = 'delivery_time', data = foodhub_df)\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the bar chart output.\n",
    "plt.xlabel('Rating', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Delivery Time', fontsize=10, fontweight='bold')\n",
    "plt.title('Relationship of Ratings & Delivery Times', fontsize=12, fontweight='bold')\n",
    "plt.xticks(rotation=45, fontsize=8, horizontalalignment='right')\n",
    "plt.xticks(rotation = 45)\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obversations:\n",
    "Delivery times between 'Not given' and a top rating of 5 do not vary much. The rating of 3 has the highest variability with delivery time which makes sense. The ratings of 4 have a lower delivery time which does not make sense and could have problems with the dataset. The rating of 'Not given' need to have some time of incentive to collect that information and see what it could be influencing. A recommendation to widen the spread of rating numbers is recommended to understand if there are possibly lower than 3 to help understand what restaurants need to make improvements. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rating vs Food preparation time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine the relationship between day of the week and delivery time.\n",
    "plt.figure(figsize=(15,7))\n",
    "\n",
    "# Create a boxplot to show the relationship between rating and preparation time.\n",
    "sns.boxplot(x='rating', y='food_preparation_time', data=foodhub_df)\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the bar chart output.\n",
    "plt.xlabel('Rating', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Food Preparation Time', fontsize=10, fontweight='bold')\n",
    "plt.title('Relationship of Ratings & Food Preparation Times', fontsize=12, fontweight='bold')\n",
    "plt.xticks(rotation=45, fontsize=8, horizontalalignment='right')\n",
    "plt.xticks(rotation = 45)\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obversations:\n",
    "The relationship of food preparation time and rating has very small variablility shown by the whiskers. The whiskers are short and suggests that the data values are relatively concentrated and have low variability. Delivery time is the most important indicator to the customer to get their order. A rating by a customer is based on food preparation time plus (+) delivery time. This plot shows that food preparation is good but delivery times are making an impact. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Rating vs Cost of the order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine the relationship between rating and cost of the order.\n",
    "plt.figure(figsize=(15,7))\n",
    "\n",
    "# Create a boxplot to show the relationship between rating and preparation time.\n",
    "sns.boxplot(x='cuisine_type', y='cost_of_the_order', data=foodhub_df)\n",
    "\n",
    "# Set the labels, title, position, fontsize and alignment for the bar chart output.\n",
    "plt.xlabel('Rating', fontsize=10, fontweight='bold')\n",
    "plt.ylabel('Cost of the Order', fontsize=10, fontweight='bold')\n",
    "plt.title('Relationship of Ratings & Order Costs', fontsize=12, fontweight='bold')\n",
    "plt.xticks(rotation=45, fontsize=8, horizontalalignment='right')\n",
    "plt.xticks(rotation = 45)\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obversations:\n",
    "There is a high variability with the order cost and rating. The bigger question to ask is rating affected by the cost of the order or the delivery time. Customers could be rating lower or not rating because of a high food cost and not getting the order on time or quickly. Without more details regarding ratings such as a comment that are easily selectable to collect. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation among variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a list of columns to include in the correlation matrix.\n",
    "col_list = ['cost_of_the_order', 'food_preparation_time', 'delivery_time']\n",
    "\n",
    "# Create a new DataFrame with the correlation matrix\n",
    "corr_df = foodhub_df[col_list].corr()\n",
    "\n",
    "# Rename the columns\n",
    "corr_df.columns = ['Order Cost', 'Preparation Time', 'Delivery Time']\n",
    "corr_df.index = ['Order Cost', 'Preparation Time', 'Delivery Time']\n",
    "\n",
    "plt.figure(figsize=(15, 7))\n",
    "\n",
    "# Add a title to the heatmap\n",
    "plt.title('Correlation Heatmap of Order Cost, Food Preparation Time, and Delivery Time', fontsize=14, fontweight='bold')\n",
    "\n",
    "# Create a heatmap with a custom color map and add annotations to the heatmap.\n",
    "sns.heatmap(corr_df, annot=True, vmin=-1, vmax=1, fmt='.2f', cmap='plasma', linewidths=1, linecolor='black')\n",
    "\n",
    "# Rotate the y-axis labels (otherwise they can be hard to read)\n",
    "plt.yticks(rotation=0)\n",
    "\n",
    "# Show the plot.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Obversations:\n",
    "As observed previously the cost of the order, preparation time (food preparation) and delivery times are all strongly correlated. Overall the preparation time and delivery time are having the largest affect on ratings by customers. These are the most important business processes that need to be investigated in the business. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YLkMf09YJY_V"
   },
   "source": [
    "---\n",
    "### **Question 13:** The company wants to provide a promotional offer in the advertisement of the restaurants. The condition to get the offer is that the restaurants must have a rating count of more than 50 and the average rating should be greater than 4. Find the restaurants fulfilling the criteria to get the promotional offer. [3 marks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>restaurant_name</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Blue Ribbon Fried Chicken</td>\n",
       "      <td>64</td>\n",
       "      <td>4.328125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Blue Ribbon Sushi</td>\n",
       "      <td>73</td>\n",
       "      <td>4.219178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>Shake Shack</td>\n",
       "      <td>133</td>\n",
       "      <td>4.278195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>The Meatball Shop</td>\n",
       "      <td>84</td>\n",
       "      <td>4.511905</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               restaurant_name  count      mean\n",
       "16   Blue Ribbon Fried Chicken     64  4.328125\n",
       "17           Blue Ribbon Sushi     73  4.219178\n",
       "117                Shake Shack    133  4.278195\n",
       "132          The Meatball Shop     84  4.511905"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the restaurants that are not rated (Note given) and create a new DataFrame, restaurants_not_rated_df.\n",
    "restaurants_not_rated_df = foodhub_df[foodhub_df['rating'] != 'Not given'].copy()\n",
    "\n",
    "# The rating columns is an object type and should be converted to a type int.\n",
    "restaurants_not_rated_df['rating'] = restaurants_not_rated_df['rating'].astype('int')\n",
    "\n",
    "# Create a new DataFrame, ratings_count_df, that groups the restaurants by name and calculates the count and mean of ratings.\n",
    "ratings_count_df = restaurants_not_rated_df.groupby(['restaurant_name'])['rating'].agg(['count', 'mean']).reset_index()\n",
    "\n",
    "# Filter the restaurants with a rating count of more than 50 and a mean rating greater than 4.\n",
    "restaurants_promo_df = ratings_count_df[(ratings_count_df['count'] > 50) & (ratings_count_df['mean'] > 4)]\n",
    "\n",
    "# Output the restaurants sorted by the mean rating of 4 in descending order.\n",
    "restaurants_promo_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "10uryUMhJY_V"
   },
   "source": [
    "#### Observations:\n",
    "There are 4 restaurants that qualify for a promotional offering. Looking back at Question 5 there are 736 restaurants that have a rating of 'Not given'. As part of the recommendations offering an incentive to customers to provide a rating could influence a greater number of restaurants that could be eligible for the promotions offering. Having more restaurants in the promotional offering will drive more net revenue. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1gvWBkRUJY_V"
   },
   "source": [
    "---\n",
    "### **Question 14:** The company charges the restaurant 25% on the orders having cost greater than 20 dollars and 15% on the orders having cost greater than 5 dollars. Find the net revenue generated by the company across all orders. [3 marks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of orders costing more than $20 is 555 which generates a net revenue of $14,754.91.\n",
      "The number of orders costing less than $5 is 1334 which generates a net revenue of $16,517.17.\n",
      "Total revenue is $6,166.30.\n"
     ]
    }
   ],
   "source": [
    "# Count the number of orders greater than $20.\n",
    "num_orders_over_20 = foodhub_df[foodhub_df['cost_of_the_order'] > 20]['cost_of_the_order'].count()\n",
    "\n",
    "# Calculate the total revenue of orders greater than $20\n",
    "total_revenue_over_20 = foodhub_df[foodhub_df['cost_of_the_order'] > 20]['cost_of_the_order'].sum()\n",
    "\n",
    "# Print the order count and calculate the net revenue using total count and the 25% surcharge.\n",
    "print(f'The number of orders costing more than $20 is {num_orders_over_20} which generates a net revenue of ${total_revenue_over_20:,.2f}.')\n",
    "\n",
    "# Count the number of orders greater than $5 and less than $20.\n",
    "num_orders_between_5_and_20 = foodhub_df[(foodhub_df['cost_of_the_order'] >= 5) & (foodhub_df['cost_of_the_order'] < 20)]['cost_of_the_order'].count()\n",
    "\n",
    "# Calculate the total revenue of orders greater than $5 and less than $20.\n",
    "total_revenue_between_5_and_20 = foodhub_df[(foodhub_df['cost_of_the_order'] >= 5) & (foodhub_df['cost_of_the_order'] < 20)]['cost_of_the_order'].sum()\n",
    "\n",
    "# Print the order count and calculate the net revenue using total count and the 15% surcharge.\n",
    "print(f'The number of orders costing less than $5 is {num_orders_between_5_and_20} which generates a net revenue of ${total_revenue_between_5_and_20:,.2f}.')\n",
    "\n",
    "# Calculate the total revenue using the number of orders over $20 and $5.\n",
    "print(f'Total revenue is ${total_revenue_over_20 * .25 + total_revenue_between_5_and_20 * .15:,.2f}.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total revenue is $6,342.53.\n"
     ]
    }
   ],
   "source": [
    "# Example of revenue difference if reversed as mentioned in the observation.\n",
    "# Calculate the total revenue using the number of orders over $20 and $5.\n",
    "print(f'Total revenue is ${total_revenue_over_20 * .15 + total_revenue_between_5_and_20 * .25:,.2f}.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Observations:\n",
    "The numner of orders costing more than more than 20 dollars is 555 which generates a net revenue of 14,754.91 dollars.\n",
    "The number of orders costing less than 5 dollars is 1,334 which generates a net revenue of 16,517.17 dollars.\n",
    "Total revenue is 6,166.30 dollars.\n",
    "\n",
    "A recommendation to make is to reverse the surcharges. Lesser cost orders should be surcharged larger amounts to offset the costs. Example: a 5 dollars order costs just as much to delivery and prepare as a 20 dollar order. If reversed an additional 176.23 dollars would be netted. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pRlVW-oeJY_W"
   },
   "source": [
    "---\n",
    "### **Question 15:** The company wants to analyze the total time required to deliver the food. What percentage of orders take more than 60 minutes to get delivered from the time the order is placed? (The food has to be prepared and then delivered.) [2 marks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "zxoTrxfLJY_W"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of orders that take over 60 minutes to delivery is 200 and accounts for  10.54% of the total orders.\n",
      "If we include orders that take exactly 60 minutes to get delivered, the total number of orders is 245 and accounts for  12.91% of the total orders.\n"
     ]
    }
   ],
   "source": [
    "# Calculate the number of orders that take more than 60 minutes to get \n",
    "# delivered (food preparation time + delivery time).\n",
    "orders_over_60_min = foodhub_df[foodhub_df['delivery_time'] + foodhub_df['food_preparation_time']> 60].shape[0]\n",
    "\n",
    "# Include orders that take exactly 60 minutes to get delivered (food preparation time + delivery time).\n",
    "orders_60_min_and_over = foodhub_df[foodhub_df['delivery_time'] + foodhub_df['food_preparation_time'] >= 60].shape[0]\n",
    "\n",
    "# Calculate the total number of orders in the dataset.\n",
    "total_orders = foodhub_df.shape[0]\n",
    "\n",
    "# Calculate the percentage of orders that take more than 60 minutes to get delivered.\n",
    "percentage_over_60_min = (orders_over_60_min / total_orders) * 100\n",
    "percentange_60_min_and_over = (orders_60_min_and_over / total_orders) * 100\n",
    "\n",
    "# Print output.\n",
    "print(f'The total number of orders that take over 60 minutes to delivery is {orders_over_60_min} and accounts for {percentage_over_60_min: .2f}% of the total orders.')\n",
    "print(f'If we include orders that take exactly 60 minutes to get delivered, the total number of orders is {orders_60_min_and_over} and accounts for {percentange_60_min_and_over: .2f}% of the total orders.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "iqzBXn2yJY_W"
   },
   "source": [
    "#### Observations:\n",
    "The total number of orders that takes over 60 minutes to delivery is 200 and accounts for  10.54% of the total orders. If we include orders that take exactly 60 minutes to get delivered, the total number of orders is 245 and accounts for  12.91% of the total orders.\n",
    "\n",
    "Understanding these data points can help drive delivery guarantees for customers. This information could also influence any incentives for customers if they exceed the 60 minute guarantee. The guarantee could be set to a guaranteed lower time limit. Setting to a lower time limit may attract more customers that need food faster. Those incentives could include coupons, order cost reductions (% off).\n",
    "\n",
    "The dataset can be re-analyzed on a recurring basis to potentially indicate deficiencies within a restaurant's processes that need to be addressed.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bUd6cYpPJY_W"
   },
   "source": [
    "---\n",
    "### **Question 16:** The company wants to analyze the delivery time of the orders on weekdays and weekends. How does the mean delivery time vary during weekdays and weekends? [2 marks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean delivery time on weekdays is  28.34%\n",
      "Mean delivery time on weekends is  22.47%\n",
      "The difference between delivery time on weekdays vs weekends is  5.87%\n"
     ]
    }
   ],
   "source": [
    "# The dataset only uses the 'day_of_the_week' set to a weekday or weekend.\n",
    "# For better tracking of actual days of the week a function was created to categorize the days of the week.\n",
    "# Recommendation:\n",
    "#       The data should be cleaned-up to represent actual days of the week for better analysis.\n",
    "#       This should be done to understand impact of delivery times on actual days of the week.\n",
    "#       Example: Orders could be \"pre-ordered\" on a Monday but set to deliver on a Wednesday. \n",
    "\n",
    "mean_delivery_time_weekday = foodhub_df[foodhub_df['day_of_the_week'] == 'Weekday']['delivery_time'].mean()\n",
    "print(f'Mean delivery time on weekdays is {mean_delivery_time_weekday: .2f}%')\n",
    "mean_delivery_time_weekend = foodhub_df[foodhub_df['day_of_the_week'] == 'Weekend']['delivery_time'].mean()\n",
    "print(f'Mean delivery time on weekends is {mean_delivery_time_weekend: .2f}%')\n",
    "\n",
    "# Calculate the difference\n",
    "difference = mean_delivery_time_weekday - mean_delivery_time_weekend\n",
    "\n",
    "print(f'The difference between delivery time on weekdays vs weekends is {difference: .2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8H-ODxbVJY_W"
   },
   "source": [
    "#### Observations:\n",
    "The average delivery time on the weekends is less than weekdays by 6%. This could indicate that there are differences in staffing of the restaurants to prepare food quicker and/or there are more delivery drivers available. Deliveries overall are important but during the weekdays delivery times should be quicker to those that are working at businesses. The differences between delivery times should be investigated further looking at staffing and delivery support."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZdX2Wz4FJY_W"
   },
   "source": [
    "### **Question 17:** What are your conclusions from the analysis? What recommendations would you like to share to help improve the business? (You can use cuisine type and feedback ratings to drive your business recommendations.) [6 marks]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PWnsI0i_guWJ"
   },
   "source": [
    "### Conclusions & Recommendations:\n",
    "\n",
    "Overall the dataset provides a good level of detail to perform analysis and determine where there are potential issues. The primary elements to further analyze in the business are related to food preparation time, delivery time and ratings. Food preparation and delivery time should always be considered in combination. If food preparation is fast but delivery slow that affects ratings. Ratings details are lacking to help make more informed decisions on what to change in the company or the participating restaurants. Those details should be expanded on with additional data collection points like comments and simple to select reasons for satisfaction or not. \n",
    "\n",
    "Individual participating restaurants should also be evaluated on their membership based on the number and cost of orders. There are restaurants with lower orders that could be causing the company to lose net revenue. \n",
    "\n",
    "Recommendations:\n",
    "*  Offer incentives to ensure ratings are collected. Additionally collect remarks with ratings as a better indicator of issues.\n",
    "*  Offer promotions to drive new customer growth\n",
    "*  Collect additional specific data for days of the week and weekends.\n",
    "*  Highlight low performing restaurants to encourage growth across all participating restaurants.\n",
    "*  Investigate inefficiences in food preparation time.\n",
    "*  Investigate delivery time issues.\n",
    "*  Consider changing surcharges for lesser cost orders.\n",
    "*  Offer walk-in pickups to remove the cost of delivery services.\n",
    "*  Offer pre-ordering to target days and potentially increase delivery services to meet demand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tSoucKKbg9Hc"
   },
   "source": [
    "---"
   ]
  }
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