EDA_Hotel_Booking_Analysis_Almabetter.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pvanand07/EDA-Hotel-Booking-Python/blob/master/EDA_Hotel_Booking_Analysis_Almabetter.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vncDsAP0Gaoa"
      },
      "source": [
        "# **Project Name**    -    **Hotel Booking Analysis** \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "beRrZCGUAJYm"
      },
      "source": [
        "##### **Project Type**    - EDA\n",
        "##### **Contribution**    - Individual\n",
        "##### **Submitted By**    - Anand Siva P V  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FJNUwmbgGyua"
      },
      "source": [
        "# **Project Summary -**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F6v_1wHtG2nS"
      },
      "source": [
        "This project aims to conduct an exploratory data analysis (EDA) on a dataset of hotel bookings to gain insights on booking patterns and customer behaviour. The analysis will focus on dentifying patterns in bookings and cancellations, customer demographics and preferences, and the effectiveness of different distribution channels.\\\n",
        "The dataset used for this project is available [here](https://drive.google.com/file/d/1C9AxF9fcVzMw0Bgs0NaRrNML2WwX1Ehm/view).\\\n",
        " [Built using jupyter notebook and Python 3]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w6K7xa23Elo4"
      },
      "source": [
        "# **GitHub Link -**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h1o69JH3Eqqn"
      },
      "source": [
        "https://github.com/pvanand07/EDA-Hotel-Booking-Python/blob/master/EDA_Hotel_Booking_Analysis_Almabetter.ipynb"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Additional Links -**\n",
        "- Presentation - Pdf -[GDrive](https://drive.google.com/file/d/1NymbdlvcuNTNnU9pO66w6w_Ol_qsdlTl/view?usp=share_link)\n",
        "- Presentation Video - [GDrive](https://drive.google.com/file/d/1-PS0_KguiOEjkEJy3OUQVNgZ3PRmAxJH/view?usp=share_link) - (Mirror)[Youtube](https://youtu.be/8Ob-kAVLnNw)\n",
        "- [Github Repository](https://github.com/pvanand07/EDA-Hotel-Booking-Python.git)\n",
        "- [Linkedin](http://www.linkedin.com/in/anand-siva-b81990113/)"
      ],
      "metadata": {
        "id": "pflJQ4LMt6t5"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yQaldy8SH6Dl"
      },
      "source": [
        "# **Problem Statement**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DpeJGUA3kjGy"
      },
      "source": [
        "Have you ever wondered when the best time of year to book a hotel room is? Or the optimal length of stay in order to get the best daily rate? What if you wanted to predict whether or not a hotel was likely to receive a disproportionately high number of special requests? This hotel booking dataset can help you explore those questions! This data set contains booking information for a city hotel and a resort hotel, and includes information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things. All personally identifying information has been removed from the data. Explore and analyse the data to discover important factors that govern the bookings."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PH-0ReGfmX4f"
      },
      "source": [
        "#### **Define Your Business Objective?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PhDvGCAqmjP1"
      },
      "source": [
        " The business objectives that this project tries to achieve are \n",
        "- To gain a comprehensive understanding of booking patterns and customer behavior \n",
        "- Understand which amenities and services are most popular among guests\n",
        "- Determine key drivers of occupancy, revenue, and customer satisfaction\n",
        "- Form strategies for increasing occupancy, revenue and customer satisfaction."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_i_v8NEhb9l"
      },
      "source": [
        "# ***Let's Begin !***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HhfV-JJviCcP"
      },
      "source": [
        "## ***1. Know Your Data***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3lxredqlCYt"
      },
      "source": [
        "### Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "M8Vqi-pPk-HR"
      },
      "outputs": [],
      "source": [
        "# Import Libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy.stats import zscore"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3RnN4peoiCZX"
      },
      "source": [
        "### Dataset Loading"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "4CkvbW_SlZ_R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e469f48-0982-4d30-f82b-8e5a943441de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "# Try to load dataset if it is on local directory\n",
        "try:\n",
        "    dataset = pd.read_csv(\"Hotel Bookings.csv\")\n",
        "# Else mount and load from google drive - Only for google colab\n",
        "except:\n",
        "    from google.colab import drive\n",
        "    drive.mount('/content/drive')\n",
        "    dataset = pd.read_csv('/content/drive/MyDrive/Almabetter Projects/Hotel Booking/Hotel Bookings.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x71ZqKXriCWQ"
      },
      "source": [
        "### Dataset First View"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "LWNFOSvLl09H",
        "colab": {
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          "height": 1000
        },
        "outputId": "036df960-6c4c-49be-d6b8-ef8d084e9129"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           0             1             2  \\\n",
              "hotel                           Resort Hotel  Resort Hotel  Resort Hotel   \n",
              "is_canceled                                0             0             0   \n",
              "lead_time                                342           737             7   \n",
              "arrival_date_year                       2015          2015          2015   \n",
              "arrival_date_month                      July          July          July   \n",
              "arrival_date_week_number                  27            27            27   \n",
              "arrival_date_day_of_month                  1             1             1   \n",
              "stays_in_weekend_nights                    0             0             0   \n",
              "stays_in_week_nights                       0             0             1   \n",
              "adults                                     2             2             1   \n",
              "children                                 0.0           0.0           0.0   \n",
              "babies                                     0             0             0   \n",
              "meal                                      BB            BB            BB   \n",
              "country                                  PRT           PRT           GBR   \n",
              "market_segment                        Direct        Direct        Direct   \n",
              "distribution_channel                  Direct        Direct        Direct   \n",
              "is_repeated_guest                          0             0             0   \n",
              "previous_cancellations                     0             0             0   \n",
              "previous_bookings_not_canceled             0             0             0   \n",
              "reserved_room_type                         C             C             A   \n",
              "assigned_room_type                         C             C             C   \n",
              "booking_changes                            3             4             0   \n",
              "deposit_type                      No Deposit    No Deposit    No Deposit   \n",
              "agent                                    NaN           NaN           NaN   \n",
              "company                                  NaN           NaN           NaN   \n",
              "days_in_waiting_list                       0             0             0   \n",
              "customer_type                      Transient     Transient     Transient   \n",
              "adr                                      0.0           0.0          75.0   \n",
              "required_car_parking_spaces                0             0             0   \n",
              "total_of_special_requests                  0             0             0   \n",
              "reservation_status                 Check-Out     Check-Out     Check-Out   \n",
              "reservation_status_date           2015-07-01    2015-07-01    2015-07-02   \n",
              "\n",
              "                                           3  \n",
              "hotel                           Resort Hotel  \n",
              "is_canceled                                0  \n",
              "lead_time                                 13  \n",
              "arrival_date_year                       2015  \n",
              "arrival_date_month                      July  \n",
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              "stays_in_week_nights                       1  \n",
              "adults                                     1  \n",
              "children                                 0.0  \n",
              "babies                                     0  \n",
              "meal                                      BB  \n",
              "country                                  GBR  \n",
              "market_segment                     Corporate  \n",
              "distribution_channel               Corporate  \n",
              "is_repeated_guest                          0  \n",
              "previous_cancellations                     0  \n",
              "previous_bookings_not_canceled             0  \n",
              "reserved_room_type                         A  \n",
              "assigned_room_type                         A  \n",
              "booking_changes                            0  \n",
              "deposit_type                      No Deposit  \n",
              "agent                                  304.0  \n",
              "company                                  NaN  \n",
              "days_in_waiting_list                       0  \n",
              "customer_type                      Transient  \n",
              "adr                                     75.0  \n",
              "required_car_parking_spaces                0  \n",
              "total_of_special_requests                  0  \n",
              "reservation_status                 Check-Out  \n",
              "reservation_status_date           2015-07-02  "
            ],
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              "    <tr>\n",
              "      <th>arrival_date_month</th>\n",
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              "      <td>GBR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>market_segment</th>\n",
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              "      <td>Corporate</td>\n",
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              "    <tr>\n",
              "      <th>is_repeated_guest</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>reserved_room_type</th>\n",
              "      <td>C</td>\n",
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              "      <td>A</td>\n",
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              "    <tr>\n",
              "      <th>assigned_room_type</th>\n",
              "      <td>C</td>\n",
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              "      <th>booking_changes</th>\n",
              "      <td>3</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c01ab133-eb14-4f1b-b7a1-e5554b5b44be button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c01ab133-eb14-4f1b-b7a1-e5554b5b44be');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "# Dataset First Look- Top 4 rows\n",
        "dataset.head(4).T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "RjqxaFD0T_Nv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "feebb637-ae3e-4f3b-898b-cbf77d987c20"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    119386      119387      119388      119389\n",
              "hotel                           City Hotel  City Hotel  City Hotel  City Hotel\n",
              "is_canceled                              0           0           0           0\n",
              "lead_time                              102          34         109         205\n",
              "arrival_date_year                     2017        2017        2017        2017\n",
              "arrival_date_month                  August      August      August      August\n",
              "arrival_date_week_number                35          35          35          35\n",
              "arrival_date_day_of_month               31          31          31          29\n",
              "stays_in_weekend_nights                  2           2           2           2\n",
              "stays_in_week_nights                     5           5           5           7\n",
              "adults                                   3           2           2           2\n",
              "children                               0.0         0.0         0.0         0.0\n",
              "babies                                   0           0           0           0\n",
              "meal                                    BB          BB          BB          HB\n",
              "country                                FRA         DEU         GBR         DEU\n",
              "market_segment                   Online TA   Online TA   Online TA   Online TA\n",
              "distribution_channel                 TA/TO       TA/TO       TA/TO       TA/TO\n",
              "is_repeated_guest                        0           0           0           0\n",
              "previous_cancellations                   0           0           0           0\n",
              "previous_bookings_not_canceled           0           0           0           0\n",
              "reserved_room_type                       E           D           A           A\n",
              "assigned_room_type                       E           D           A           A\n",
              "booking_changes                          0           0           0           0\n",
              "deposit_type                    No Deposit  No Deposit  No Deposit  No Deposit\n",
              "agent                                  9.0         9.0        89.0         9.0\n",
              "company                                NaN         NaN         NaN         NaN\n",
              "days_in_waiting_list                     0           0           0           0\n",
              "customer_type                    Transient   Transient   Transient   Transient\n",
              "adr                                 225.43      157.71       104.4       151.2\n",
              "required_car_parking_spaces              0           0           0           0\n",
              "total_of_special_requests                2           4           0           2\n",
              "reservation_status               Check-Out   Check-Out   Check-Out   Check-Out\n",
              "reservation_status_date         2017-09-07  2017-09-07  2017-09-07  2017-09-07"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2341e542-fe5c-48cd-9470-aabe0c60bd56\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>119386</th>\n",
              "      <th>119387</th>\n",
              "      <th>119388</th>\n",
              "      <th>119389</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>hotel</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>City Hotel</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_canceled</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lead_time</th>\n",
              "      <td>102</td>\n",
              "      <td>34</td>\n",
              "      <td>109</td>\n",
              "      <td>205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_date_year</th>\n",
              "      <td>2017</td>\n",
              "      <td>2017</td>\n",
              "      <td>2017</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_date_month</th>\n",
              "      <td>August</td>\n",
              "      <td>August</td>\n",
              "      <td>August</td>\n",
              "      <td>August</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_date_week_number</th>\n",
              "      <td>35</td>\n",
              "      <td>35</td>\n",
              "      <td>35</td>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "      <td>31</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stays_in_weekend_nights</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stays_in_week_nights</th>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>adults</th>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>children</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>babies</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>meal</th>\n",
              "      <td>BB</td>\n",
              "      <td>BB</td>\n",
              "      <td>BB</td>\n",
              "      <td>HB</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>country</th>\n",
              "      <td>FRA</td>\n",
              "      <td>DEU</td>\n",
              "      <td>GBR</td>\n",
              "      <td>DEU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>market_segment</th>\n",
              "      <td>Online TA</td>\n",
              "      <td>Online TA</td>\n",
              "      <td>Online TA</td>\n",
              "      <td>Online TA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>distribution_channel</th>\n",
              "      <td>TA/TO</td>\n",
              "      <td>TA/TO</td>\n",
              "      <td>TA/TO</td>\n",
              "      <td>TA/TO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_repeated_guest</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>previous_cancellations</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>previous_bookings_not_canceled</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reserved_room_type</th>\n",
              "      <td>E</td>\n",
              "      <td>D</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>assigned_room_type</th>\n",
              "      <td>E</td>\n",
              "      <td>D</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>booking_changes</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>deposit_type</th>\n",
              "      <td>No Deposit</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>No Deposit</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agent</th>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>company</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>days_in_waiting_list</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>customer_type</th>\n",
              "      <td>Transient</td>\n",
              "      <td>Transient</td>\n",
              "      <td>Transient</td>\n",
              "      <td>Transient</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>adr</th>\n",
              "      <td>225.43</td>\n",
              "      <td>157.71</td>\n",
              "      <td>104.4</td>\n",
              "      <td>151.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>required_car_parking_spaces</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_of_special_requests</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reservation_status</th>\n",
              "      <td>Check-Out</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>Check-Out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reservation_status_date</th>\n",
              "      <td>2017-09-07</td>\n",
              "      <td>2017-09-07</td>\n",
              "      <td>2017-09-07</td>\n",
              "      <td>2017-09-07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2341e542-fe5c-48cd-9470-aabe0c60bd56')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2341e542-fe5c-48cd-9470-aabe0c60bd56 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2341e542-fe5c-48cd-9470-aabe0c60bd56');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "# Dataset First Look- Bottom 4 rows\n",
        "dataset.tail(4).T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7hBIi_osiCS2"
      },
      "source": [
        "### Dataset Rows & Columns count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "Kllu7SJgmLij",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eda78c5e-22bd-4d9b-9141-afc289c82de0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Rows :119390 \n",
            "Dataset Columns :32\n"
          ]
        }
      ],
      "source": [
        "# Dataset Rows & Columns count\n",
        "print('Dataset Rows :{} \\nDataset Columns :{}'.format(dataset.shape[0],dataset.shape[1]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JlHwYmJAmNHm"
      },
      "source": [
        "### Dataset Information"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "e9hRXRi6meOf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43b860fc-211b-413f-eff3-ba180e1a04a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 119390 entries, 0 to 119389\n",
            "Data columns (total 32 columns):\n",
            " #   Column                          Non-Null Count   Dtype  \n",
            "---  ------                          --------------   -----  \n",
            " 0   hotel                           119390 non-null  object \n",
            " 1   is_canceled                     119390 non-null  int64  \n",
            " 2   lead_time                       119390 non-null  int64  \n",
            " 3   arrival_date_year               119390 non-null  int64  \n",
            " 4   arrival_date_month              119390 non-null  object \n",
            " 5   arrival_date_week_number        119390 non-null  int64  \n",
            " 6   arrival_date_day_of_month       119390 non-null  int64  \n",
            " 7   stays_in_weekend_nights         119390 non-null  int64  \n",
            " 8   stays_in_week_nights            119390 non-null  int64  \n",
            " 9   adults                          119390 non-null  int64  \n",
            " 10  children                        119386 non-null  float64\n",
            " 11  babies                          119390 non-null  int64  \n",
            " 12  meal                            119390 non-null  object \n",
            " 13  country                         118902 non-null  object \n",
            " 14  market_segment                  119390 non-null  object \n",
            " 15  distribution_channel            119390 non-null  object \n",
            " 16  is_repeated_guest               119390 non-null  int64  \n",
            " 17  previous_cancellations          119390 non-null  int64  \n",
            " 18  previous_bookings_not_canceled  119390 non-null  int64  \n",
            " 19  reserved_room_type              119390 non-null  object \n",
            " 20  assigned_room_type              119390 non-null  object \n",
            " 21  booking_changes                 119390 non-null  int64  \n",
            " 22  deposit_type                    119390 non-null  object \n",
            " 23  agent                           103050 non-null  float64\n",
            " 24  company                         6797 non-null    float64\n",
            " 25  days_in_waiting_list            119390 non-null  int64  \n",
            " 26  customer_type                   119390 non-null  object \n",
            " 27  adr                             119390 non-null  float64\n",
            " 28  required_car_parking_spaces     119390 non-null  int64  \n",
            " 29  total_of_special_requests       119390 non-null  int64  \n",
            " 30  reservation_status              119390 non-null  object \n",
            " 31  reservation_status_date         119390 non-null  object \n",
            "dtypes: float64(4), int64(16), object(12)\n",
            "memory usage: 29.1+ MB\n"
          ]
        }
      ],
      "source": [
        "# Check dataset information using dataset.info() \n",
        "dataset.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "35m5QtbWiB9F"
      },
      "source": [
        "#### Duplicate Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "1sLdpKYkmox0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa435d78-893b-4ae7-8ba9-4c663a2731a9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "26.797889270458164"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "# Percentage of duplicate values in dataset\n",
        "len(dataset[dataset.duplicated()])*100/dataset.shape[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "fgc5oQwDhdAH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c6d2845-63c8-42ee-998e-c4b3ba4ee25e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of dataset after dropping duplicate values: (87396, 32)\n"
          ]
        }
      ],
      "source": [
        "# Dropping Duplicate Values\n",
        "df = dataset.drop_duplicates().reset_index(drop = True)\n",
        "print('Shape of dataset after dropping duplicate values:',df.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PoPl-ycgm1ru"
      },
      "source": [
        "#### Missing Values/Null Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "GgHWkxvamxVg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1403900e-f0a4-4b7a-fc2d-23724be26600"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "company     82137\n",
            "agent       12193\n",
            "country       452\n",
            "children        4\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Count of missing values/null values\n",
        "nan_count = df.isna().sum().sort_values(ascending = False)\n",
        "\n",
        "# Print only columns with non-zero number of missing values\n",
        "nan_count = nan_count[nan_count>0]\n",
        "print(nan_count)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "3q5wnI3om9sJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "outputId": "95004a06-2acd-4321-931b-933bea81970e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Finding percentage of missing values\n",
        "df_nan_perc = (nan_count*100/len(df)).round(3)\n",
        "\n",
        "# Visualizing the missing values\n",
        "df_nan_perc.plot.bar()\n",
        "\n",
        "# Adding text on top of bars\n",
        "for idx,y in enumerate(df_nan_perc):\n",
        "    plt.text(idx,y+1,y, ha ='center')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RM1HqXv-zUdS"
      },
      "source": [
        "- Most data is missing (`94%`.) from the `'company'` column and also some data is missing from three other columns as well.\n",
        "- We can drop the `'company'` and `'agent'`column.\n",
        "- We can drop the missing rows from columns `'country'` and `'children'`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "uJn1ZeCN1Awq"
      },
      "outputs": [],
      "source": [
        "#Droping missing values\n",
        "df_1 =   df.drop(['company','agent'],axis = 1).dropna().reset_index(drop = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H0kj-8xxnORC"
      },
      "source": [
        "### What did you know about your dataset?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gfoNAAC-nUe_"
      },
      "source": [
        "This data set contains booking information for a city hotel and a resort hotel, and includes information such as when the booking was made, length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nA9Y7ga8ng1Z"
      },
      "source": [
        "## ***2. Understanding Your Variables***"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "j7xfkqrt5Ag5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c78bde2f-afba-4db3-a3c2-672fcd21f604"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',\n",
              "       'arrival_date_month', 'arrival_date_week_number',\n",
              "       'arrival_date_day_of_month', 'stays_in_weekend_nights',\n",
              "       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',\n",
              "       'country', 'market_segment', 'distribution_channel',\n",
              "       'is_repeated_guest', 'previous_cancellations',\n",
              "       'previous_bookings_not_canceled', 'reserved_room_type',\n",
              "       'assigned_room_type', 'booking_changes', 'deposit_type',\n",
              "       'days_in_waiting_list', 'customer_type', 'adr',\n",
              "       'required_car_parking_spaces', 'total_of_special_requests',\n",
              "       'reservation_status', 'reservation_status_date'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "# Dataset Columns\n",
        "df_1.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "DnOaZdaE5Q5t",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "outputId": "cbc68903-5c79-4bee-bf23-f18222ca5576"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  count         mean        std      min  \\\n",
              "is_canceled                     86940.0     0.275903   0.446971     0.00   \n",
              "lead_time                       86940.0    80.193375  86.100147     0.00   \n",
              "arrival_date_year               86940.0  2016.211824   0.686016  2015.00   \n",
              "arrival_date_week_number        86940.0    26.841477  13.654505     1.00   \n",
              "arrival_date_day_of_month       86940.0    15.818024   8.834469     1.00   \n",
              "stays_in_weekend_nights         86940.0     1.007396   1.028763     0.00   \n",
              "stays_in_week_nights            86940.0     2.628744   2.043753     0.00   \n",
              "adults                          86940.0     1.878525   0.625860     0.00   \n",
              "children                        86940.0     0.139211   0.456786     0.00   \n",
              "babies                          86940.0     0.010835   0.113592     0.00   \n",
              "is_repeated_guest               86940.0     0.039234   0.194152     0.00   \n",
              "previous_cancellations          86940.0     0.030228   0.369574     0.00   \n",
              "previous_bookings_not_canceled  86940.0     0.176708   1.717885     0.00   \n",
              "booking_changes                 86940.0     0.271854   0.728090     0.00   \n",
              "days_in_waiting_list            86940.0     0.753497  10.041816     0.00   \n",
              "adr                             86940.0   106.574804  54.957439    -6.38   \n",
              "required_car_parking_spaces     86940.0     0.083448   0.280402     0.00   \n",
              "total_of_special_requests       86940.0     0.699459   0.831840     0.00   \n",
              "\n",
              "                                    25%     50%     75%     max  \n",
              "is_canceled                        0.00     0.0     1.0     1.0  \n",
              "lead_time                         12.00    50.0   125.0   737.0  \n",
              "arrival_date_year               2016.00  2016.0  2017.0  2017.0  \n",
              "arrival_date_week_number          16.00    27.0    37.0    53.0  \n",
              "arrival_date_day_of_month          8.00    16.0    23.0    31.0  \n",
              "stays_in_weekend_nights            0.00     1.0     2.0    16.0  \n",
              "stays_in_week_nights               1.00     2.0     4.0    41.0  \n",
              "adults                             2.00     2.0     2.0    55.0  \n",
              "children                           0.00     0.0     0.0    10.0  \n",
              "babies                             0.00     0.0     0.0    10.0  \n",
              "is_repeated_guest                  0.00     0.0     0.0     1.0  \n",
              "previous_cancellations             0.00     0.0     0.0    26.0  \n",
              "previous_bookings_not_canceled     0.00     0.0     0.0    72.0  \n",
              "booking_changes                    0.00     0.0     0.0    21.0  \n",
              "days_in_waiting_list               0.00     0.0     0.0   391.0  \n",
              "adr                               72.25    98.4   134.1  5400.0  \n",
              "required_car_parking_spaces        0.00     0.0     0.0     8.0  \n",
              "total_of_special_requests          0.00     0.0     1.0     5.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-eba98970-9f75-4212-a47d-2187c7d8c716\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>is_canceled</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>0.275903</td>\n",
              "      <td>0.446971</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lead_time</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>80.193375</td>\n",
              "      <td>86.100147</td>\n",
              "      <td>0.00</td>\n",
              "      <td>12.00</td>\n",
              "      <td>50.0</td>\n",
              "      <td>125.0</td>\n",
              "      <td>737.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_date_year</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>2016.211824</td>\n",
              "      <td>0.686016</td>\n",
              "      <td>2015.00</td>\n",
              "      <td>2016.00</td>\n",
              "      <td>2016.0</td>\n",
              "      <td>2017.0</td>\n",
              "      <td>2017.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_date_week_number</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>26.841477</td>\n",
              "      <td>13.654505</td>\n",
              "      <td>1.00</td>\n",
              "      <td>16.00</td>\n",
              "      <td>27.0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>53.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>15.818024</td>\n",
              "      <td>8.834469</td>\n",
              "      <td>1.00</td>\n",
              "      <td>8.00</td>\n",
              "      <td>16.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>31.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stays_in_weekend_nights</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>1.007396</td>\n",
              "      <td>1.028763</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>16.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stays_in_week_nights</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>2.628744</td>\n",
              "      <td>2.043753</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>41.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>adults</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>1.878525</td>\n",
              "      <td>0.625860</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.00</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>55.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>children</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>0.139211</td>\n",
              "      <td>0.456786</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>babies</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>0.010835</td>\n",
              "      <td>0.113592</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_repeated_guest</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>0.039234</td>\n",
              "      <td>0.194152</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>previous_cancellations</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>0.030228</td>\n",
              "      <td>0.369574</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>previous_bookings_not_canceled</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>0.176708</td>\n",
              "      <td>1.717885</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>72.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>booking_changes</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>0.271854</td>\n",
              "      <td>0.728090</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>days_in_waiting_list</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>0.753497</td>\n",
              "      <td>10.041816</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>391.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>adr</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>106.574804</td>\n",
              "      <td>54.957439</td>\n",
              "      <td>-6.38</td>\n",
              "      <td>72.25</td>\n",
              "      <td>98.4</td>\n",
              "      <td>134.1</td>\n",
              "      <td>5400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>required_car_parking_spaces</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>0.083448</td>\n",
              "      <td>0.280402</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>total_of_special_requests</th>\n",
              "      <td>86940.0</td>\n",
              "      <td>0.699459</td>\n",
              "      <td>0.831840</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eba98970-9f75-4212-a47d-2187c7d8c716')\"\n",
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            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "# Dataset Describe\n",
        "df_1.describe().T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "STSKEfkWxtxl"
      },
      "source": [
        "- Cancellation rate: The is_canceled feature has a mean of 0.27, which suggests that approximately 27% of bookings are cancelled. This could be an important metric, as it could indicate issues with overbooking or customer dissatisfaction.\n",
        "\n",
        "- Lead time: The mean lead time of 80 days suggests that on average, customers book their stay about 80 days in advance. This could be useful for the hotel to know when planning for staffing and room inventory.\n",
        "\n",
        "- Room occupancy: The mean number of adults per booking is 1.87, children is 0.14, and babies is 0.01. This could indicate the typical composition of guests staying in the hotel, and could inform decisions such as room size and amenities offered.\n",
        "\n",
        "- Repeat guests: The is_repeated_guest feature has a mean of 0.04, which suggests that only 4% of guests are repeat customers. This could indicate a need for the hotel to focus on customer retention or improving the guest experience.\n",
        "\n",
        "- Special requests: The total_of_special_requests feature has a mean of 0.7, which suggests that on average, guests make about 0.7 special requests per booking. This could be useful for the hotel to know when planning for staffing and resources.\n",
        "\n",
        "- Booking Changes: The booking_changes feature has a mean of 0.27, this could indicate that guests are frequently changing their bookings, which could indicate issues with customer service or flexibility with the booking process.\n",
        "\n",
        "- Average Daily Rate: The mean of ADR is 106.57,this would be useful for the hotel to know when planning for pricing strategy.\n",
        "\n",
        "Additionally we can see that the max adr is 5400, max days in waiting list is 391 days, max lead time is 737 days which could be outliers, which can be addressed during data visualization.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBTbrJXOngz2"
      },
      "source": [
        "### Variables Description "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJV4KIxSnxay"
      },
      "source": [
        "| Field | Description |\n",
        "| --- | --- |\n",
        "| hotel | H1= Resort Hotel H2=City Hotel |\n",
        "| is_cancelled | If the booking was cancelled(1) or not(0) |\n",
        "| lead_time | \"Number of days that elapsed between the entering date of the booking into the PMS and the arrival date\" |\n",
        "| arrival_date_year | Year of arrival date |\n",
        "| arrival_date_month | Month of arrival date |\n",
        "| arrival_date_week_number | Week number for arrival date |\n",
        "| arrival_dat_day | Day of arrival date |\n",
        "| stays_in_weekend_nights | Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel |\n",
        "| stays_in_week_nights | Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel |\n",
        "| adults | Number of adults | \n",
        "| children | Number of children |\n",
        "| babies | Number of babies |\n",
        "| meal | Kind of meal opted |\n",
        "| country | Country code | \n",
        "| market_segment | Which segment the customer belongs to |\n",
        "|  Distribution _channel | How the customer accessed the stay-corporate booking/Direct/TA.TO |  \n",
        "|  is_repeated_guest | Guest coming for first time or not |  \n",
        "|  previous_cancellation | Was there a cancellation before |  \n",
        "|  previous_bookings | Count of previous bookings |  \n",
        "|  reserved_room_type | Type of room reserved |  \n",
        "|  assigned_room_type | Type of room assigned |  \n",
        "|  booking_changes | Count of changes made to booking |  \n",
        "|  deposit_type | Deposit type |  \t\n",
        "|  agent | Booked through agent |  \n",
        "|  days_in_waiting_list | Number of days in waiting list |  \n",
        "|  customer_type | Type of customer |  \n",
        "|  required_car_parking | If car parking is required |  \n",
        "|  total_of_special_req | Number of additional special requirements |  \n",
        "|  reservation_status | Reservation of status |  \n",
        "|  reservation_status_date | Date of the specific status |  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u3PMJOP6ngxN"
      },
      "source": [
        "### Check Unique Values for each variable."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "YLSNcCShhaOK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce4ac687-0ef5-436e-cfbe-246191301ad2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique values in hotel                       : ['Resort Hotel' 'City Hotel']\n",
            "Unique values in is_canceled                 : [0 1]\n",
            "Unique values in is_repeated_guest           : [0 1]\n",
            "Unique values in arrival_date_year           : [2015 2016 2017]\n",
            "Unique values in deposit_type                : ['No Deposit' 'Refundable' 'Non Refund']\n",
            "Unique values in reservation_status          : ['Check-Out' 'Canceled' 'No-Show']\n",
            "Unique values in customer_type               : ['Transient' 'Contract' 'Transient-Party' 'Group']\n",
            "Unique values in required_car_parking_spaces : [0 1 2 8 3]\n",
            "Unique values in distribution_channel        : ['Direct' 'Corporate' 'TA/TO' 'Undefined' 'GDS']\n",
            "Unique values in children                    : [ 0.  1.  2. 10.  3.]\n",
            "Unique values in babies                      : [ 0  1  2 10  9]\n",
            "Unique values in meal                        : ['BB' 'FB' 'HB' 'SC' 'Undefined']\n",
            "Unique values in total_of_special_requests   : [0 1 3 2 4 5]\n",
            "Unique values in market_segment              : ['Direct' 'Corporate' 'Online TA' 'Offline TA/TO' 'Complementary' 'Groups'\n",
            " 'Aviation']\n",
            "Unique values in reserved_room_type          : ['C' 'A' 'D' 'E' 'G' 'F' 'H' 'L' 'B' 'P']\n",
            "Unique values in assigned_room_type          : ['C' 'A' 'D' 'E' 'G' 'F' 'I' 'B' 'H' 'L' 'K' 'P']\n",
            "Unique values in arrival_date_month          : ['July' 'August' 'September' 'October' 'November' 'December' 'January'\n",
            " 'February' 'March' 'April' 'May' 'June']\n"
          ]
        }
      ],
      "source": [
        "# Check Unique Values for each variable.\n",
        "df_1_unique = df_1.nunique().sort_values()\n",
        "# Print only unique values that are less than 13\n",
        "unique_8 = df_1_unique[df_1_unique < 13].index\n",
        "for column in unique_8:\n",
        "  print('Unique values in {:28}:'.format(column),df_1[column].unique())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dauF4eBmngu3"
      },
      "source": [
        "## **3. *Data Wrangling***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bKJF3rekwFvQ"
      },
      "source": [
        "### Data Wrangling Code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "_mr56tse-1mK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "83d20159-6186-4230-9dfc-2aca42dd6b76"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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RrcvhqnWpJ8C6OnIgq6prqurczOWzVfW6qvrpqnp8pvzli6wwsNqEN5ZlWWPLmGUvxz0uThz1gd39sSTXJklVXZbk8STvSvKDSd7U3T+3kBoCAEzcog5ZviTJw939iQWtDzaGd+cALCqQ3ZDk9pnbr62qe6vq1qq6fK8HVNXpqjpbVWe3t7cXVA24NOEHgFU0dyCrqqckeUWS3xyK3pzkedk5nHk+yRv3elx339Ldp7r71NbW1rzVAABYW4uYIXtZkg929xNJ0t1PdPcXu/tvkrw1yXUL2AZwSKsyG7gq9QBYZYsIZDdm5nBlVV05c9/3JLl/AduASRNaADbbkT9lmSRV9dQk35nkh2eKf6aqrk3SSR654D4AAC4w1wxZd/9Fd39Nd39mpuxV3f0Pu/ubuvsV3X1+/mrC8Rh7purkmTu/rA5j1wmA5fNN/QBsDG9wNtth9/9xjheBDABgZAIZxLtm4Mv5v8BxEsgAAEYmkAEAjEwgY2Nc7PCDwxJHp+8AFkMggyMSRgCmaYz/7wIZAMDIBDLWmlmq4+MLawGWRyBjUhYREgQNAI6bQAYAMDKBDJZsajNuU2sPwCoQyIAvIXAxFcYy60QgAwAYmUAGa2zZMwBmGACOh0AGEyJAAawngQwmbhkhzc9QASyWQAYAMDKBDABgZAIZk+XwGQDrQiADABiZQAZrwowfwHQJZEyaEMMYjDvgsAQyAICRCWQAACMTyJg8h48AWHUCGQDAyAQyJseM2P70EcBqmTuQVdUjVXVfVZ2rqrND2TOr6q6qenD4e/n8VYVxrFJ4uVRdVqmeABzOombIvq27r+3uU8PtM0nu7u6rk9w93AYAYA/LOmR5fZLbhuu3JXnlkrYDG2uRM2Jm1wDGtYhA1kl+p6ruqarTQ9kV3X1+uP7JJFdc+KCqOl1VZ6vq7Pb29gKqAQCwnk4sYB3f2t2PV9XXJrmrqj46e2d3d1X1hQ/q7luS3JIkp06d+rL7AQA2xdwzZN39+PD3ySTvSnJdkieq6sokGf4+Oe92AACmaq5AVlVPraqn715P8l1J7k/yniQ3DYvdlOTd82wHNp1zvACmbd5DllckeVdV7a7rV7v7t6vqj5L8RlXdnOQTSb5vzu0AAEzWXIGsuz+e5B/tUf6pJC+ZZ90AAJvCN/WzlhzCA2BKBDIAgJEJZKy9VZwtW3ad/IQSwLQIZAAAIxPIAABGJpABAIxMIIMNt3vOmXPPAMYjkLF2BIfF0ZcAq0EgAwAYmUDGRjATBMAqE8hgQwmpAKtDIAMAGJlAxtoyw7M/fQSwHgQyJmnKQWTKbQPYVAIZAMDIBDIAgJEJZAAAIxPIAABGJpCxVpzQDsAUCWQAACMTyAAARiaQAQCMTCADABiZQAYAMDKBDABgZAIZa8NXXgAwVQIZAMDIBDLWgtkxAKbsyIGsqq6qqt+tqo9U1Yer6keG8p+uqser6txwefniqgschUALsNpOzPHYLyT5se7+YFU9Pck9VXXXcN+buvvn5q8eAMD0HTmQdff5JOeH65+rqgeSPHtRFQMA2BQLOYesqk4meUGSDwxFr62qe6vq1qq6/CKPOV1VZ6vq7Pb29iKqAQCwluYOZFX1tCR3JHldd382yZuTPC/JtdmZQXvjXo/r7lu6+1R3n9ra2pq3GkyQ854A2BRzBbKq+orshLF3dPdvJUl3P9HdX+zuv0ny1iTXzV9NAIDpmudTlpXkbUke6O6fnym/cmax70ly/9GrBwAwffN8yvJbkrwqyX1VdW4o+8kkN1bVtUk6ySNJfniuGgLHxmFigHHM8ynL309Se9z13qNXBwBg8/imfgCAiziuIwcCGQDAyAQyAICRCWQAACMTyAAARiaQAQCMTCADALiE4/ikpUAGADAygQwAYGQCGaPaaxrYz/cAsGkEMlaGIAbAphLIAABGJpABAIxMIONIDnp40WFIANifQAYAMLKVCGT3Pf6ZsasAADCalQhkrDeHJQFgPgIZAMDIBDIAgJEJZCzd7CHNix3edNgTgE0mkG24g4Slede737qFMQA2nUAGADAygYwDO46ZLLNlAKyiZb8+CWQAACMTyAAARiaQLdmiT5o/zMnyR1nvfp+CPHnmTocVAWDBBLIVssygs+wQtdf6BTcAOJilBbKqemlVfayqHqqqM8vaDgDAultKIKuqy5L8lyQvS/L8JDdW1fOXsS32d5SZqnlmt8yMAcDhLGuG7LokD3X3x7v7r5P8WpLrl7StlXXUb6Xf6zytC89Fm3cdi6rnPAQ3ANixrED27CSPztx+bCgDAOAC1d2LX2nV9yZ5aXf/0HD7VUn+SXe/dmaZ00lODzevSfKxhVdkcZ6V5M/GrsRINrntuza9Dza5/Zvc9l2b3geb3P5Nbvuui/XB3+vurUVu6MQiVzbj8SRXzdx+zlD2/3X3LUluWdL2F6qqznb3qbHrMYZNbvuuTe+DTW7/Jrd916b3wSa3f5Pbvus4+2BZhyz/KMnVVfXcqnpKkhuSvGdJ2wIAWGtLmSHr7i9U1WuT/M8klyW5tbs/vIxtAQCsu2Udskx3vzfJe5e1/mO2FodWl2ST275r0/tgk9u/yW3ftel9sMnt3+S27zq2PljKSf0AABycn04CABjZJANZVV1VVb9bVR+pqg9X1Y8M5c+sqruq6sHh7+VD+TdW1fur6vNV9eMXrOuRqrqvqs5V1dlLbHPPn4qqqtcOZV1Vz1pWm2e2t0ptf3tV/enw+HNVde2y2n1BfVapD769qj5YVfdX1W1VtbTTBGa2ucj2P6Oq3llVH62qB6rqn15km1Mc//O2fQrjf94+WNvxX1XXzOy7c1X12ap63SHbv5bjf0FtP/bxv2LtP/zY7+7JXZJcmeSFw/WnJ/mT7PyE088kOTOUn0nyn4frX5vkHyd5fZIfv2BdjyR51j7buyzJw0m+PslTkvxxkucP970gycmDrGeCbX97ku/d1P2fnTc8jyb5hmG5/5Tk5jVr/21Jfmi4/pQkz9ig8T9v26cw/o/cB1MY/xe08ZPZ+e6pjRj/C2j7sY//VWn/Ucf+JGfIuvt8d39wuP65JA9k55cCrs/OP5gMf185LPNkd/9Rkv97xE1e9KeiuvtD3f3IUdtyWKvU9rGsUB98TZK/7u4/GZa7K8m/POI2DmxR7a+qr07y4iRvG5b76+7+8z02Obnxv4i2j2WF+mCtx/8FXpLk4e7+xB73TW78X+BIbR/DCrX/SGN/koFsVlWdzM67lA8kuaK7zw93fTLJFQdYRSf5naq6p3Z+XWAvK/lTUSvS9tdX1b1V9aaq+srD1H8RRu6DP0tyoqp2v1Twe/OlX5i8dHO2/7lJtpP8clV9qKp+qaqeusdyUxz/i2r7Oo//eftg3cf/rBuS3H6R+6Y4/mfN0/bRxv/I7T/S2J90IKuqpyW5I8nruvuzs/f1zjziQT5i+q3d/cIkL0vymqp68eJrungr0vafSPKN2ZkSfmaS/3jIx89l7D4YtnFDkjdV1R8m+VySLx708fNaQPtPJHlhkjd39wuS/EV2pvtX3oq0fd3H/1x9MIHxv7uepyR5RZLfXHgll2RF2j7a+B+7/Ucd+5MNZFX1FdnZIe/o7t8aip+oqiuH+69M8uR+6+nux4e/TyZ5V5LrhhMHd0/2e3UO8FNRx2lV2j5MH3d3fz7JL2dnevdYrFAfvL+7X9Td1yV5X3bOaVi6BbX/sSSPdfcHhtvvTPLCDRn/c7d9AuN/EX2wzuN/18uSfLC7nxgeuwnjf9eR2z7W+F+h9h967E8ykFVVZee8hwe6++dn7npPkpuG6zclefc+63lqVT1993qS70pyf3c/2t3XDpe3ZIV+KmqV2j7zBKjsHLO/f1Ht3Kfuq9QHXzv8/crsvEN8y6LaeYl6L6T93f3JJI9W1TVD0UuSfGQTxv8i2r7u439BfbC243/GjZk5ZLUJ43/Gkds+xvhfsfYffuz3MX4C4rguSb41O1OS9yY5N1xenp0T7e5O8mCS/5XkmcPyfzc77wY/m+TPh+tflZ1PTvzxcPlwkp+6xDZfnp0E/PDsckn+3bC+LyT5P0l+aYPa/r+T3JedJ+J/S/K0Ddz/P5udE0s/lp3p87Vp/3DftUnODuv670ku34Txv6C2r/X4X1AfrPv4f2qSTyX56n22OcXxP2/bj338r1j7Dz32fVM/AMDIJnnIEgBgnQhkAAAjE8gAAEYmkAEAjEwgAwAYmUAGADAygQwAYGQCGQDAyP4fqkTPEcd4gIEAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Write your code to make your dataset analysis ready.\n",
        "# To check the distribution of data points we can use a countplot\n",
        "\n",
        "# Converting the date column in 'df_1' to a datetime data type using pd.to_datetime() function.\n",
        "df_1['reservation_status_date'] = pd.to_datetime(df_1['reservation_status_date'])\n",
        "\n",
        "# Count the number of values by date\n",
        "counts = df_1['reservation_status_date'].value_counts()\n",
        "\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Limit the x axis for better visualization\n",
        "plt.xlim(pd.Timestamp('2014-10-17'), pd.Timestamp('2017-09-14'))\n",
        "\n",
        "# Plot the bar plot\n",
        "plt.bar(counts.index, counts.values)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MU5PMzypxtxq"
      },
      "source": [
        "- From the plot we can see that very little data points exists before '2015-05-01'. Including these data could skew our results. \n",
        "- Hence we are only including the data from `'2015-05-01'` to `'2017-09-14'` (Last date of booking in the dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "5C59JnGm8cwD"
      },
      "outputs": [],
      "source": [
        "# Trimming the rows of dataset where reservation date is older than 2015-05-01 \n",
        "df_2=df_1[(df_1['reservation_status_date']>pd.Timestamp('2015-05-01'))&((df_1['reservation_status_date']<pd.Timestamp('2017-09-14')))].reset_index(drop = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "aQQdAJaG91JU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "10c056b6-92ec-442e-bb3d-b0d4c95cc41e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Visualizing the new selection of data points\n",
        "# Get the counts\n",
        "counts = df_2['reservation_status_date'].value_counts()\n",
        "\n",
        "# Set the figure size\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Plot the bar plot\n",
        "plt.bar(counts.index, counts.values)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MSa1f5Uengrz"
      },
      "source": [
        "### What all manipulations have you done and insights you found?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "iGZk_OF-xtxr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ecfe7eb-fee4-47ad-a94e-5af6359c806f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the dataset before cleaning : (119390, 32)\n",
            "Shape of the dataset after cleaning : (86691, 30)\n"
          ]
        }
      ],
      "source": [
        "print('Shape of the dataset before cleaning :',dataset.shape)\n",
        "print('Shape of the dataset after cleaning :',df_2.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LbyXE7I1olp8"
      },
      "source": [
        "- `26.79%` of values in dataset are duplicated, hence these are dropped \n",
        "- Data points seems to be missing before `2015-05-01`. Hence the  data before the same is excluded from analysis.\n",
        "- `'company'`,`'agent'`,`'country'`,`'Children'` columns are missing `93.983%`, `3.951%`, `0.517%`, `0.005%` of data repectively\n",
        "- `'company'` and `'agent'`columns are dropped\n",
        "- Missing rows from columns `'country'` and `'children'` are also dropped\n",
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GF8Ens_Soomf"
      },
      "source": [
        "## ***4. Data Vizualization, Storytelling & Experimenting with charts : Understand the relationships between variables***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OopIPJDl5Cv9"
      },
      "source": [
        "## **Univariate Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijXsOAFPxtxs"
      },
      "source": [
        "1. To understand the distribution of data in various categorical variables lets plot a percentage bar plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "1pu2YxNkovYS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 904
        },
        "outputId": "5910f586-6cd9-43a0-a563-ccce2f406d61"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#let's try to plot all the non numerical categorical columns. First let's remove columns \n",
        "#having too many categories, also the 'hotel' column to analyze later.  \n",
        "cat_columns = df_2.select_dtypes(exclude=['int', 'float']).columns\n",
        "cat_columns =list(cat_columns)\n",
        "cat_columns.remove('reservation_status_date')\n",
        "cat_columns.remove('country')\n",
        "cat_columns.remove('hotel')\n",
        "\n",
        "# set a larger figure size for subplots\n",
        "plt.figure(figsize = [20  , 15]) \n",
        "for i,x in enumerate(cat_columns):\n",
        "\n",
        "  # 3 row, 3 cols, subplot i+1 since subplot starts from 1\n",
        "  plt.subplot(3, 3,i+1)\n",
        "\n",
        "  # Get the value counts of the x column\n",
        "  counts = (df_2[x].value_counts()*100/len(df_2)).round(1)\n",
        "  \n",
        "  # Create a barplot using the value counts\n",
        "  sns.barplot(x=counts.index, y=counts.values, order = counts.index )\n",
        "  plt.title(str(x)) \n",
        "  plt.xticks(rotation = 20)\n",
        "  # Adding percentage values on top of bars\n",
        "  for idx,y in enumerate(counts.values):\n",
        "    plt.text(idx,y+1,'{} %'.format(y), ha ='center')\n",
        "    \n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C3JDaF6weRI_"
      },
      "source": [
        "- Here bar charts are used  due to effectiveness in comparing and visualizing large categories of data\n",
        "- `August` and `July` are the months with the highest number of bookings (`12.9%` and `11.5%`, respectively) \n",
        "- While `january` and `November` has the least number of bookings (`5.3%` and `5.7%`, respectively)\n",
        "- The meal type `BB   (Breakfast in Bed)` is most prefered, (`77%`) of total orders\n",
        "- `Online TA/TO` (Travel agent/Tour Operator) is the most prefered medium of bookings(`59%`)\n",
        "- `Room type A` is the most reserved and assigned room type (`64.6%` and `53.0%`, respectively).\n",
        " Later we can check if the demand has a correlation with the price the room. \n",
        "- `No deposit bookings` account for the majority of bookings(`98.8%`)\n",
        "- `Customer type transient` makes the majority of bookings(`82.4%`)\n",
        "\n",
        "- The data suggest that Bookings during `August` and `July`, `BB meal type`, `No deposit` Booking type, Booking through `Online TA`, `Room type A`, `Transient` are the popular options among guests.\n",
        "Increasing the availability of these options could lead to an increased revenue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ZhUL5Jjis2r"
      },
      "source": [
        "## **Univariate Analysis Questions**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GKriEuS8p3OG"
      },
      "source": [
        "#### Chart - 1. What is the trend of *bookings* over **month**, **week** and **day**?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "kYr490Xq4atA",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "9adf7680-c22a-420b-d443-4a735e3fd1b5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x360 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "cat_columns_ordinal =['arrival_date_month', 'arrival_date_week_number', 'arrival_date_day_of_month']\n",
        "\n",
        "# Create a figure with 3 subplots\n",
        "plt.figure(figsize = [25, 5]) \n",
        "\n",
        "# 1 row, 3 cols, subplot 1\n",
        "plt.subplot(1, 3, 1)\n",
        "x=cat_columns_ordinal[0]\n",
        "\n",
        "# Create a histogram of the values in the column\n",
        "plt.hist(df_2[x], bins = 12)\n",
        "plt.xlabel(str(x))\n",
        "plt.xticks(rotation = 20)\n",
        "\n",
        "# Subplot 2\n",
        "plt.subplot(1, 3, 2)\n",
        "# Assign the second column in the list to x\n",
        "x=cat_columns_ordinal[1]\n",
        "\n",
        "# Create a histogram of the values in the column\n",
        "plt.hist(df_2[x], bins = 53)\n",
        "plt.xlabel(str(x))\n",
        "plt.xticks(rotation = 20)\n",
        "\n",
        "# Subplot 3\n",
        "plt.subplot(1, 3, 3)\n",
        "x=cat_columns_ordinal[2]\n",
        "\n",
        "# Create a histogram of the values in the column\n",
        "plt.hist(df_2[x], bins = 31)\n",
        "plt.xlabel(str(x))\n",
        "plt.xticks(rotation = 20)\n",
        "\n",
        "# Show the figure\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5QZ13OEpz2H"
      },
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XESiWehPqBRc"
      },
      "source": [
        "Histograms helps us to see the distribution of data over categories or a period of time."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lQ7QKXXCp7Bj"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pYA8-Rx2qGXL"
      },
      "source": [
        "- Based on the data, it appears that bookings vary by season, with the highest number of bookings take place during the months of May, June, July, and August. \n",
        "- The months of November, December, January, and February tend to have the lowest levels of bookings.\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "448CDAPjqfQr"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3cspy4FjqxJW"
      },
      "source": [
        "- Identifying the periods of higher demand can help the management to prepare beforehand and allocate more resources\n",
        "- Additionally strategies can be developed to address lower demand periods."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ICO9KadDxtxz"
      },
      "source": [
        "#### Chart - 2. What are the guest demographics?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "evl_oWRixtxz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "68c75b43-acf5-4f75-d802-be66b86ca4d6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Get the top 20 countries based on the number of bookings and the proportion of total bookings\n",
        "df_top = (df_2['country'].value_counts()[:20]*100/len(df_2)).round(2)\n",
        "\n",
        "# Create a bar plot with the top countries on the y-axis and the proportion of bookings on the x-axis\n",
        "sns.barplot(x=df_top.values, y=df_top.index)\n",
        "\n",
        "# Add x-axis label\n",
        "plt.xlabel('Proportion of Total Bookings')\n",
        "# Add y-axis label\n",
        "plt.ylabel('Country')\n",
        "\n",
        "# Loop through the bars and add the percentage value above each bar\n",
        "for idx,y in enumerate(df_top.values):\n",
        "    plt.text(y,idx,str(y)+'%', ha ='left')     \n",
        "# Show the plot\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kA-npbPdxtxz"
      },
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zUFWh_IZxtx0"
      },
      "source": [
        "- Here horizontal bar chart helps us to visualize and compare multiple categories "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q0a2qSbSxtx0"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EyXaZAJExtx0"
      },
      "source": [
        "- Guests from `Portugal` has made most bookings (`31%`) followed by `United Kingdom` (`12%`) and `France` (`10%`). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pOvmtI7mxtx0"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jcg7zAwQxtx1"
      },
      "source": [
        "- Identifying the demographics of guests helps to better prepare staffs and run ad-campaigns "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KSlN3yHqYklG"
      },
      "source": [
        "#### Chart - 3. What is the **percentage of cancellations?**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "R4YgtaqtYklH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "24c29623-244a-43c3-8bd4-2bc864624aea"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Chart - 2 visualization code\n",
        "#identify percentage of cancellations\n",
        "\n",
        "# Get percentage of value counts for 'arrival_date_month' column\n",
        "counts = (df_2['is_canceled'].value_counts()*100/len(df_2)).round(2)\n",
        "\n",
        "# Set figure size\n",
        "plt.figure(figsize = [10  , 5])   \n",
        "\n",
        "# Create a barplot using the value counts\n",
        "sns.barplot(x=counts.index, y=counts.values, order = counts.index )\n",
        "\n",
        "#Rotate x-tick labels for easier reading, add x-label\n",
        "plt.xticks(rotation = 20)\n",
        "plt.xlabel('is_canceled')\n",
        "\n",
        "#Add value count labels on top of each bar\n",
        "for idx,y in enumerate(counts.values):\n",
        "  \n",
        "  #plt.text(x_co-ord, y_co-ord, value, ha(horrizontal alignment))\n",
        "  plt.text(idx,y+0.5,str(y)+'%', ha ='center') \n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6dVpIINYklI"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "##### 1. Why did you pick the specific chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5aaW0BYyYklI"
      },
      "source": [
        "A bar chart makes it easier compare the different categories. Here it helps to visualize and compare the proportion of bookings that were cancelled"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijmpgYnKYklI"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PSx9atu2YklI"
      },
      "source": [
        "- From the chart we an see that a good portion (`27%`) of the bookings were cancelled which leads to loss of revenue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-JiQyfWJYklI"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? \n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BcBbebzrYklV"
      },
      "source": [
        "- The cancellations can lead to negative growth due to a decrease in revenue. Further analysis can be conducted to identify the reason/sources of high cancellations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZmrZinTlEKW"
      },
      "source": [
        "## **Bivariate Analysis**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OH-pJp9IphqM"
      },
      "source": [
        "#### Chart - 4. Which Hotel generates highest revenue?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "r84ImAAL3t8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "outputId": "365f703c-3bf7-4660-8523-c9d1b094cae7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Chart - 2 visualization code\n",
        "# Get total stay duration from stays in week and weekends\n",
        "df_2['Total_stay_duration'] = df_2['stays_in_weekend_nights'] + df_2['stays_in_week_nights']\n",
        "\n",
        "# Get revenue by multiplyig total stay duarion with adr\n",
        "df_2['revenue'] = df_2['Total_stay_duration']*df_2['adr']\n",
        "\n",
        "# Group and sum the revenue column by hotel column\n",
        "# Grouping returns a multi-index pandas series. Use pd.DataFrame to convert to a dataframe\n",
        "group_1 = pd.DataFrame(df_2[['hotel','revenue']].groupby(['hotel']).sum())\n",
        "\n",
        "# Plot the data using sns.barplot()\n",
        "sns.barplot(data = group_1, x=group_1.index,y='revenue')\n",
        "\n",
        "# Add value count labels on top of each bar\n",
        "for idx,y in enumerate(group_1.values):\n",
        "  \n",
        "  #plt.text(x_co-ord, y_co-ord, value, ha(horrizontal alignment))\n",
        "  plt.text(idx,y+(10**5),str((y/10**6).round(2))+' M', ha ='center') \n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bbFf2-_FphqN"
      },
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loh7H2nzphqN"
      },
      "source": [
        "Here the Bar chart helps us to easily visualize and compare the difference in revenue of the two hotels."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ouA3fa0phqN"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VECbqPI7phqN"
      },
      "source": [
        "- City Hotel and Resort hotel has generated a revenue of `18.75 M Pounds` and `15.49 M pounds` respectively during the period `2015-05-01` - `2017-09-01`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Seke61FWphqN"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? \n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DW4_bGpfphqN"
      },
      "source": [
        " - Revenue is a key metric which is used to identify the performance of a hotel. This can help us in the further evaluations."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Of9eVA-YrdM"
      },
      "source": [
        "#### Chart - 5. How does average of ADR varies by Week,Room type and Market Segment? \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "rhsWtlOQxtx9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 420
        },
        "outputId": "16b48dba-1345-46f3-a2c2-6e216bcb7997"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# set figure size \n",
        "plt.figure(figsize = [20, 5])\n",
        "\n",
        "# create first subplot\n",
        "plt.subplot(1,3,1)\n",
        "\n",
        "# group data by 'arrival_date_week_number' and calculate average 'adr'\n",
        "df_2.groupby('arrival_date_week_number')['adr'].mean().plot()\n",
        "plt.title(\"Average ADR by Week Number\")\n",
        "\n",
        "# create second subplot\n",
        "plt.subplot(1,3,2)\n",
        "\n",
        "# group data by 'reserved_room_type' and calculate average 'adr' while sorting values in descending order\n",
        "df_2.groupby('reserved_room_type')['adr'].mean().sort_values(ascending =False).plot(kind='bar')\n",
        "plt.title(\"Average ADR by Room Type\")\n",
        "\n",
        "# create third subplot\n",
        "plt.subplot(1,3,3)\n",
        "\n",
        "# group data by 'market_segment' and calculate average 'adr' while sorting values in descending order\n",
        "df_2.groupby('market_segment')['adr'].mean().sort_values(ascending =False).plot(kind='bar')\n",
        "plt.title(\"Average ADR by Market Segment\")\n",
        "\n",
        "# display the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iky9q4vBYrdO"
      },
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJRCwT6DYrdO"
      },
      "source": [
        "- Here the line chart helps to see the trend of ADR over time, while the bar chart helps to compare different categories."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F6T5p64dYrdO"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xx8WAJvtYrdO"
      },
      "source": [
        "- The peak in average `ADR` occurs during the `weeks 30-35` which can be correlated to high booking demand.\n",
        "- This might be due to the hotels adjusting the room rates for the higher demand and increased sales of other amenities. \n",
        "- The rooms which generates the highest `ADR` are `H` followed `G`, and `F`\n",
        "- The market segment which generates highest `ADR` are `Online Travel Agent` followed by `Direct Booking` and `Aviation`\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-Ehk30pYrdP"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zY-Kz7sxtyA"
      },
      "source": [
        "- Since `online TA` generates high ADR the management can direct its marketing towards online mediums to increase its visibility on these platforms and potentially capture more bookings."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fx1c3KWixtyA"
      },
      "source": [
        "#### Chart 6. How does adr varies with room type?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "CNygUJF4xtyB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "3ce56356-6ac2-41cf-a125-cb4416d426a4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Filtering outlier in ADR\n",
        "df_2 = df_2[df_2['adr']<1000]\n",
        "\n",
        "# Set figure size\n",
        "plt.figure(figsize=(20,5))\n",
        "\n",
        "# First subplot\n",
        "plt.subplot(1,2,1)\n",
        "\n",
        "# Add boxplot for ADR over assigned room type\n",
        "sns.boxplot(x='assigned_room_type', y='adr', data=df_2)\n",
        "plt.xlabel('Assigned Room Type')\n",
        "plt.ylabel('Average Daily Rate')\n",
        "plt.title('ADR vs Room Type')\n",
        "\n",
        "# Second subplot\n",
        "plt.subplot(1,2,2)\n",
        "\n",
        "# Add countplot for bookings over room type\n",
        "sns.countplot(data=df_2, x='assigned_room_type')\n",
        "plt.xlabel('Assigned Room Type')\n",
        "plt.ylabel('Bookings Count')\n",
        "plt.title('Bookings Count vs Room Type')\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8dOe600xtyB"
      },
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZhVM-Om2xtyC"
      },
      "source": [
        "- Here the box plot helps to visualize the distribution of ADR over different room types"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "37vhM4ooxtyC"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uL3wNQ5HxtyC"
      },
      "source": [
        "- Most booked rooms are `A`, `D`, `E` with an average ADR of `95`, `109` and `119` respectively\n",
        "- Most expensive rooms are `F`,`G` and `H` with average ADR of `153`, `167`, and `172` respectively\n",
        "- Average daily rate is negatively correlated to number of bookings"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKAQXcGaxtyD"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9oNtIO2FxtyD"
      },
      "source": [
        "- Management can use these insights to optimize ADR and to maximize revenue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PIIx-8_IphqN"
      },
      "source": [
        "#### Chart - 7. Which hotel has highest cancellations?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "lqAIGUfyphqO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "f7f0a9f5-8a68-46e7-f666-d2b2e3a70b91"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Get percentage of cancellations for each hotel\n",
        "counts = (df_2.groupby('hotel')['is_canceled'].sum()* 100 / counts_total).round(2)\n",
        "\n",
        "# Create a barplot of cancellation percentages for each hotel\n",
        "sns.barplot(x=counts.index, y=counts.values)\n",
        "\n",
        "# Add x and y labels\n",
        "plt.xlabel('Hotel')\n",
        "plt.ylabel('Cancellation Percentage (%)')\n",
        "\n",
        "# Add value count labels on top of each bar\n",
        "for i, v in enumerate(counts.values):\n",
        "    plt.text(i, v, f'{v}%', ha='center')\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t27r6nlMphqO"
      },
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iv6ro40sphqO"
      },
      "source": [
        "Here barchart helps us compare and visualize differences in percentage of cancellations in each hotel"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r2jJGEOYphqO"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Po6ZPi4hphqO"
      },
      "source": [
        "We can observe that that City Hotels have a higher percentage of cancellations compared to Resort Hotels."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0JNsNcRphqO"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? \n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xvSq8iUTphqO"
      },
      "source": [
        "- Cancellations can lead to a loss of revenue. We can try to identify the reasons for this in our further analysis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bamQiAODYuh1"
      },
      "source": [
        "#### Chart - 8. How does cancellation rate varies by Room Type? "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "XW-6JjnGxtyG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "e54f94e0-769e-4891-aabf-99ca393ae18a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Set figure size\n",
        "plt.figure(figsize=[20,10])\n",
        "\n",
        "# Subplot for Cancelled Room Types\n",
        "plt.subplot(2, 3, 1)\n",
        "\n",
        "# Get percentage of cancelled rooms grouped by room type\n",
        "cancelled_rooms = (df_2.groupby('reserved_room_type')['is_canceled'].sum() * 100 / len(df_2)).round(2).sort_values(ascending=False)\n",
        "\n",
        "# PLot barplot for cancelled_rooms\n",
        "sns.barplot(x=cancelled_rooms.index, y=cancelled_rooms.values)\n",
        "plt.title('Cancellation % across Room Types')\n",
        "plt.ylabel('Cancellation Percentage (%)')\n",
        "\n",
        "# Add perentage values on top of bars\n",
        "for i, v in enumerate(cancelled_rooms.values):\n",
        "    plt.text(i, v, f'{v}%', ha='center')\n",
        "\n",
        "# Subplots for Room Type Distribution\n",
        "# Use for loop to plot booking percentage for two room types\n",
        "room_types = ['reserved_room_type', 'assigned_room_type']\n",
        "for i, col in enumerate(room_types):\n",
        "    plt.subplot(2, 3, i+2)\n",
        "    counts = (df_2[col].value_counts() * 100 / len(df_2)).round(1)\n",
        "    sns.barplot(x=counts.index, y=counts.values)\n",
        "    plt.title('Booking % for '+str(col))\n",
        "    plt.ylabel('Booking Percentage (%)')\n",
        "\n",
        "    # Show % value on top of bars\n",
        "    for j, val in enumerate(counts.values):\n",
        "        plt.text(j, val, f'{val}%', ha='center')\n",
        "        "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QHF8YVU7Yuh3"
      },
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dcxuIMRPYuh3"
      },
      "source": [
        "- Bar chart helps us to visualize and compare the cancellation rates across room types"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GwzvFGzlYuh3"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uyqkiB8YYuh3"
      },
      "source": [
        "- Cancellations are higher for room `A` followed by `D` and `E`\n",
        "- From the booking percentages can see the shift of provided rooms from `A` to `D` and `E`. \n",
        "- Hence we can conclude that room type `A` is getting cancelled due to the lack of availability."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qYpmQ266Yuh3"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? \n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTKeXjJvxtyJ"
      },
      "source": [
        "- Increasing the availabilty of most popular room (A) could decrease cancellations and increase revenue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7rn4smqUxtyJ"
      },
      "source": [
        "#### Chart - 9. How does cancellation rate varies by market segment? \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "36kINW_fn-dL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "9b9d4942-cfa1-45b3-a606-75759034a18e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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EOedc0jyJOOecS1qVqANwzjm3tyZbJ6Xs2sv247X8ScQ551zSPIk455xLmicR55xzSfMk4pxzLmmeRJxzziXNk4hzzrmkeRJxzjmXNE8izjnnkuZJxDnnXNJKnUQkHSKpXQLH1ZCUK+kjSZ9IGhOWN5X0b0lLJD0lqVpYXj3cXhLubxJzrWvC8s8lnRZT3jssWyJpVGk/i3POubJJKIlImiWptqR6wIfA/0n6WwmnbQNONrP2QDbQW1I34DbgbjNrBqwHLg6PvxhYH5bfHR6HpFbA+UBroDfwT0mVJVUG/gH0AVoBA8NjnXPOlZNEn0TqmNkPwABggpl1BU7d1wkW+DHcrBq+DDgZeDYsfwzoF74/K9wm3H+KJIXlk81sm5l9BSwBuoSvJWa21My2A5PDY51zzpWTRJNIFUlHAucCLyd68fCJYT6wCpgBfAlsMLOd4SHLgYbh+4bAtwDh/o3AobHlRc4prjxeHJdKypOUt3r16kTDd845V4JEk8gYYDrBnf9cSUcDi0s6yczyzSwbyCJ4cmiZdKRlYGYPm1mOmeXUr18/ihCcc+6AlOhU8CvNbHdjupktTaBNZDcz2yDpLeB4oK6kKuHTRhawIjxsBdAIWC6pClAHWBtTXiD2nOLKnXPOlYNEn0TuS7BsN0n1JdUN3x8E9AQ+Bd4Czg4PGwJMCd9PDbcJ979pZhaWnx/23moKNAdygblA87C3VzWCxvepCX4e55xz+8E+n0QkHQ+cANSX9IeYXbWByiVc+0jgsbAXVSXgaTN7WdIiYLKkm4B5wCPh8Y8Aj0taAqwjSAqY2SeSngYWATuB4WaWH8Z3OUE1W2VgnJl9kuDnds45tx+UVJ1VDagVHndwTPkP7HmaiMvMFgAd4pQvJWgfKVq+FTinmGv9FfhrnPJXgVf3FYdzzrnU2WcSMbO3gbcljTezr8spJueccxki0Yb16pIeBprEnmNmJ6ciKOecc5kh0STyDPAgMBbIT104zjnnMkmiSWSnmT2Q0kicc85lnES7+L4k6TJJR0qqV/BKaWTOOefSXqJPIgXjN66KKTPg6P0bjnPOuUySUBIxs6apDsQ551zmSSiJSLogXrmZTdi/4TjnnMskiVZndY55XwM4hWBdEU8izjlXgSVanTUidjucE2tySiJyzjmXMZJdY/0nwNtJnHOugku0TeQlgt5YEEx2eBzwdKqCcs45lxkSbRO5M+b9TuBrM1uegnicc85lkISqs8KJGD8jmMn3EGB7KoNyzjmXGRJKIpLOJVgI6hyCddb/LWmfU8E755w78CVanXUt0NnMVkGwaiHwBvBsqgJzzjmX/hLtnVWpIIGE1pbiXOeccweoRJ9EpkmaDjwZbp+HryjonHMVXklrrDcDDjezqyQNAE4Md/0LmJjq4JxzzqW3kp5E/g5cA2BmzwPPA0hqG+77dUqjc845l9ZKatc43Mw+LloYljVJSUTOOecyRklJpO4+9h20PwNxzjmXeUpKInmShhUtlHQJ8EFqQnLOOZcpSmoT+R/gBUmD2JM0coBqQP9UBuaccy797TOJmNn3wAmSegBtwuJXzOzNlEfmnHMu7SW6nshbwFspjsU551yG8VHnzjnnkuZJxDnnXNI8iTjnnEuaJxHnnHNJ8yTinHMuaSlLIpIaSXpL0iJJn0i6IiyvJ2mGpMXhz0PCckm6V9ISSQskdYy51pDw+MWShsSUd5L0cXjOvZKUqs/jnHNub6l8EtkJ/D8zawV0A4ZLagWMAmaaWXNgZrgN0AdoHr4uBR6AIOkA1wNdgS7A9QWJJzxmWMx5vVP4eZxzzhWRsiRiZivN7MPw/SbgU6AhcBbwWHjYY0C/8P1ZwAQLzAHqSjoSOA2YYWbrzGw9MAPoHe6rbWZzzMyACTHXcs45Vw7KpU1EUhOgA/BvgpmBV4a7/gMcHr5vCHwbc9rysGxf5cvjlMf7/ZdKypOUt3r16jJ9Fuecc3ukPIlIqgU8B/yPmf0Quy98grBUx2BmD5tZjpnl1K9fP9W/zjnnKoyUJhFJVQkSyMRwUSuA78OqKMKfBWu3rwAaxZyeFZbtqzwrTrlzzrlyksreWQIeAT41s7/F7JoKFPSwGgJMiSm/IOyl1Q3YGFZ7TQd6STokbFDvBUwP9/0gqVv4uy6IuZZzzrlykNAEjEn6BTAY+FjS/LDsT8CtwNOSLga+Bs4N970K9AWWAJuBiwDMbJ2kG4G54XE3mNm68P1lwHiCBbJeC1/OOefKScqSiJm9CxQ3buOUOMcbMLyYa40DxsUpz2PPFPXOOefKmY9Yd845lzRPIs4555LmScQ551zSPIk455xLmicR55xzSfMk4pxzLmmeRJxzziXNk4hzzrmkeRJxzjmXNE8izjnnkuZJxDnnXNI8iTjnMtrQoUNp0KABbdrsPY3eXXfdhSTWrFkT99zKlSuTnZ1NdnY2Z5555l77R44cSa1atXZv33fffbRp04a+ffuyfft2AN59912uvPLK/fRpMo8nEedcRrvwwguZNm3aXuXffvstr7/+Oo0bNy723IMOOoj58+czf/58pk6dWmhfXl4e69evL1Q2ceJEFixYwAknnMD06dMxM2688UZGjx69fz5MBvIk4pzLaN27d6devXp7lV955ZXcfvvtBMsNlU5+fj5XXXUVt99+e6FyM2PHjh1s3ryZqlWr8sQTT9CnT5+4v7+i8CTinDvgTJkyhYYNG9K+fft9Hrd161ZycnLo1q0bL7744u7y+++/nzPPPJMjjzyy0PGXX3453bp145tvvuEXv/gFjz76KMOHx13BosJI5aJUzjlX7jZv3szNN9/M66+/XuKxX3/9NQ0bNmTp0qWcfPLJtG3bloMOOohnnnmGWbNm7XX84MGDGTx4MAA33HADI0eO5LXXXmPChAk0atSIu+66i0qVKta9ecX6tM65A96XX37JV199Rfv27WnSpAnLly+nY8eO/Oc//9nr2IYNGwJw9NFHc9JJJzFv3jzmzZvHkiVLaNasGU2aNGHz5s00a9as0Hnfffcdubm59OvXj7vuuounnnqKunXrMnPmzHL5jOnEn0SccweUtm3bsmrVqt3bTZo0IS8vj8MOO6zQcevXr6dmzZpUr16dNWvW8N5773H11VfTqlWrQgmnVq1aLFmypNC5o0eP5oYbbgBgy5YtSKJSpUps3rw5hZ8sPfmTiHMuow0cOJDjjz+ezz//nKysLB555JFij83Ly+OSSy4B4NNPPyUnJ4f27dvTo0cPRo0aRatWrUr8ffPmzQOgY8eOAPz2t7+lbdu2vPfee/Tu3Xs/fKLMomBp84ojJyfH8vLyog7DOZdqf6mT4utvTOnlm4x6JWXXXnbr6aU6XtIHZpYTb58/iThXwZVlsN4333xDr169OO6442jVqhXLli0rtN8H6x34PIk4V8GVZbDeBRdcwFVXXcWnn35Kbm4uDRo02L3PB+tVDJ5EnKvgkh2st2jRInbu3EnPnj2BoAG6Zs2agA/Wq0g8iTjn9pLIYL0vvviCunXrMmDAADp06MBVV11Ffn4+4IP1KhLv4uucKyTRwXo7d+5k9uzZzJs3j8aNG3Peeecxfvx4+vTp44P1KhD/23LOFZLoYL2srCyys7M5+uijqVKlCv369ePDDz/0wXoVjD+JOOcKSXSwXufOndmwYQOrV6+mfv36vPnmm+Tk5HD66af7YL0KxJOIcxXcwIEDmTVrFmvWrCErK4sxY8Zw8cUXxz02Ly+PBx98kLFjx1K5cmXuvPNOTjnlFMyMTp06MWzYsBJ/X3GD9Ro1asTVV1+93z5Xk62T9tu14lmW0qtnDh9s6JyLzwfr7VNpB+yVlg82dM45d8DzJOKccy5pKUsiksZJWiVpYUxZPUkzJC0Ofx4SlkvSvZKWSFogqWPMOUPC4xdLGhJT3knSx+E59yqZ5cucc86VSSqfRMYDRae0HAXMNLPmwMxwG6AP0Dx8XQo8AEHSAa4HugJdgOsLEk94zLCY8yre9JnOORexlCURM3sHWFek+CzgsfD9Y0C/mPIJFpgD1JV0JHAaMMPM1pnZemAG0DvcV9vM5ljQM2BCzLWcc86Vk/JuEznczFaG7/8DHB6+bwh8G3Pc8rBsX+XL45Q755wrR5GNEzEzk1Qu/YslXUpQTbbPGUmdc3v4OAuXiPJ+Evk+rIoi/FkwLHYF0CjmuKywbF/lWXHK4zKzh80sx8xy6tevX+YP4ZxzLlDeSWQqUNDDaggwJab8grCXVjdgY1jtNR3oJemQsEG9FzA93PeDpG5hr6wLYq7lnHOunKSsOkvSk8BJwGGSlhP0sroVeFrSxcDXwLnh4a8CfYElwGbgIgAzWyfpRmBueNwNZlbQWH8ZQQ+wg4DXwpdzzrlylLIkYmYDi9l1SpxjDYi7qICZjQPGxSnPA/Zez9M551y58RHrzjnnkuZJxDnnXNI8iTjnnEuaJxHnnHNJ8yTinHMuaZ5EnHPOJc2TiHPOuaR5EnHOOZc0TyLOldG3335Ljx49aNWqFa1bt+aee+7Zve++++6jZcuWtG7dmquvvjru+dOmTaNFixY0a9aMW2+9dXf5hRdeSNOmTcnOziY7O5v58+cD8Nxzz9G6dWt++ctfsnbtWgC+/PJLzjvvvBR+Sufii2wWX+cOFFWqVOGuu+6iY8eObNq0iU6dOtGzZ0++//57pkyZwkcffUT16tVZtWrVXufm5+czfPhwZsyYQVZWFp07d+bMM8+kVatWANxxxx2cffbZhc657777mDt3Ls8//zyTJk1ixIgR/PnPf+amm24ql8/rXCx/EnGujI488kg6dgxWdD744IM57rjjWLFiBQ888ACjRo2ievXqADRo0GCvc3Nzc2nWrBlHH3001apV4/zzz2fKlH3PJVqpUiW2bdvG5s2bqVq1KrNnz+aII46gefPm+//DOVcCTyIuckOHDqVBgwa0abNnKrSrrrqKli1b0q5dO/r378+GDRv2Om/r1q106dKF9u3b07p1a66//vrd+y6++GLat29Pu3btOPvss/nxxx+B4C6+TZs29O3bl+3btwPw7rvvcuWVV+6Xz7Js2TLmzZtH165d+eKLL5g9ezZdu3blV7/6FXPnzt3r+BUrVtCo0Z7VDrKyslixYs+qBtdeey3t2rXjyiuvZNu2bQBcc801nHrqqbz00ksMHDiQG2+8kdGjR++X+J0rLU8iLnIXXngh06ZNK1TWs2dPFi5cyIIFCzj22GO55ZZb9jqvevXqvPnmm3z00UfMnz+fadOmMWfOHADuvvtuPvroIxYsWEDjxo25//77AZg4cSILFizghBNOYPr06ZjZfvsS/vHHH/nNb37D3//+d2rXrs3OnTtZt24dc+bM4Y477uDcc88lmGs0MbfccgufffYZc+fOZd26ddx22227/2w++OADXnrpJaZMmULfvn354osvOPvssxk2bBibN28u82dxLlGeRFzkunfvTr169QqV9erViypVgia7bt26sXz58r3Ok0StWrUA2LFjBzt27CBYXgZq164NgJmxZcuW3eVmxo4dO3ZXBT3xxBP06dNnr99fWjt27OA3v/kNgwYNYsCAAUDwVDFgwAAk0aVLFypVqsSaNWsKndewYUO+/XbPCtDLly+nYcNgpecjjzwSSVSvXp2LLrqI3NzcQudu3ryZ8ePHM3z4cK6//noee+wxTjzxRCZOnFimz+JcaXgScWlv3Lhx9OnTJ+6+/Px8srOzadCgAT179qRr166791100UUcccQRfPbZZ4wYMQKAyy+/nG7duvHNN9/wi1/8gkcffZThw+OuQpAwM+Piiy/muOOO4w9/+MPu8n79+vHWW28B8MUXX7B9+3YOO+ywQud27tyZxYsX89VXX7F9+3YmT57MmWeeCcDKlSt3X//FF18sVN0HQaP7yJEjqVq16u5EWalSJX8SceXKe2e5tPbXv/6VKlWqMGjQoLj7K1euzPz589mwYQP9+/dn4cKFu79sH330UfLz8xkxYgRPPfUUF110EYMHD2bw4MEA3HDDDYwcOZLXXnuNCRMm0KhRI+666y4qVSrdvdV7773H448/Ttu2bcnOzgbg5ptvZuiKqxk6ZSttHvwr1SrDY71qoDF1+aQcnzMAAByDSURBVG7TLi6ZupVXB9WkCnD/8Ts4LacZ+WYMza5G62dOgGdg0GM/sXqzYQbZR1TmwTNqwF8eAOC7TbvI/az77nagESNG0LlzZ+rWrcuLL76YzB+1c0nxJOLS1vjx43n55ZeZOXPm7uqo4tStW5cePXowbdq0QnfslStX5vzzz+f222/noosu2l3+3XffkZuby3XXXcevfvUr3nzzTW666SZmzpxJz549SxXniSeeGL+tI1c8MeCgvYqPOrgSrw6quXu7b/Oq9G1eda/j3hzys2J/51EHV+KVV17ZvX3OOedwzjnnlCpu5/YHr85yaWnatGncfvvtTJ06lZo1a8Y9ZvXq1bt7bW3ZsoUZM2bQsmVLzIwlS5YAQVXQ1KlTadmyZaFzR48ezQ033LD7XK8Kci45nkRc5AYOHMjxxx/P559/TlZWFo888giXX345mzZtomfPnmRnZ/Nf//VfQPAE0bdvXyBoM+jRowft2rWjc+fO9OzZkzPOOAMzY8iQIbRt25a2bduycuVKrrvuut2/b968eQC7x3b89re/pW3btrz33nv07t27nD+9c5lNpelyeCDIycmxvLy8qMNwifhLnRRff2OKr5/Z8TcZ9UrJB5XBsltPT+n1Pf7ilTZ2SR+YWU68fd4m4lyKNNk6KaXXX5bSqzuXGE8iLm35l7Bz6c/bRJxzziXNk4hzzrmkeRJxzjmXNE8iB4BUzII7aNAgWrRoQZs2bRg6dCg7duwAfEEk51xhnkQOAKmYBXfQoEF89tlnfPzxx2zZsoWxY8cCexZE+v3vf8+kSUHDty+I5FzF5UkkFO9u/qOPPuL444+nbdu2/PrXv+aHH36Ie+7dd99N69atadOmDQMHDmTr1q1A+d3Np2IW3L59+yJp9wy0Bef7gkjOuVieRELx7uYvueQSbr31Vj7++GP69+/PHXfcsdd5K1as4N577yUvL4+FCxeSn5/P5MmTgfS5m092FlwIksvjjz++eyS3L4jknIvlSSQU727+iy++oHv37kBQPfTcc8/FPXfnzp1s2bKFnTt3snnzZo466iggPe7mE50Fd/ny5eTm5rJw4cJC+y+77DK6d+/OL3/5S8AXRHLOFeZJZB9at269e73rZ555ptDiQQUaNmzIH//4Rxo3bsyRRx5JnTp16NWrV6FjorqbL5gFd+LEiaWaBbfAmDFjWL16NX/729/2Ot4XRHLOgSeRfRo3bhz//Oc/6dSpE5s2baJatWp7HbN+/XqmTJnCV199xXfffcdPP/3EE088UeiYKO7myzILLsDYsWOZPn06Tz75ZNz1NXxBJOccHADTnkjqDdwDVAbGmtmt++vaLVu25PXXXweCqq3Y9RsKvPHGGzRt2pT69esDMGDAAN5//31+97vfAXvu5h966KG9zi24m58+fTpnnHEGzz//PM8++ywTJ05k2LBhCcc5cOBAZs2axZo1a8jKymLMmDHccsstLFu1kYZtgjaO6ke14NDTLmfnprWsnXYvh58zhu2rvmLNK3eD7QLbRc2Wv+Tyd8Xl777C17f/nip1GlCnSWsAah57AnV/MRCAnZvWsm7ay2z+0hdEcq6iy+gkIqky8A+gJ7AcmCtpqpkt2h/XX7VqFQ0aNGDXrl3cdNNNu6cjj9W4cWPmzJnD5s2bOeigg5g5cyY5OcFklwV38zNnzkzp3fyTTz65V9nFF18cdxbQKgcfyuHnjAGgWoOmHHXRvXGv+fOrpxb7+6ocfCgNzvnL7m1fEMm5iiujkwjQBVhiZksBJE0GzgJKnUTi3c3/+OOP/HHM7UBwJ/72kQ24YdQrhe7mATbUbU+dRi1QpUpUO/wYZtXvz12jSr6bb78s15c3dc5ltIxeT0TS2UBvM7sk3B4MdDWzy4scdylwabjZAvg8RSEdBqxJ0bXLg8cfLY8/Wpkcf6pj/7mZ1Y+3I9OfRBJiZg8DD6f690jKK27hlkzg8UfL449WJscfZeyZ3jtrBdAoZjsrLHPOOVcOMj2JzAWaS2oqqRpwPlB8i7Bzzrn9KqOrs8xsp6TLgekEXXzHmdknEYaU8iqzFPP4o+XxRyuT448s9oxuWHfOORetTK/Ocs45FyFPIs4555LmScQ5V2FJqiWpVtRxlJWkGpIimTbCk4hzrsKRdJmkb4CvgW8kfS3psqjjKg1JlSX1lfQ4weeIZI1qTyL7gaQTJP1W0gUFr6hjSpSkEZIOiTqOZEk6VtJMSQvD7XaS/hx1XImQdLuk2pKqhp9htaTfRR1XaYRfYCWWpZPw38cZwElmdqiZ1QN6AH0y4d+OpF9JeghYBlxMMHdgUzM7O4p4PImUUfgf5k7gRKBz+MqkUa+HE0xc+bSk3ipp4ZH083/ANcAOADNbQDBeKBP0MrMfCL7QlgHNgKsijaj0WsduhJOidooolkQNBgYUzLkHEL4/F0jrG0BJy4FbgHeBVmb2G2CLmUW2BkNGjxNJEzkEf5kZ2VfazP4saTTQC7gIuF/S08AjZvZltNElpKaZ5RbJfTujCqaUCv7/nQ48Y2YbMyWHS7oG+BNwkKQfCoqB7aT/eAszs61xCrdI2hVFQKXwLNCPoOoqX9IUINLvHn8SKbuFwBFRB1EWYQL8T/jaCRwCPCvp9kgDS8waSccQ/kcKJ+VcGW1ICXtZ0mcEd+4zJdUH9vpyS0dmdouZHQzcYWa1w9fBYfXQNVHHV4IVkk4pWijpZNL8346Z/Q/QFLgLOIlgMtn6ks6NqoOADzYsI0lvAdlALrCtoNzMzowsqFKQdAXBI/waYCzwopntkFQJWGxmx0QaYAkkHU1w53sCsB74ChhkZl9HGliCJNUDNppZvqSaQG0z+0/UcSVKUn/gTTPbGG7XJWhrSNv1DCS1BqYQVAl9EBbnAL8Azop41ot9knS5md0fs10VOA0YCJxmZoeVe0yeRMpG0q/ilZvZ2+UdSzIk/QV4NN6XrqTjzOzT8o8qcZKamtlXkn4GVDKzTQVlUcdWEkk1gMsI2tOM4EvtgXhVLelK0nwzyy5SNs/MOkQVUyLCP/vfsqdNZxEwMd3/7CV9aGYdi9l3kJltKfeYPImUnaTDCRrUAXLNbFWU8SQqbAT9xMxaRh1LsuL9p5L0gZmle+MuYdvTJuCJsOi3QF0zy5hlIiUtMLN2Rco+NrO2UcVUEkmvm1mvqONIxr6SSFS8Yb2MJJ0L3AHMImhYvE/SVWb2bKSBJSCsQvlcUmMz+ybqeEpDUkuCu8g6kgbE7KoN1IgmqlJrY2atYrbfkrRflnYuR3mS/kawTDXAcPZUEaWruIsrZYh2MR0ZYomgebN2eQfkSaTsrgU6Fzx9hI2jbxD0osgEhwCfSMoFfioozIA2nRYEXWPrAr+OKd8EDIskotL7UFI3M5sDIKkrkBdxTKU1AhgNPEVQJTeDIJGks6I3HoWY2fPlGUwpfZxuVYVenVVGRR/dwwbpj9L5cT7WAdCmc7yZ/SvqOJIh6VOCZFjwFNiYoLfNToK7ynbFnZtuJP3MzH4q+cjoSVpL0LAerz+1mdnQcg4pYenY3uRPImU3TdJ04Mlw+zzg1QjjKZVMSRb7ME/ScIKqrd3VWOn8RRCjd9QBlJWkEwh69dUCGktqD/zezNJ5CpFvMuTfRzzPRB1AUT5OpIzM7CqCLqbtwtfDZva/0UaVOEndJM2V9KOk7ZLyi6lzTVePE4zTOQ14m2CJ5E2RRpQ4i/cys68zpYsycDfBn/1aADP7COgeaUQly9jqFzO7OeoYivInkf3AzJ4Dnos6jiTdTzBNyDMEfeUvAI6NNKLSaWZm50g6y8wekzQJmB11UAl6heALTQRPUU0JqrNa7+ukdGNm3xYZaZ8fVSwJ2iDpSuA1M/ss6mAynT+JJEnSu+HPTZJ+iHltyrA7ecxsCVDZzPLN7FEyq5plR/hzg6Q2QB2gQYTxJMzM2ppZu/Bnc6ALkGntO9+GVVoWTiT5RyCtxxYRdKVeD/xF0oeSHpB0VjjWyJWSN6xXcJLeAU4lqNf+D8G0DxeaWftIA0uQpEsIngLbAuMJ6uZHm9lDUcaVrHQfY1GUpMOAewj+DQl4HbjCzNZGGliCwo4wXYE+wCnAFuB1M0vrKX/CsWk3A0eZWR9JrYDjzeyRco/Fk0jZSHrczAaXVJauJP0c+B6oBlxJcCf/j0yYfDH8AjjbzJ6OOpZkSPpDzGYloCNwqJmdFlFIpRIOVp1gZoOijmV/kdQFaG5mE6OOZV8kvQY8ClxrZu0lVQHmRXED4tVZZVd0KuwqpP9U2LH6mdlWM/vBzMaY2R8Ixl+kPTPbBVwddRxlcHDMqzpBG8lZkUZUCmaWD/xcUrWoYykLSa0k3ShpMfDPdE8gocPCm6ddAGa2k4jaorxhPUkZPhV2rCEE1RGxLoxTlq7eCOvhn6LwYMl10YWUGDMbA8ESreH2j9FGlJSlwHuSplL4z/9v0YVUMklNCCYtHEjQrvZzIMfMlkUXVan8JOlQ9sxe3Q3YGEUgXp1VRpJuyYCpr/ciaSBBA+OJFO7NdDCwy8z2mio7HUmKN9GimdnR5R5MKYUdAR4H6oVFa4AhZrYwuqhKR9L18coLEmQ6kvQvgulxJgOTzWyxpK/MrGnEoSVMUkfgPqANwXIU9QmqdheUeyyeRMpOwfKyzSk82O2d6CIqWdgW0pRglbRRMbs2AQvCx2OXQpLeJ6jTfivcPgm42cxOiDSwA5ykFwnan6YCk8zsfUlLM+HGI1ZYdd6CoAbkczPbUcIpqYnDk0jZhL2DriAY5DYf6Ab8y8xOjjSwCiJcT+G/2TPAbRbwUFT/oUpD0kdFe8HFK0tHkv5uZv8j6SXiDN5L97nXJNUBBhBUZzUnmIPtNDPLjTSwUgi7VjchplnCzCaUexyeRMpG0scE08DPMbPscHbZm82s2Ane0kk4Ed1tBGMrRISzgSZD0ligKvBYWDQYyDezS6KLKjGSXgA+JKjSAvgd0MnM+kcXVWIkdTKzDzJx7jVJA2InWZTUgGB99YFAYzNrFFlwCZL0OHAMwY1rQYO6mdnIco/Fk0jZSJprZp0lzQe6mtk2SZ+YWUaMOpa0BPi1pfniU8XJ8Lv5Q4Ax7FmUajYwxszWRxpYKYUzV2Nmq6OOJRH7WpND0s8zYcqZcPLOVpYGX+DeO6vslitYEvRFYIak9UDa/yOM8X2mJpBQvqRjCsa1KFguN92n3SgYY/G8mfWIOpZkKVgV83KCoQKStBO4z8xuiDSwMsiEBBJaSDBnXORrwvuTyH4UPtrXAaaZ2fao40mEpHsI/jG+SOE14tN5TYXdJJ1CMOhqKUFV3M+Biwoaq9OZpJnAAAvXJ88k4UDJPsClFi5FHCbwBwj+/d8dZXz7ImkzsCTeLjJkCn5JbwHZQC6F/9+We1uUJ5EkSaptZj9IqhdvfyaMUwCQ9GicYsukqbIlVSfopQJBL5Vt+zo+XUiaAnQgWMgpdoxFuddrl5akeUBPM1tTpLw+wbQhabXmRSxJnwB9i9ufCU8j6dQW5dVZyZtEMLL7A/bMxBr7MyO6C5rZRVHHUBaSagCXEdOuIOlBM9sabWQJeT58wZ4eTvEWSkpHVYsmEAjaRcIec+lseyYkin1Jp44LnkSSZGZnhD8zZoBSPJKOJaiCONzM2khqB5xpZjdFHFqiJhCMbbkv3P4tQW+ncyKLqASSzgKyzOwf4XYuwWAxAzJlLZp9Vdeme1Xue1EHkCxJ75rZiZI2UbhrdWS9Kr06q4zC6R6eBKaY2eao4yktSW8DVxGMregQli00szbRRpYYSYvMrFVJZelE0nvA+Wb2bbg9HziZYAbiRzNhtgBJ+cRUwcXuAmqYWbo/jbj9xJ9Eyu4ugiVxb5U0l2AqhZczpDoFoKaZ5arwokKZNFr9Q0ndzGwOgKSuQF7EMZWkWkECCb0btqGty5Q1LcysctQxVETFtcEWiKIt1pNIGYV1k2+HXTZPBoYB4wjm5skEayQdw56J3M4mDboNlkIn4H1J34TbjYHPw0Gg6drT5pDYDTO7PGazfjnH4jJLbBtsUZG0xXoS2Q8kHQT8muCJpCPB4kiZYjjBrMMtJa0AviIYOZ0pMmkVxgL/ljTMzP4vtlDS7wm6bLpyIKkm8P8IRqkPk9QcaGFmL0ccWrHSsQ3W20TKSNLTBMuaTiOYjjwfOM/MhkcaWCmF1SiVzGxT1LEkKnz6+8TMWkYdS2mE02wUjMv5MCzuRLCmSD8z+z6q2CoSSU8R3NlfEHYqqQm8b2bZEYeWEEkNCcZFxc6dVe4Tv/qTSNk9AtxJ0BvoUYI7+ecijagUwtH2FxBO5FbQNpIJYxXMLF/S55Iam9k3JZ+RHsxsFXCCpJPZs6jZK2b2ZoRhVUTHmNl54bIImNlmFWkcTFeSbiOo+VhEzNxZgCeRTBF2jS1Y1GYNwVOIMnAai1eBOcDHhKukZZhDgE/CbrKxA/bSehZZgDBpeOKIzvawKrqgPfAYYkZ/p7l+BFVvkcfrSSR5nxFMmHeGmS0BkHRltCElpYYFS+JmqtFRB+Ay1vUE1dCNJE0EfkGwqmcmWEowe3XkScTbRJIkqR9wPsE/vGkEXXvHpmPD176Eie9H4GUKz8GTEdO2AEg6nGA6foDcsLrIuRIpWGK2G0FvpznxRuGnI0nPAe2BmRT+f+tTwWeasEH6LIJqrZMJRlC/YGavRxpYgiQNB/4KbGDPCFizDFnlTdK5wB0Ei1EJ+CVwlZk9G2VcLjOkS+N0aUkaEq/czB6LV57SWDyJ7D/h+hDnEPTOSvtRxwCSlgJdMuUOrChJHxFMBLgq3K4PvJEJ64m4aMU0Tn/CnvZAy4T2tHDOuGbh5pIoBzd7m8h+FC4m9HD4yhRLgIybriVGpSLVV2sJ1rdwriRp0zidqHBd9ZuBoQTrFomgTedR4FqLYFloTyLuJ2B+uD5BpHWrSZomaTrB/GUQ3Fm+GmE8LnOkTeN0KdwBHAw0LRjTJak2wTCDO4Eryjsgr86q4NKpbrU0JDUjmHn4vXCd+BPDXRuAiQUrHTpXnHRqnE6UpMXAsUWXxQ0H3n5mZs3LPSZPIi7sK9/YzD6POpZESXoZuMbMPi5S3ha42cx+HU1kLlNk4g2UpC/M7NjS7kslr86q4CT9muAxuBrQVFI2cEMGNC4eXjSBAJjZx5KalH84LtOkc7LYh0WSLjCzCbGFkn5HMHat3PmTSAUn6QOCrsmzMmk9EUmLi3t0l7TEzJrF2+ecpKfN7NyCmZ6L7k/TmZ+B3V2Snwe2EMz7BZADHAT0N7MV5R2TP4m4HWa2sciUQZkw/UleMTPhXsKe/1zOxVPQ+HxGpFEkIUwSXYvMu/aqmc2MKiZ/EqngJD1C0LA4CvgNMJJg/ez/ijSwEoSj1F8gWIo19o6sGsEd2X+iis25isSTSAUXTn99LdCLoM/5dODGTFmZUVIPoKDq7ROfCdeVJGZ9cpEm65RnMk8izrkKRdLRZrY06jgOFJ5EKihJLxGnUbFABvTOci4pkj4ws06SZmbK9ETpzBvWK647ow7AuYhUkvQnoIWkvZZBMLO/RRBTxvIkUkGZ2dsF7yVVA1oSPJl8bmbbIwvMudQ7n2DerMoEU4i4MvAkUsFJOh14EPiSoGGxqaTfm9lr0UbmXMr0NrPbJFU3sxuiDibTeZtIBSfpMwqvzngMwXrfLaONzLnUkDTfzLIlfWhmHaOOJ9P5k4jbVJBAQkuBTVEF41w5+DScyPAoSQtiygu6+KbtiPV05E8iFZykBwhWdnuaoE3kHOAb4A0AM3s+uuicSw1JRxCMidqrF6KZfV3+EWUuTyIVXLiYTXHMzIaWWzDOlaN0Wh0wk3kScc5VKDGrA15E8NQtoBEQ2eqAmcyTSAUnqSkwAmhCTBuZDzZ0BypJdxN07b0yzuqAW8ys3FcHzGSeRCo4SR8BjwAfEzN7b+w4EucOJOm4OmAm895ZbquZ3Rt1EM6VIyuaQMLCfEl+V11KlaIOwEXuHknXSzpeUseCV9RBOZdCiyRdULQwytUBM5lXZ1Vwkm4BBhOMWC+ozjIzOzm6qJxLnXRcHTCTeRKp4CQtAVr5fFmuoimyOuCiKFcHzGSeRCo4SS8Cl5rZqqhjcc5lHm9Yd3WBzyTNBbYVFHoXX+dcIjyJuOujDsA5l7m8Ossh6XCgc7iZ61VbzrlEeRffCk7SuUAuwcSL5wL/lnR2tFE55zKFP4lUcOGI9Z4FTx+S6gNvmFn7aCNzzmUCfxJxlYpUX63F/1045xLkDetumqTpwJPh9nnAqxHG45zLIF6dVUFJagYcbmbvSRoAnBju2gBMNLMvo4vOOZcpPIlUUJJeBq4xs4+LlLcFbjazX0cTmXMuk3jdd8V1eNEEAhCWNSn/cJxzmciTSMVVdx/7Diq3KJxzGc2TSMWVJ2lY0UJJl7BnZlPnnNsnbxOpoMJR6i8A2yk8HXY1gumw/xNVbM65zOFJpIKT1ANoE25+YmZvRhmPcy6zeBJxzjmXNG8Tcc45lzRPIs4555LmScRVGJJM0hMx21UkrQ4HXpbmOieV5hxJF0o6qph93ST9W9J8SZ9K+ktpYikPkv5HUs2o43DpyZOIq0h+AtpIKhgH0xNYUZoLSEpmvrkLgbhJBHiMYHnibIIODk8ncf1U+x/Ak4iLy5OIq2heBU4P3w9kz8STSOoi6V+S5kl6X1KLsPxCSVMlvQnMjL2YpM7h8cdI6iTpbUkfSJou6chwbZYcYGL4tFF0IGcDYCWAmeWb2aLwuj+TNE5Sbnj9s8LympKelrRI0gvhU0xOuO9HSXdI+kTSG+HnmSVpqaQzw2Mqh8fMlbRA0u/D8pPCY5+V9JmkiQqMJEiAb0l6a//9NbgDhpn5y18V4gX8CLQDngVqAPOBk4CXw/21gSrh+1OB58L3FwLLgXrh9knAy8AJBGNsGgNVgfeB+uEx5wHjwvezgJxiYroOWE8wZuf3QI2w/Gbgd+H7usAXwM+APwIPheVtgJ0F1wYM6BO+fwF4PYyrPTA/LL8U+HP4vjqQBzQNP9NGIIvg5vJfwInhccuAw6L++/NXer58KnhXoZjZAklNCJ5Cik55Xwd4TFJzgi/kqjH7ZpjZupjt44CHgV5m9p2kNgRf6jMkAVQmfMIoIZ4bJE0EegG/DeM6Kdw+U9Ifw0NrECSrE4F7wnMXSloQc7ntwLTw/cfANjPbISl2PrReQLuY1SvrAM3Dc3PNbDmApPnhOe+W9BlcxeZJxFVEU4E7Cb6sD40pvxF4y8z6h4lmVsy+n4pcYyXBF3sH4DtABIM1jy9tMBZMu/+ApP8DVks6NLzeb8zs89hjwwRVnB1mVjDwaxewLbz+rpi2HAEjzGx6keueVHB8KB//fnAJ8DYRVxGNA8bY3rMY12FPQ/uFJVxjA0Hbyi3hF/DnQH1JxwNIqiqpdXjsJuDgeBeRdLr2ZIbmBF/eG4DpwIiCfZI6hMe8B5wblrUC2pYQZ1HTgf+WVDW8xrGSflbCOcXG75wnEVfhmNlyM7s3zq7bCZLCPBK4Czez74EzgH8QPJGcDdwWrls/n6DNBGA88GAxDeuDgc/D6qPHgUFmlk/wVFQVWCDpk3Ab4J8EyWoRcBPwCUFbRqLGAouADyUtBB5K4LM+TLACpjesu734tCfOZRBJlYGqZrZV0jHAG0ALM9secWiugvI6T+cyS02C7rZVCdo3LvME4qLkTyLOOeeS5m0izjnnkuZJxDnnXNI8iTjnnEuaJxHnnHNJ8yTinHMuaf8fxaEkkEFt7bsAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Creating a crosstab of market_segment and is_canceled columns and sorting by count of non cancelled bookings\n",
        "df_2_crosstab = pd.crosstab(df_2['market_segment'], df_2['is_canceled']).sort_values(by = 0)\n",
        "\n",
        "# Plotting the crosstab in a stacked bar chart\n",
        "df_2_crosstab.plot(kind='bar',stacked=True)\n",
        "\n",
        "# Adding the percentage values of cancellations on top of each bar\n",
        "ax = plt.gca()\n",
        "for i,x in enumerate(df_2_crosstab.values):\n",
        "    plt.text(i, (x[0]+x[1])*1.01, '{:.2%}'.format(x[1]/(x[0]+x[1])), ha='center')\n",
        "    \n",
        "ax.set_ylabel(\"Counts\")\n",
        "ax.set_xlabel(\"Market Segment\")\n",
        "ax.legend(title='is_canceled')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OqdzbDgFxtyK"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eSO2_6-IxtyK"
      },
      "source": [
        "- `Online TA` has the highest percentage of cancellation at `35.4%` \n",
        "- `Offline TA/TO` and `groups` have relatively lesser cancellations at `14.54%` and `14.64%` respectively"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I2PkB77ixtyL"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? \n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ee3BD5l6xtyL"
      },
      "source": [
        "- Identifying areas of higher cancellations could help the hotel to make the necessary changes to address these issues."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bzV1Z6fhxtyL"
      },
      "source": [
        "#### Chart - 10. How does ADR and number of bookings varies over time?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "47jEcomextyL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "outputId": "00d45dad-ffcb-40ea-8f12-f3944bc0f084"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faacf0fe6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Calculating the count of bookings by month using pd.Grouper\n",
        "# Since count is taken on 'adr' column, rename column 'adr' to 'Booking count'\n",
        "bookings_by_month = df_2[['reservation_status_date','adr','hotel']].rename(columns={'adr':'Bookings_Count'})\n",
        "bookings_by_month = bookings_by_month.groupby([pd.Grouper(key='reservation_status_date', freq='M'),'hotel']).count()\n",
        "\n",
        "# plot the output\n",
        "bookings_by_month.unstack().plot()\n",
        "\n",
        "# Calculating the average of adr generated by month using pd.Grouper\n",
        "adr_by_month = df_2[['reservation_status_date','adr','hotel']].groupby([pd.Grouper(key='reservation_status_date', freq='M'),'hotel']).mean()\n",
        "adr_by_month.unstack().plot()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DYj1U40txtyM"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zNLBZ-gxxtyM"
      },
      "source": [
        "- From these tables, we can see that there are more bookings and higher ADR for City Hotels compared to Resort Hotels. Additionally, it appears that bookings and ADR for both City and Resort Hotels peak in the months of July and August.\n",
        "\n",
        "- It also appears that the ADR for City Hotels is consistently higher than that of Resort Hotels. This could indicate that City Hotels are able to charge higher rates for their rooms.\n",
        "\n",
        "- We can also observe that there is a increasing trend in the ADR for both city and resort hotels throughout the years."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4j4odDEUxtyM"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? \n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VzR8Qi9ixtyM"
      },
      "source": [
        "- Overall, these insights can help management to make strategic decisions such as adjusting room prices and allocating enough resources to optimize revenue during high-demand periods."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U2RJ9gkRphqQ"
      },
      "source": [
        "#### Chart - 11. Is there any relationship between Lead Time and Cancellation Rate?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "GM7a4YP4phqQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "988c76d0-ee6b-4ff3-c6e3-af552ab0a60a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "38.0\n",
            "79.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Chart - 10 visualization code\n",
        "\n",
        "# Calculating z-score for column 'lead_time'\n",
        "z = np.abs(zscore(df_2['lead_time']))\n",
        "\n",
        "# Filtering values having a z-score less than 3\n",
        "df_2z = df_2[(z < 3)]\n",
        "\n",
        "# Plotting boxplot using sns.boxplot()\n",
        "sns.boxplot(data = df_2z , x='is_canceled',y='lead_time')\n",
        "\n",
        "# Calculating mean of lead time for both categories\n",
        "mean_0 = df_2[df_2['is_canceled']==0]['lead_time'].quantile(0.5)\n",
        "mean_1 = df_2[df_2['is_canceled']==1]['lead_time'].quantile(0.5)\n",
        "print(mean_0)\n",
        "print(mean_1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1M8mcRywphqQ"
      },
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8agQvks0phqQ"
      },
      "source": [
        "Here boxplot is used to visualize the distribution of a numerical variable over a categorical variable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tgIPom80phqQ"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qp13pnNzphqQ"
      },
      "source": [
        "- The Average of lead time is `38 days` for not cancelled and `79 days` for cancelled bookings\n",
        "- High lead time can increase the probability of cancellations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMzcOPDDphqR"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? \n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R4Ka1PC2phqR"
      },
      "source": [
        "- The hotel can provide regular updates to the customers having higher lead time to reduce cancellations "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d5dP_MazeSQ1"
      },
      "source": [
        "#### Chart - 12. Is there any relationship between Deposit type and Cancellation Rate?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "vYQ4INZ5eSQ1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "outputId": "fb7e0932-022f-4eec-e55d-340d7cd54fdb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Revenue by deposit type')"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Creating a new dataframe 'data' with the cancellation rate for each deposit type\n",
        "data = (df_2.groupby('deposit_type')['is_canceled'].sum()/len(df_2)).reset_index()\n",
        "\n",
        "# Creating a figure with a size of (10,5)\n",
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "# First subplot\n",
        "plt.subplot(1,2,1)\n",
        "\n",
        "# Creating a bar plot of the cancellation rate for each deposit type\n",
        "sns.barplot(x= 'deposit_type',y='is_canceled', data = data)\n",
        "\n",
        "# Adding the percentage value of cancellation rate on top of each bar\n",
        "for idx,y in enumerate(data['is_canceled']):\n",
        "    plt.text(idx,y,'{:.1%}'.format(y), ha ='center')\n",
        "\n",
        "# Adding title to the subplot\n",
        "plt.title(\"Cancellation rate by deposit type\")\n",
        "\n",
        "# Second subplot\n",
        "plt.subplot(1,2,2)\n",
        "\n",
        "# Creating a new dataframe 'data' with the revenue for each deposit type as a percentage of total revenue\n",
        "data = (df_2.groupby('deposit_type')['revenue'].sum()*100/sum(df_2['revenue'])).reset_index()\n",
        "\n",
        "# Creating a bar plot of the revenue for each deposit type\n",
        "sns.barplot(x= 'deposit_type',y='revenue', data = data)\n",
        "\n",
        "# Adding the percentage value of revenue on top of each bar\n",
        "for idx,y in enumerate(data['revenue']):\n",
        "    plt.text(idx,y,'{:.3}%'.format(y), ha ='center')\n",
        "\n",
        "# Adding title to the subplot\n",
        "plt.title(\"Revenue by deposit type\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1PYqozveSQ1"
      },
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FFLb0UMYeSQ2"
      },
      "source": [
        "Here bar charts helps us to compare cancelation rate of different segments of customers "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W9fasnFUeSQ2"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YlhZmLXzeSQ2"
      },
      "source": [
        "- Data indicates Most `cancelations` are from customers booking without deposits (`26.3%` of total bookings)\n",
        "- Most `revenue` (`99.2%` of total revenue) is also from the same segment  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "brV7SCAFeSQ2"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? \n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sv6aGqVqeSQ3"
      },
      "source": [
        "- To reduce the cancelation rates, the management could to encourage customers who are making a deposit by providing additional offers and reduced rates."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-EpHcCOp1ci"
      },
      "source": [
        "#### Chart - 13. Duration  of stay over months and weeks?\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "Ybfb1s_SxtyT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "outputId": "9b674472-3f52-4607-aa80-a90e7dfcb377"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-71-c4e6ba1f9c96>:23: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated.  Please use Series.dt.isocalendar().week instead.\n",
            "  sns.lineplot(x=data['reservation_status_date'].dt.week,y='Total_stay_duration',data = data);\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total_stay_duration Statistics\n",
            "count    86326.000000\n",
            "mean         3.554769\n",
            "std          2.439017\n",
            "min          0.000000\n",
            "25%          2.000000\n",
            "50%          3.000000\n",
            "75%          5.000000\n",
            "max         14.000000\n",
            "Name: Total_stay_duration, dtype: float64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Creating a figure with a size of (12,4)\n",
        "plt.figure(figsize=(12,4))\n",
        "\n",
        "# Creating a new column 'Total_stay_duration' in the dataframe df_2\n",
        "df_2['Total_stay_duration'] = df_2['stays_in_weekend_nights'] + df_2['stays_in_week_nights']\n",
        "\n",
        "# Filtering the dataframe to include only rows where the total stay duration is less than 15 days for better visualization\n",
        "data = df_2[df_2['Total_stay_duration']<15].reset_index(drop = True)\n",
        "\n",
        "# First subplot\n",
        "plt.subplot(1,2,1)\n",
        "\n",
        "# Creating a boxplot of total stay duration by month of reservation status\n",
        "sns.boxplot(x=data['reservation_status_date'].dt.month,y='Total_stay_duration',data = data);\n",
        "\n",
        "# Adding title to the subplot\n",
        "plt.title(\"Total stay duration by month\")\n",
        "\n",
        "# Second subplot\n",
        "plt.subplot(1,2,2)\n",
        "\n",
        "# Creating a lineplot of total stay duration by week of reservation status\n",
        "sns.lineplot(x=data['reservation_status_date'].dt.week,y='Total_stay_duration',data = data);\n",
        "\n",
        "# Adding title to the subplot\n",
        "plt.title(\"Total stay duration by week\")\n",
        "\n",
        "#Printing the summary statistics of 'Total_stay_duration'\n",
        "print('Total_stay_duration Statistics')\n",
        "print(data['Total_stay_duration'].describe())\n",
        "\n",
        "# Show chart\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X_VqEhTip1ck"
      },
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vsMzt_np1ck"
      },
      "source": [
        "Here boxplots and lineplots helps us to visualize the distribution of duration data over different months"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8zGJKyg5p1ck"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZYdMsrqVp1ck"
      },
      "source": [
        "- Average stay duration is relatively higher for the months `June` to `September`\n",
        "- The average stay duration lies between `2.8` and `4.2`\n",
        "- The average stay duration steadily increases upto approximately `35th week` then sharply decreases "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PVzmfK_Ep1ck"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? \n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "druuKYZpp1ck"
      },
      "source": [
        "- Some months have relatively higher average stay duration. With these insights the hotel management can better allocate resources to meet the customer needs "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n3dbpmDWp1ck"
      },
      "source": [
        "#### Chart - 14. Is there any relationship between Duration of stay vs ADR?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "MFPja8r8xtyW"
      },
      "outputs": [],
      "source": [
        "# Removing outlier in adr\n",
        "df_2 = df_2[df_2['adr']<1000]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "bwevp1tKp1ck",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 280
        },
        "outputId": "472effbb-0e03-4215-c189-c2b6591c0e41"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Chart - 14 visualization code\n",
        "# Total stay duration vs adr sns.scatterplot\n",
        "sns.scatterplot(x='Total_stay_duration',y='adr',data = df_2);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ylSl6qgtp1ck"
      },
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m2xqNkiQp1ck"
      },
      "source": [
        "- Here, a scatterplot is used to visualize the relationship between two numerical continuous variables (adr and total stay duration) by displaying the dependency of the two variables on each other."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZWILFDl5p1ck"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-lUsV2mp1ck"
      },
      "source": [
        "- From the chart we can observe a `negative correlation` between `ADR` and `stay duarion`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M7G43BXep1ck"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? \n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wwDJXsLp1cl"
      },
      "source": [
        "- Since customers staying for shorter periods generate more ADR management can focus attracting more of these customers to increase revenue."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YKGNM3yMPjYJ"
      },
      "source": [
        "### Multi Variate Visualization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8cZljQNkfDg_"
      },
      "source": [
        "#### Chart - 15 - What is the count of bookings and cancellations for each hotel over the years?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "tAOMYKF5fDg_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "outputId": "92d40e23-e269-440a-9503-7afc59a1df70"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "hotel             City Hotel       Resort Hotel      \n",
              "is_canceled                0     1            0     1\n",
              "arrival_date_year                                    \n",
              "2015                    5128  1321         5381  1123\n",
              "2016                   18969  7786        11968  3400\n",
              "2017                   13280  6838         8225  3271"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3ed805df-8c7a-4683-917e-de438de1c5e3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>hotel</th>\n",
              "      <th colspan=\"2\" halign=\"left\">City Hotel</th>\n",
              "      <th colspan=\"2\" halign=\"left\">Resort Hotel</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>is_canceled</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrival_date_year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>5128</td>\n",
              "      <td>1321</td>\n",
              "      <td>5381</td>\n",
              "      <td>1123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>18969</td>\n",
              "      <td>7786</td>\n",
              "      <td>11968</td>\n",
              "      <td>3400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>13280</td>\n",
              "      <td>6838</td>\n",
              "      <td>8225</td>\n",
              "      <td>3271</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ed805df-8c7a-4683-917e-de438de1c5e3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3ed805df-8c7a-4683-917e-de438de1c5e3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3ed805df-8c7a-4683-917e-de438de1c5e3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Chart - 15 visualization code\n",
        "# create pivot table with year as index and hotel and is_canceled as columns, counting the number of rows\n",
        "pivot_1 = pd.pivot_table(df_2, index=['arrival_date_year'], columns=['hotel','is_canceled'], aggfunc='size')\n",
        "\n",
        "# plot the pivot table using bar chart and set color codes for the bars\n",
        "pivot_1.plot(kind ='bar',color=['#0ebded','#1c6a80','#0bb52a','#077319'])\n",
        "pivot_1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zZVcvF_ifDhA"
      },
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9o_CjEcLxtya"
      },
      "source": [
        "- We can observe the trend in number of bookings and cancellations effectively using stacked bar charts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hq6nAkKWfDhA"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DTbT2NrGfDhA"
      },
      "source": [
        "\n",
        "- There seems to be an overall increase in the number of bookings in the year 2016 followed by a decrease in 2017.\n",
        "- The number of cancellations also seems to follow the same pattern. \n",
        "- The data suggests that City Hotel is more popular among customers but also has a higher cancellation rate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dGNbNPHSfDhB"
      },
      "source": [
        "##### 3. Will the gained insights help creating a positive business impact? \n",
        "Are there any insights that lead to negative growth? Justify with specific reason."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBr77pipfDhB"
      },
      "source": [
        "\n",
        "- The data suggests that Resort Hotel is less popular than City Hotel.If the hotel is not able to attract more customers and increase its popularity, it may struggle to grow occupancy and revenue. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NC_X3p0fY2L0"
      },
      "source": [
        "#### Chart - 16 - Correlation Heatmap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "xyC9zolEZNRQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "outputId": "9b08b757-dfaa-4451-b44e-aed40dfb5a71"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.matrix.ClusterGrid at 0x7faaced4b100>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 792x720 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Correlation Heatmap visualization code\n",
        "# Droping number of day, month, year as their numerical correlation does not add value to our analysis\n",
        "df_3 = df_2.drop(['arrival_date_day_of_month', 'arrival_date_year',\n",
        "                  'arrival_date_month', 'arrival_date_week_number'], axis=1)\n",
        "\n",
        "# ploting cluster map using sns.clstermap()\n",
        "sns.clustermap(df_3.corr(),cmap='RdBu',center=0,figsize=(11, 10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "Lw3auDL7OjZj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "outputId": "6ea6bb6f-c9aa-4cdf-f7ae-02dc081faf3d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 792x720 with 4 Axes>"
            ],
            "image/png": 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s8zzlq4vf4+fxyehXycrKpOcFF9PvyhtypE/56hNm/fgdQcHBHNOgITfd+yjh0U0A+NeFZ9As1vPZh0VGc9eQl/yq87cFv/D+qP+QlZXFWX370//qf+RIn/jFx8yY9C1BwTWo37AhtzzwOJHRTfhz3RrGjniRvXt2ExQUzCXX3MhpZ51bZH0nNWvIv3rEEmTG/1Yl8sVvW3Okn9M2kpu6tyDV+3c4cflfTFmdxAlN6vPP02Kz8zVrWJuXp65h3sb0QuuLnzeX0a8OJysriwsuvoQrr78xR/qyJb/y1ohX+GP9OgY//Rw9z+qTI3337gxuvfZKevQ8kzvuf7jI6yuPeyhVx4Jf5jLy1eFkZWbSr/8lXH3DTTnSly7+lZGvDmfD+nU8/szznHl2nwLOJFJ1FD+AyKrc35mdc5OASbmOPemzvQ+4ooCyzwHPlWkDK7D3v5vDqM+mMm7o0XkhXJDBHWe04rHvl5Oy+wCvXtaZeRvT2Jy+N0e+metTeHN2zuCgffQxdGh8DHd+vgSAlwecwAlN67Ns685i1T/44o7cPm4BiTv38dHtPfh5ZRIbko+8VOiVSauyt686tQXtmtYv9jU+fmkn/jVmPn/t2Mtnd/dk+opENiQeqeOlCUdWEb7m9Fjax+Ss464L2rFoQ5rfdc6av4iNCdv44YM3WbpyDc+8OppPR72cJ1/v07pxzSX96Hv9HTmOX3hOL/7e/wIAps1ZwEtvjmXMi0/5XX9VkpWZyUcjh3Pf8yNoFBHFs3ffTNfuPWnaomV2nmNbt+Xx18YSEhrK9O+/4vOxo7jtkaEA1KoVwlMj3yvo9AXWOe71l3nkxdcJj4zi8Ttv5KQePWnWolV2ntjj2vLsqPcICQ3lfxO+5JMxb/DvJ54jJDSU2x9+iibNjiU9JZnH7vgHnbudSt16xxRYX5DBbWe05ImJK0jdfYD/XHoC8zems3l7zr/DWRtS8wTpy7bt5O6vlgJQL6QGY/5+Iou3FP4Cx8zMTEa+8iLPvzqSiKho7v7nDXQ/oxctWh65vqjoxtz/2BC+/OSDfM/xwdujOaHriYXWc1h53EOpOjIzM3ntlWG8NGIUkVHR3DHwek7reSaxvr+vjRvz0BNP8/lH+f++ilRF1W0Ik5TC7F/XkL5j91E7X9uoemzduZe/du3nUJZj5voUTosN86usw1EzOIgaQUHe/xrb9xwsVv2dmjVkc9puEtL3cijTMXnpNnq3jyow/wWdm/BjriezRTnh2IZsSt3NlrQ9HMp0/LAkgbM7RheYv9+JTZm0+EgdHWIaEF6vFnPXJPtd57S5C+h/bm/MjC4d2rErYzfJqXkDkC4d2hEZnvfzrle3Tvb23n37/HpBY1X1x5oVRDVtRmSTGGrUrMkpZ/ZhybxZOfIc3+VkQkJDAWh9fEfSU5JKVee61SuIbtqM6KaeOk/rfS6L5szMkadj17jsOtu070Sat84mzY6lSbNjAWgUEUn9ho3Yub3w3oA2kfXYtmMfiT5/h91jGxW73ae3DGPR5nT2Zxb+7/Calctp2qw5TWKaUbNmTc485zzmzfo5R57oJk1peVwbzPL+L2rtqpWkp6VyUrdT/WpXedxDqTpWrVhOTLPmNPX+vp7V5zzmzpyRI0/jJk1pfVwbrIxWqTSPYn9fEylLxf+FdFkV+0cqjfC6IaRkHMjeT8k4QHjdWnnynd4ynJFXdOHRc9sR4U1flZjB0oQdfHhDHB9eH8eiLdvzPDEtSlT9UBJ37MveT9y5j8gGofnmbdIwlKZhtVm4IbV4dTSozV/bferYvo+oBrXzr6NRbWLC6jB/XQoAZvBg/w4M/35lsepMSkmjcVRE9n50ZDiJKf73YAB8/M0kLrj2Vv4z5j0eHXR0epwqo/SUZBpFHgn4GkVEkp5acDA3a8r3nBB35IvtwQMHGPrvgTx/z79YPPfnAsvlrDOJ8KgjdYZFRpFWSJ3Tf5xAl26n5Tm+btVyDh06RHTTZoXWF163Fim792fvp+4+QHjdkDz5erQM47W/dWZwn7bZf4e+eraOYOb6lELrAkhJTiLS5/oioqJITfbvC3tWVhZvv/F//HPQPX7lh/K5h1J15P59jYyKJiXZ/wc6JWVmsWa22szeB34HnjCzhWa21Mye9uYZZmZ3+pQZYmYPeLcfzCd/rJmtNLO3zWy5mU0xs9retBlmFufdjjCzP73bwWb2ss+5bi3zi5dKoVrNgZDKZ/6f6cxYm8KhLEff9tHcf3YbHvluOU3qh9K8UR1u+CAegOcu6kjHxsew/K9dZdKO809oytTf/yKrDFcx7tu1KVOWbsuu46oescxcmZQjyAmUay7pxzWX9OP7qT8z+sPPeWHw3QFvQ2Xzy7Qf2bhmFQ++NDL72IvvfUWjiEiStyUwfPBdxMS2JqqIL/TFMfunH/hj9Uqe+M/oHMfTU1N4c9gQbnvoSYKCSv/gcsHGdH5e5/k7vKB9FPf0Po7HJx4Zeteodk1iw+rw6+bChy+V1vdffU63007P8YXuaCqPeyhSiDZ4FnWpj2dRl1PwvDN3gpn1Aj4DXgUO/8JeCZxvZud5y+bOv8l7/Grn3L+88zr/BnxYSBtuBnY457qZWQgwx8ymeF+oK9WYAohSiIuLGwzk/8i6UuhcrrWn7t5PRL0jTzIj6tXKnqR52K79h7K3J69KZOCpLQDP09DVibvYd8jz+xi/OZ32xQwgknbuI9qnxyG6fijJBXxZP79zE4Z9t9zvc2fXsWMvjRv61NEwlKQd+feU9O3alOe+OjJJukuLRpzcMoyrerSgTkgNagYbe/Yf4lWfeRmHffzNJL6YOAWATu3a8FfSkSfBicmpREf4NzQst35n9WToq2+VqGxV0CgikvTkxOz99JRkGoVH5sm3YvFCJn76Hg+9NJKatWrlKA8Q2SSGdp1PYtP6NUV++WwUEUVq0pE605KTCMunzmWLFvDNx+N54pU3c9S5Z3cGLz92H1cOvI02HU4o8hpTdx8gwqfHIbxuLVJ9eiQg59/hlFVJ3Ni9RY70M1qH88ufaWT68Z6giMgokn2uLyUpifDIgocO+lr5+zKWL13M9199wb69ezh48BChdeow8Pa7CixTHvdQqo7cv6/JSYlEROb9/SkuP99ZtdE5N8/MhgPnAYu9x+sBbZxz75pZlJk1BSKBdOfcZjO7O7/8eAKIP5xzS7zHFwGxRTT1PKCzmV3u3W/gPZcCiGqu+C+SyzxUdKbqIzQ+Pn5IeTeipGqdOLBcZ8auScqgaYPaRB8TQuruA/RqHcFLU9fkyNOoTk3SvXMburcIyx6mlJyxn/PbRxO0GMyME5rU55tl24pV//KEHRwbXpemjWqTtHMf53duwiP//S1PvtiIutSvXYPfNm0v9jX+vnkHx0bUJSasNok79tG3awwPffRrnnwtI+tSv3ZNlvisXjP448XZ2wPimtGxecN8gwc40mMA8PO8eD7+ZhL9zu7J0pVrqFe3br5zHQqycctWWjRrmn2uFjFN/C5b1cS2bU/i1i0k/7WVRuGRLPj5J/718JAceTatW80Hr73IPc/+H/UbHvmcd+/aSa2QUGrWqsWuHdtZt2IpF1x+bZF1tm7Xnr8SNpO0bSthEZH8MuN/DHp0aI48f65dzbuvDuPhF16lQaMjdR46eJD/G/IwPc/tS/de5/h1jWuTM2jaIDTH3+HwaWtz5GlUuybpez1/h6e0CMuz0EGv1hG8v2CTX/W1Pb4DW7ds5q+tCYRHRvHz1Ck8/NSzfpV9eMiRfP+b+B1rV60oNHiA8rmHUnUc374DCZs3s21rAhGRUUz/aQqPPV36tVSKeGfVYYcnHRrwgnMuv6c5n+PpnWiMp0eiwPxmFgv4Ph3IBA6PqT3EkWHtvg9GDbjLOTcZER/qgRC/ffDCrfQ6uR0RDeux4cfhPDP6W8Z/M6voggXIcvDm7A08e2EHgsyYsjqRTel7uS6uOWuTM5i/MZ0BnZrQPTaMzCzHrv2H+M/0dQDM3pBK55gGjLqyKzhYtHk7C4pYOjK3zCzHi9+tYNSN3Qgy49tft7AhKYPbz2nDioQd/OxdLvb8zk2YvLR4wYlvHc9/vZy3/tWdYDO+XriZ9YkZ3Hl+W5Zv3sGMFZ4nW31PjOGHJcWboF2QXt1PZub8RfS97jZCQ0N49qF/Z6dd9q97+OrtVwEY/tZ4Jk2dxb79+zn7ypv5W78+3Hnj1Xz8zSR+WfQbNWoEU/+Yejz/cPUdvhQcXINrbr+PVx+/l6zMTE4/7yJiWrTim/ffJrbt8XQ9tSefvzuSffv2Mvr5x4EjS31u27yRD15/EbMgnMui75XX51j5p7A6b7zrAYYN/jdZWVn0vuBimsW24vPxb9GqbXtO7tGLj8a8zr69e3ht6KMAhEc15oGhw5n380+sWrqYjJ07mDllIgC3Pvgksce1LbC+LAej5/zB0309yxn/tDqJTel7ufbk5qxNyWDBxnQu7tSE7i0akek8f4cjZqzLLh9VL4TIeiH8vs2/FdCCa9Tg9nsf5PH77iIzM5PzLupPi1atef/t0bQ9vj2n9jyT1SuXM/SRB8nYtZP5c2bx4TtjeOuj//p1/vw+z0DfQ6k6gmvU4K77H+LhewaRlZVJ34sGENuqNePGvEm79h3o0fNMVq1YzlODHyBj105+mT2L9955i7Eff340mzEZGGpmHznnMswsBjjonEvCEzS8DUQAZxaWv4g6/gROxvN+q8t9jk8Gbjezac65g2bWFkhwzh29FVWkUjLnR5ezr4OJf5ThKPDSqxndMmBLxsTFxQ2p5D0QAb2XfW4N7GTcbWU8Hju3g/sDv8Txknvzvt+hrNWIaR/wOgNlVjEnyZdWnZrBAa0PYMgPxZuUX1ojLusU0PoSdh4oOtNR1tP7rhipnLakZRSd6ShrFlav0O8q3t6C751znbz7dwOH/yeaAVznnFvvTVsGpDjnzvIpnyc/nh4H33M+ANRzzg0xs+OB/3rzTPSeP9a7+tOzwMV4eiOSgUucc4H9H6xUOOqBEBEREalAnHN/Ap189kcAIwrIm2eyUyH5fc853Gd7FTknRj7uPZ4FPOr9EclW/DkQWZoDISIiIiJSXakHQkRERERE/KY3G4qIiIiIiN80hElERERERPxW/CFMWRrCJCIiIiJSXZVgDkSFXsVVRERERETKkCZRV2OBfi/DT2+9E9D6Lr37toDWt2jBloDWB+CCawW8zqqsZcOQgNeZuFvDQo+mYM3sExEpc8WfA6EAQkTkqDm5ecOA1vfdLacFtL5A+ysjsC8DlMqvWVi98m6CSKVTgjkQelomIiIiIlJdlSCAyCyDZoiIiIiISGVQ7AAiqO3pVhYNERERERGRik/TzURERERExG8KIERERERExG8KIERERERExG8KIERERERExG/FX4VJqrSTmzfk1tNbEmQweWUSny9JyJHep10kN58aS8ruAwB8//s2Jq9KAmDgqS3odmwjzGDxlh28NeePUrdnzFM30a9XF5LTdnLiFU+W+nxdYhpw4ynHEmTGtLXJfLtsW470M4+L4Lq45qTt8Vzf5JVJTFubDMAnN3Rj0/Y9AKRkHODlaWv9qrNXh2gev6IzwWb8d+6fvDVlTY70x/52At3bRgJQu1Yw4ceEcNID3wPw0KWdOKtjYywI5qxMYujnS4uszznHC6+9xaz5CwkNCeG5R+6jQ9vj8uQb8fZ7TJg8lZ0ZGSz88avs4/G/LePF18ewZsMfvPzkYM7rfYZf11ldLPhlLiNfHU5WZib9+l/C1TfclCN96eJfGfnqcDasX8fjzzzPmWf3KaeWymHLFs7j49GvkpWZSa++F3Ph32/IkT75y0+Y+eN3BAUHc0yDhgy871EiopuQkriN1595BJflyDx0iD4DLuesiy4tp6sQEak4ShRAxMXFDQZCj3JbKqPY8m7A0RRkcMcZrXjs++Wk7D7Aq5d1Zt7GNDan782Rb+b6FN6cnTM4aB99DB0aH8Odny8B4OUBJ3BC0/os27qzVG16/7s5jPpsKuOGlv6t2WYwsHsLnpuymtQ9B3jhoo7Eb0onYce+HPnm/pHGuPkb85Q/kJnFwxOWF6vOIIMhf+/CP16bzV/b9/LVw2cxdek21v21K7QLT1oAACAASURBVDvPc18uy96+vncrOjTzvFjsxFZhnNwqnAuf+wmAz+4/k+5tIpi/NqXQOmfNj2fTlgQmffQOS1esZuh/3uCT0a/myde7R3euuexi+l2b87NtEhXFs4/cx/hPvyzWtVYHmZmZvPbKMF4aMYrIqGjuGHg9p/U8k9iWrbLzRDVuzENPPM3nH31Qji2Vw7IyM/lg5HAeeGEEYRFRPHPXzXQ9tScxLVpm5zm2dVuefH0sIaGhTPvuK/77zijueGwoDcMiePz/xlCzVi327d3D47deR9fTzqBReGQ5XpFUR2Z2DzDGObenjOt5BpjpnPupLOvxox2xwPfOuU5H+bzjvef9opjl+gMdnHPDCsnTG3jAOXdRPmkBuX+BVNIeiND4+PghR7MhlVFcXNyQ8m7D0dQ2qh5bd+7lr137AU+gcFpsGJvTE4ooCQ5HzeAgagQFYQY1goztew6Wuk2zf11DiybhpT4PwHER9UjctZ+kDM/1zf0jlW7HNiIhVy/E0dQlNoyNybvZnOr5N2Pioi306dIkRwDh6+K45oz4fqVnx0FIzSBq1gjCMGoEB5HivTeFmT57Hv3PPwczo0vH49mVsZvk1DQiw8Nytq3j8fmWj2kSDUBQkEY45rZqxXJimjWnaUwzAM7qcx5zZ87IEUA0btIUAAvSitcVwYbVK4hq2oyoJjEAnNK7D4t/mZUjgGjf9eTs7dbtO/LLtMkA1KhZM/v4oYMHcVkuQK0WyeMe4EOgTL+AOudK39VfBTnnJgATSnGKgNy/QNI3BMkWXjeElIwD2fspGQcIr1srT77TW4Yz8oouPHpuOyK86asSM1iasIMPb4jjw+vjWLRlO5u3781TtjyF1alJ6u4jX8BTdx+gUZ2819e9RSNe6t+Je3sfR7hPes3gIJ6/qCPPXtiBuGMb+lVndMNQtvn04PyVvpfoBrXzzds0rDbNwuvyy2rPkLDFf6Qxb00yv7zQj1+G9WPWykTWFxB4+EpMSaFx1JEnpNGRESQmF95rIf5JSU4iMio6ez8yKpqU5ORybJEUJT01mbDII/csLCKS9JSC79nMH7/nhG6nZu+nJiXyxG3Xc/91l9DvyuvU+yBlzszqmtlEM/vNzH43s6eApsB0M5vuzfOmmcWb2XIze9p77Gwz+8bnPOea2ddmFmxm473nWmZm9xZS93gzu9y7/aeZPW1mv3rL5f/UyZN3mZk1NI9UM7vBe/x9bzuCzexlM1toZkvN7Fafsg/6HH86n3O3MrPFZtbNzFqb2Y9mtsjMZh1uk7fdr5nZXDPb4HMNZmZvmNlqM/sJiCris8/3ms3sRjN7w7vd2szmedOfNbMMn1PUM7MvzGyVmX3krf/fvvevOPejItMciACpiMO+ov75WrHLzP8znRlrUziU5ejbPpr7z27DI98tp0n9UJo3qsMNH8QD8NxFHenY+BiW+/GFtyJZtHk7czakcijL0adtJHf0bMXQyasAuPOLJaTvOUhUvRCeuOB4NqfvJdGPHgF/XXRyc35cnMDhh5wtIuvSunF9znjsBwDeu+sM4lqHE78+9ajVKSJHzJ36I3+uXcXgl0dmHwuPimbo6A9IT03m9SGDiet5Fg0ahRVyFpGimdktwC0+h8Y458Z4ty8AtjrnLvTmbQDcBJzlnDv8NOgx51yamQUDU82sMzAdGGVmkc65ZG+ZsUBXIObwcCAz8+8JmEeKc+4kM7sDeAAoaDzxHOB0YCOwAegJvA+cBtwO3AzscM51M7MQYI6ZTQHaeH9OAQyYYGa9gE3etrYDPgVudM79ZmZTgducc2vNrDswCjjb24YmwBnA8Xh6C74ALgXaAR2AaGCF9zMpzTWPAEY45z4xs9typZ0IdAS2Hv5MnHOvmdl9eO+fmZ1Mye9HhaEAInAq3LCvfqPnPuW7n7p7PxH1jjxxj6hXi9TdB3KU2bX/UPb25FWJDDy1BQA9WoaxOnEX+w5lARC/OZ32FSyASNtzkPC6Idn74XVrkb4n5/Vl+Fzf1LXJXBvXPHs/3TskKyljPyv+2klsWJ0iA4jE7fto0uhIj0PjRrVJ3JF/z8xFcc146rMl2fvndmnKkj/S2LM/E4CflydyYquwfAOIT77+ji++9wy76NSuDX8lHXnCmpicQnRkRKHtFP9EREaRnJSYvZ+clEhEpJ5IV2SNwiNJSz5yz9JSkmkUkfeeLf91Id9/8h6Dh4+kZq28PZONwiOJiW3Fmt+X0K3n2XnSRYrDGyyMKSB5GfCKmb2IZ7z+LLM8QyKv9AYhNfB8ce7gnFtqZh8A15nZODxf3m8AjgFamdnrwERgSjGaeniFjUXAZYXkmwX0whNAvAncYmYxQLpzbreZnQd0PtwzADTAEzic5/1Z7D1ez3t8ExAJfAtc5pxbYWb1gB7A5z6fx5H/qcM3zrksYIWZHe527AV84pzLBLaa2bSjcM2nAZd4tz8GhvukLXDObQEwsyV45srOzlV+AyW/HxWGhjBJtjVJGTRtUJvoY0KoEWT0ah3BvD/TcuRpVOfImODuLcKyhyklZ+ynU9P6BBkEBxknNKnPpvSKNYRpfUoGjeuHEFmvFsFBRo+W4cRv3p4jT8PaR64vrnmj7AnWdWsFU8M7pv2YkBq0izqGLX4M0Vq6MZ0WUfVoFl6HmsHGhSc3Y+rSvHMuWkXXo36dmizecOTz3pq+h1PaRBAcZNQIMk5pE1HgEKarL72YL999gy/ffYOze57GhMlTcc7x2/JV1KtbN8/8BymZ49t3IGHzZrZtTeDgwYNM/2kKPXqeWd7NkkK0bNeepIQtJP+1lUMHD7Jgxk+ceGrOlcU2rlvNe6+9yL+ffon6DY/8raQlJ3Fgv+chwe5dO1m7fCmNm7UIaPul+nHOrQFOwhNIPGtmOeYlmFlLPE/Gz3HOdcbzJfTwCIdxwHXA1cDnzrlDzrl0oAswA7gNeKcYzTn8lCyTwh86z8TT69DTW08ycDmewAI8vQt3Oee6en9aOuemeI+/4HP8OOfcu94yO/AEEof/YIOA7T55uzrn2ufT1sP1lZS/11xY2QLLl/J+VBjqgZBsWQ7enL2BZy/sQJAZU1Ynsil9L9fFNWdtcgbzN6YzoFMTuseGkZnl2LX/EP+Zvg6A2RtS6RzTgFFXdgXnGQq0YGN6qdv0wQu30uvkdkQ0rMeGH4fzzOhvGf/NrKILFnB9Y+dt5NFzjyfIYMa6ZLZs38sVXWPYkLqbRZu307d9NCc3b0iW8/RGjJq9AYCYBrX5V49YnPOs5vTtsq15Vm/KT2aW4+nPljBu0OkEBxmf/7KRtdt2cfdF7fl943ameidwXxTXnInxW3KU/fHXBE5rG8XEx88BBzNXJDJt2V9F1tnr1G7MmreQvtfcTO2QEIYOPjK88m83D+LLd98A4JU332XS1Bns27efcy6/nssuPJ87b7qOZSvXcM8TQ9m5K4MZc+czctyHfPveaL8/56osuEYN7rr/IR6+ZxBZWZn0vWgAsa1aM27Mm7Rr34EePc9k1YrlPDX4ATJ27eSX2bN47523GPvx5+Xd9GorOLgG1955H688ei9ZWZn0PO8iYmJb8fV7bxPb9nhOPK0n/317JPv37mXUs48DnmFLdz/9Ets2/cmnb7+OYTgcF1x+Nc1bti7nK5KqzsyaAmnOuQ/NbDueITS78PQkpAD1gd3ADu+T9r54vozinNtqZluBx4E+3vNFAAecc1+a2Wo8k3mPKufcZm89tZxzG8xsNp4gZ5A3y2TgdjOb5pw7aGZtgQTv8aFm9pFzLsPba3F4BZYDeIYgTTazDOfcx2b2h5ld4Zz73DzdEJ2dc78V0rSZwK1m9h6e+Q9n4ek1KI15wN+Az4Cr/CyTff8CcT8CwZwr/qoScXFxQyracJzyUJzPoSJ+Zv1Gzw3okiI/vRXYIPvSu3MPTSxbixZsKTrTUbbyyS4Br7Nm46r7BWpLWkbRmY6yZmH1Al5nVTb3z8DPEeoRe3RWipNqpcAn5GZ2PvAykIXny/TteIbNDMIzN+Is8yxH2gPYjOdJ/QTn3Hhv+auAe5xzp3r3u+DpmTg86uQR59wPBdQ9Hu8yp2b2JxDnHbcfBwx3zvUupN0fAMHOuWvMrAeeoTuRzrlUMwsCngUu9l57MnCJc26Hmd3NkXkGGXh6UDK97ejknSPwP2Aonl6ZN/EM26oJfOqce8ZyLc/qDTjqeYOM14Fz8fRmHATGFrSMa0HXbGY3eo8PMrM2eL701wZ+BK51zsVYrmVcvZOu451z483sLrz3D8+KTH7dj4pMPRAiIiIiFYRzbjKeJ/O+4vF8ET6c58ZCTnEG8LZP3t/wDInyp+4bfbZjfbbjgd5FlL3eZ3suPsPkvXMTHvX+5C43As/E5Nw6edO3A918jl9QWLu9+/W8/3Uc6QUpUkHX7A3OxnuTEoBTnXPOG6y18+aZgbcnyLs/yGf7dXzuH37ej4pMAYSIiIhIFWBmi/AMb7q/vNtShZ0MvOHt3dgODCzn9pQLBRAiIiIiVYBz7uSic4GZjcSz7KqvEc65cUWUuwm4O9fhOc65O/1vZfkzs6+BlrkOP+zt/SmUc24WnknQ1ZoCCBEREZFqpKRf+L0BRqFBRmXgnLu0vNtQ2WkZVxERERER8ZsCCBERERER8ZsCCBERERER8ZvmQFRj2zbvCGh9gX4vw9cjAvvys5BjyuNtz9V+HtdRlVXeDZBSS8w4UN5NEBGp8tQDISIiIiIiflMAISIiIiIiflMAISIiIiIiflMAISIiIiIiflMAISIiIiIiflMAISIiIiIiftMyrqWzLy4uboifeWPLsB0iIiIiIgGhAKIU4uPjh/mbtxiBRoXRo00ED17YnqAg45v4LYybuSFH+v39jqdbq3AAQmsGE1a3Fr2e/alYdXSJacCNpxxLkBnT1ibz7bJtOdLPPC6C6+Kak7bHs7b75JVJTFubDMAnN3Rj0/Y9AKRkHODlaWtLdJ2+xjx1E/16dSE5bScnXvFkqc8H0OeUdrz07wEEBQXx/sT5/Oej6TnSm0c3YtTgK4loWJf0nXv557MfszXZ846O7dNfYvkGz2eyJWk7f39kXJH1Oed44bW3mDV/IaEhITz3yH10aHtcnnwj3n6PCZOnsjMjg4U/fpV9/L3PvuLLiZMJDg4mrGEDhj58D00bR5fmI6jUFv4yl1GvDicrM5O+/S/hqhtuypG+dPGvvPnqcDasX8djzzxPr7P7ZKc9cs8gVi5fRqfOXXn2lRGBbrp4rV48n+/GvoHLyqTbORfS+7Jrc6TPm/wtv/z4DUFBQdQKrc1ltz1AdPNYdu/awUcvP8WW9as4ufcFDPjXPeV0BSIiFYsCCMlXkMHgizty+7gFJO7cx0e39+DnlUlsSM7IzvPKpFXZ21ed2oJ2TesXqw4zGNi9Bc9NWU3qngO8cFFH4jelk7BjX458c/9IY9z8jXnKH8jM4uEJy4t5ZYV7/7s5jPpsKuOG/vOonC8oyHjl3ksZcN8YEpJ38POYu5k4ewWrNyZm53nujov4ZPIiPv4xnl4nHceQW/pxy3OfALB3/0FOv/n/ilXnrPnxbNqSwKSP3mHpitUM/c8bfDL61Tz5evfozjWXXUy/a3Nea/s2rflszAhqh4by6TcTeWX0WF4Z8kgJrr7yy8zM5PVXhvHiiFFEREUzaOD1nNbzTFq0bJWdJ6pxYx584mk+/+iDPOWvuPYG9u/bx8Rvvgxks8VHVmYm3749gpufHE6D8EjeePg22nc7nejmsdl5uvbsw6nnDwBgxcI5TBw/koFPvEzNmrU47+qB/LXpDxI3/VFOVyAiUvFoDoTkq1OzhmxO201C+l4OZTomL91G7/ZRBea/oHMTfvxta7HqOC6iHom79pOUsZ/MLMfcP1Lpdmyj0ja9VGb/uob0HbuP2vni2h/LhoRU/tyWxsFDmXw5dQkXndExR57jY6P5+VdP78nMX9dxYa704po+ex79zz8HM6NLx+PZlbGb5NS0PPm6dDyeyPC8b88+5aQu1A4N9eTpcDyJySmlak9ltnrFcpo2a06TmGbUrFmT3n3OY+7MGTnyNG7SlFbHtcGCLE/5k7qdQp26dQLUWsnP5nWrCG8cQ3jjptSoWZMuZ5zNioVzcuQJrVM3e/vAvn2epxtArdDaxLbvTI2atQLaZpHczOweMyvzf0zM7Bkz61N0zjJvR6yZ/X4UzjPJzBoWkWeGmcXlc7yrmfUrbRuqKgUQkq+o+qEk+vQEJO7cR2SD0HzzNmkYStOw2izckFqsOsLq1CR19/7s/dTdB2hUJ+//qLu3aMRL/Ttxb+/jCPdJrxkcxPMXdeTZCzsQd2yh/z6UmyYRDUhI2p69n5C8nSaRDXLkWbZuK/17nQBA/16dqF83lLD6nv9PhNaqwc9j7mbam3flCTwKkpiSQuOoyOz96MiIEgcBX02aTM/uef5drTZSkpOIjDoyfCsiKpqU5ORybJEU1860ZBpEHPl7aBAWyc7UvPfwlx++5qU7ruGHD0bTf+C/A9lEEX/cA5R5AOGce9I5V7yxyBWYc66fc2570Tnz1RVQAFEABRBSauef0JSpv/9Fljv65160eTuDvviNhyb8zrKtO7ij55GhI3d+sYRHv1/Oaz+v5x+ntCD6mJCj34AAeGzU95zRtTWz37mX07u2JiFpO5lZWQB0uPI5zrxlBAOf+Yhhdw2gZdPwgLXruynTWL56LTdddXnA6hQpL6f1vZSHRn1M3+tvZdqXeYejiQSKmdU1s4lm9puZ/W5mTwFNgelmNt2b500zizez5Wb2tPfY2Wb2jc95zjWzr80s2MzGe8+1zMzuLaTu8WZ2uXf7TzN72sx+9ZY7vpByy8ysoXmkmtkN3uPve9sRbGYvm9lCM1tqZrf6lH3Q5/jT+Zy7lZktNrNuBdR9o5l9ZWY/mtlaM3vJJ+1PM4vwbj9hZqvNbLaZfWJmD/ic5gozW2Bma8ysp5nVAp4B/m5mS8zs72Z2pnd7ibc9xxT0eVQHmgNRycXFxQ0G8u8aKMr5QwtMStq5j2ifHofo+qEk55qbkH2azk0Y9l3x5yKk7TlIeN0jX/rD69Yi3TtZ+rCM/Yeyt6euTebauObZ++l7DnramrGfFX/tJDasDom79lORbEvZQUzUkd6RmMiGbPNOkD7sr9SdXPv4ewDUrV2LAb1OYEfGPm/5nQD8uS2N2UvW07lNDH9szdvT88nX3/HF95MB6NSuDX8lHXnCmpicQnRkRLHa/Uv8YsZ88BnjX3uRWrVqFqtsVRIRGUVy0pH5KilJiURERhZSQiqa+mGR7Eg58vewIy2Z+uEF38POp5/N12OKN+9IpLjM7BbgFp9DY5xzY7zbFwBbnXMXevM2AG4CznLOHe5Ofsw5l2ZmwcBUM+sMTAdGmVmkcy7ZW2YsnifpMc65Tt7zFafLPsU5d5KZ3QE8ABQ0QXAOcDqwEdgA9ATeB04DbgduBnY457qZWQgwx8ymAG28P6cABkwws17AJm9b2wGfAjc6534rpJ1dgROB/cBqM3vdObf5cKI3+Pgb0AWoCfwKLPIpX8M5d4p3yNJTzrk+ZvYkEOecG+Q9x3fAnc65OWZWD8j/S1E1oQCi8guNj48fUpKCJz72w1MFpS1P2MGx4XVp2qg2STv3cX7nJjzy37x/u7ERdalfuwa/bSp+D+H6lAwa1w8hsl4t0vYcpEfLcF6buT5Hnoa1a7J9rydQiGveKHuCdd1awew/lMWhLMcxITVoF3UME3Kt4FQRLFq1mdbNImjRJIytyTv42zldGfjMRznyhDeoQ9rOvTjnuP/as/lg0kIAGtarzZ79BzhwMJPwBnXofkIs//fxjHzrufrSi7n60osB+PmXBXzy1Xf0PedMlq5YTb26dfOd61CQlWvW8/Qrr/PWy0MJb1Qxh4YFSrv2HUjYvJltWxOIiIxixk9TeOTp58q7WVIMzY5rR+q2LaQlbqN+WAS/zZ7G1fc8niNPytYtRDRtBsCqRfOIaBJTHk2VasQbLIwpIHkZ8IqZvQh875ybZZZnjtWV3iCkBtAE6OCcW2pmHwDXmdk4PF/ebwCOAVqZ2evARGBKMZp6eIm+RcBlheSbBfTCE0C8CdxiZjFAunNut5mdB3Q+3LsBNMATOJzn/VnsPV7Pe3wTEAl8C1zmnFtRRDunOud2AJjZCqAFsNkn/XTgW+fcPmCfNxgo6DpjC6hjDvAfM/sI+Mo5t6WINlVpCiAkX5lZjhe/W8GoG7sRZMa3v25hQ1IGt5/ThhUJO/h5VRLg6X2YvLRkX9yzHIydt5FHzz2eIIMZ65LZsn0vV3SNYUPqbhZt3k7f9tGc3LwhWc7TGzFqtmcp2ZgGtflXj1ic88x3/HbZ1jyrN5XEBy/cSq+T2xHRsB4bfhzOM6O/Zfw3s0p8vszMLB549Wu+Gf4vgoKMDyYtZNWfiTw28HwWr97MpDkrOKPrcQy5tS84mPPbBu77P8+/Y+1ioxjxwOVkZTmCgoz/+2h6jtWbCtLr1G7MmreQvtfcTO2QEIYOPtJb/bebB/Hlu28A8Mqb7zJp6gz27dvPOZdfz2UXns+dN13HK6PfZc/efdz31AsANImK5I0XCow1q7TgGjUYdP9DPHLPILKyMjn/ogHEtmrN+DFv0rZ9B3r0PJPVK5YzZPADZOzaybzZs3j/nbd45+PPAbj3tpvZvPFP9u7Zy9X9+3Lfo0/Q7dQe5XxV1UtwcA36//Nuxg59kKysLOLO7kv0sS2Z8slYmh3Xjg7dTmfuD1+zbukigmsEU7vuMVw56MiqY8Nu+zv79+4h89BBli+Yzc1PDs+xgpPI0eacW2NmJ+EZf/+smU31TTezlnh6A7o559LNbDxHRiKMA77D83T8c+fcISDdzLoA5wO3AVcCA/1szuFu/UwK/844E7gTOBZ4DLgUuBxPYAGe3oW7nHOTc13L+cALzrm3ch2PBXbgCSTOAIoKIHyHHxTV1sLKF1jWOTfMzCbiuS9zzOx859yq/PJWB+Zc8Qeux8XFDSnpU+/qqqw+s9Kc98THfiiDWQsFa9smcOP3Ab4eMTqg9YUc4/9T/qMl7b+3B7zOmo1bB7zOQNmUllF0pqPs2LB6Aa+zKvv698D3RF7aqUnA65RKL++ybYcTzJoCac65fWZ2EZ5hQ62B/s65P7zBwPt4huxEAkuBh51z473lvwNOAvo451Z65wAccM7tNLNOwIfOua4F1D0eT6/HF2b2J54hPCneVYqGO+d6F9LuNcBO51ycmT0MDAIGOee+9faW9AOucM4dNLO2QAKenoGhwDnOuQxvr8VBPBPGvwe6A5OBUc65jwuo90ZyDjX63tvWGYevAWgJvAX0wBMg/Ipn2NhwM5sBPOCci/d+VvHOuVgz+5v3M/+H97ytnXPrvdtfeD/Hb6im1AMhIiIiUnGcALxsZll4vkzfjmc40o9mttU5d5aZLQZW4RmmMydX+Y+ASOfcSu9+DDDOzA4vnFNWL/aZDwR7t2cBLwCzvfvv4Bka9Kt5xmMlA5c456aYWXvgF+8wrQzgOjw9AXiHP10E/M/MMpxzE0rSMOfcQjObgCfYSsQzTGxH4aWYDgw2syXeaznDzM4CsoDlwA8laUtVoQBCREREpILwDvOZnOtwPPC6T54bCznFGcDbPnl/w9Mj4U/dN/psx/psxwO9iyh7vc/2XHxW+nTOZQGPen9ylxsBjMjnlJ286duBfFdg8qaPB8b77F+U3zXg6ZUYYp73aczEO4nat1fFO0k91rudlqvezwpqQ3WkAEJERESkCjCzRcBu4P7ybksFNMbMOuCZL/Kec+7X8m5QZaYAQkRERKQKcM6d7E8+MxuJZ/6BrxHOuXFFlLsJuDvX4TnOuTv9b2XJeCdcv5jr8B/OuUv9Ke+cu+bot6r6UgAhIiIiUo2U9Au/N8AoNMgoKwUM7ZJyojdRi4iIiIiI3xRAiIiIiIiI3xRAiIiIiIiI3zQHoho7uD8zoPUtWhDYt74H+sVu+3elBbQ+AMs6FPA6q7ISvFdTKpiDWbqJIiJlTQFE4OyLi4sbUgbnjS2Dc4qIiIiI5EsBRIDEx8cPK4vzllFQIiIiIiKSL82BEBERERERvymAEBERERERvymAEBERERERvymAEBERERERvymAEBERERERv2kVJsnh9HaRDB7QkeAg48v5m3h3+voc6Q/178AprcMBCK0VTFi9EHo8MTk7vW5IDb598EymLU/k+a9/L7K+Xh2iefyKzgSb8d+5f/LWlDU50h/72wl0bxsJQO1awYQfE8JJD3zvaculnTirY2MsCOasTGLo50uLrK/PKe146d8DCAoK4v2J8/nPR9NzpDePbsSowVcS0bAu6Tv38s9nP2Zr8g4Atk9/ieUbtgGwJWk7f39kXJH1FWXMUzfRr1cXktN2cuIVT5b6fADOOV54/W1mzo+ndmgIzz18Dx3ats6Tb8Q7HzBhynR27Mog/of/Zh//bMIPfPLNJIKCgqhTO5Qh99/JcbHHHpW2VUYL583lzVeHk5WZyQUXX8JVN9yUI33p4l8ZPWI4G9av49Gnn6fX2X0AWL9mNa+9/AJ79uwmKCiIq/9xM737nFcel1DtrV28gInj3sBlZXLyORfS69JrcqQvmDKB+T9+Q1BQELVCazPg1vuJah7Lut/i+d9HYzh06BA1atTg/9m77/Aqiu6B49+TQhIIEFKB0DtIUyKIdEGlqIj9B69KEfQVUEREVJSAig0RUFpQBBHUFwWUIqJ0kBZ6L4p0QjoEEgzJ/P64m36T3JCQUM7neXy4Ozs7Z3ZzE/fcmdl7/9MvUK3BHUV0Fkopdf3QBEKlchIY3q0+fUM2cTY2nh9ebsXKfWH8HRaXWufjX/alvu7eogp1A0tlaGNgx9ps/duxL1RzEgh+shHPTljH2Zh45r3ejuW7znDk7IXUOu//tDv19dNtq1GvghcAj6PbagAAIABJREFUt1fzpkk1H7q8/wcAP7zahmY1fdl0OCL7eE7Cp690o+vgEE6Fx7I65GUWr9vHwWNhafFefIDvftvKnKWhtL6jBsH9OtPv/e8AiL+cSIs+nzl0bo76ZuF6Jv2wnK/ffa7A2ly7aSvHTp3m12+nsmv/QUZ9NpnvJ4/JUq/t3XfSvVsXOv3nhQzlXdq34cmHOgGwYv0mPp70FSEfjyyw/t1IkpKS+GLMh3w4fhK+/gEM7PM0zVu1oXLVaql1/MuWZcjwkfw4Z1aGY93c3Rn6zigCK1YiMjyc/r17ENSsOZ4lSxb2adzSkpOSWPjVeHq+/QmlvP2Y8sYL1Am6G/+KVVLrNGzZnqb3PQTA/i3r+XXmJJ4d/jHFS5Wmx7DRlPL2Jez4UWa+N5ShIXOL6EyUUur6oVOYVKoGlbw4HnmRk1GXuJJk+HXHKe65LSDb+p1vL8+S7adTt+sFlsbHsxh/Hgp3KF6jKt4cC7/IichLJCYZFm89SYdG5bKt/2BQRRaFWt9mbcDN1QlXFyeKuTjj4uxExIXLOcYLqluJv09F8s+ZKBKvJPHT8h080PK2DHXqVAlg9bbDAKzZdoQumfYXtHXbDhEde7FA21yxfhMP3dcOEaFRvTpcuHiR8MisSV2jenXw88n6bd2eJYqnvo5PSEBECrR/N5KD+/ZSvkJFygVWwNXVlTYd7uPPtasy1ClbrjzVatREnDJepwqVKhNY0TZy4+Pnh1cZb2Jjogur68py8sgBfMqWxzugPC6urjRocQ/7Q9dnqONevETq68TLae/58lVrUsrbFwD/ilW48u9lriT+W3idV7ckEfERkR3Wf2dF5FS67WKZ6g4SkeLZtZWu3ioRCcpjP3qKSPm89j8P7bcVkUUF2F6GayEiS0TEq6DaVxlpAqFS+Zf24GxMQup2WEwC/qU97NYtV8aDQO/ibDpi+8RfBF57qB5jFu13OF6AlztnouNTt89GxxOQTbzy3h5U8CnBhoPnANh+NIqNh8LZ8EFnNnzYmbX7w/gr3ciF3T77lubUuZjU7VPhMZTzK52hzu4jp3modQMAHmpdn1Il3PEuZft75F7MhdUhL7Ni8sAsicf15FxEJGX9/VK3A3x9CIuIzFMbc+YvpmOPfoydOpM3B/Yr6C7eMCLCz+EXkJZE+/kFEBnuWIKc3oF9e0hMTKRcYIWC7J5ywPmoCEr7+Kdul/b240Jk1pHKTUvnM3ZAD377dipdeg/Msn/vxjWUq1YTF9diWfYpVZCMMZHGmMbGmMbAFOCzlG1jTOYMdhCQawJxlXoC1yyByCuxyem+NcO1MMZ0NsbE5FBf5YMmEOqqdGpcnmW7zpBsbNtP3V2FNfvPERabkPOBV+mBJhVZuv1UarzKfiWoXrYULd/6lRZvLqF5LT+CrLUZ+fHWpEW0bFyddV++QovG1Tl1Loak5GQA6j3xPm36jaf3qNl8OLArVcvnP971qnu3LiydHcIr/Z5lyqwfiro7N7TIiHA+HvUOQ94KxslJ/+Rer5p17MbgL2ZzX49+rPop43S0sBNHWTY7hK79BhdR79StTkTai8h2EdktItNFxE1EXsJ2g79SRFZa9SaLSKiI7BURh+aeioiziMwQkT1W+6+IyGNAEDDbGvnwEJF3RGSLVS/EuqGvLiLb0rVVM/22nVgdReSAVeeRdOXBIjIk3fYeEali/XdQRL4B9gAV7Z1jNtfiHxHxtV4PttrcIyKDrLIqIrJfRKZZbS0TEfufYqosdA3EjS8hKCgo+KqObJvxsHOx8ZT1ck/dDvBy51xsPPZ0alye9+elLZJuVLkMTap689TdlSnu5oKrs3Dp8hXGLTmQbfiwmATKlUn7XS1bxoOwbOI9EFSBET/sSN2+t1F5dhyN4tLlJABW7w3j9mrehP6V/SftZyJiCfRPG80M9PPijLVAOsXZyPP0GD4TgBIexejaugGxcQnW8ecB+OdMFOt2/EXDmoEcPZ23T/avlTnzF/Pj4mUA1K9Tk7Pn0j4lD4uIJMD36pKdzve04t1xkwukjzciXz9/wsPS1siEh4fh4+eXwxEZXbwYx9tDXqZnvxepW7/BteiiykUpb19iI8+lbsdGhVPSxzfb+g1a3MPCaePS6keG890n7/DogGF4lw28pn1Vtw4R6QekH94NMcaEZFPdHZgBtDfGHLJupv9rjBknIoOBdsaYlGG1t4wxUSLiDCwXkYbGmNyeMNIYCDTG1Lf65mWMiRGRAcAQY0yoVf6FMWaU9XoW8IAxZqGIxIpIY2PMDqAXYPcJIyLiDkwD7gGOAI5+OlUTeNYYs9Fqx945TrBzLVLiNrH61QwQYJOIrAairbb/zxjTV0T+BzwKfOtgv25pmkDc4EJDQz+82mPrD1k0Iv32nhOxVPItQaC3B2GxCXRqHMjQ2Vk/SKjqV4JSHq7sOJY2n3vYnO2pr7sGVeC2il45Jg8Au45FU9nfkwo+xQmLiadLkwoM/npLlnrVAjwpVdyV7ekWZ5+OvsSTLaoy5TdBgKY1fZmx8kiO8bYeOEH1Cr5ULufN6fBYHm3fmN6jZmeo41O6OFHn4zHG8GqPe5i1xNYfL08PLl3+l38Tk/ApXZxmDarw2ZxVOcYrTN27daF7ty4ArN6whTkLFtP5ntbs2n8QzxLF7a51yM6xk6epXME2ar16YyiVA6+bEexCV7tuPU6dPMGZ06fw9fNn9R/LGBb8vkPHJiYmMnLYEDp0eiD1yUyq8AXWqEPkmVNEh52hpLcvu9ev4PGXh2eoE3nmJD7lbNPLDm3biE85W6IQfzGOWR8M494efalcRxNAVXCsZCG7hCEzZ+CoMSblMYUzgf7AODt1n7CSExegHFAPyC2B+BuoJiKfA4uBZdnUayciQ7FNE/IG9gILgS+BXtYN/JNA02yOr2Odx2EAEfmWjElUdo6lJA+WvJ5jS2C+MeaiFXce0Ar4xepPyqeTW4EqDvRHoQmESicp2TB6/l6m9m2Gswjzt5zgr7A4+t9fi70nYlm1z/ZJbKfbA/l1x+lcWnMs3sgfdvD1gBY4OwlzNxzj8JkLvPxAXfYci2H5btsjUx8IqsjilMXTlqXbTtG8lj+Lh7cHA2v2hbFi99mc4yUlM2TcfBaM6YuTkzBryRYO/BPGW73vZ/vBEyxZv4+WjWsQ/HwnMLB+598M/mweALWr+DN+yGMkJxucnITPZq/M8PSmqzXrg+dp3aQ2vl6e/L10DKOm/MyMBWvz1Wbru4JYs2krnf7zPO5ubrz3+kup+x557mXmfTkegDFTvmbJ8jUkXL7MPY/34tEu99K/Z3fmzF/Mhq07cHFxoVRJT0YPG5Sv/tzInF1cGDB4KG++MoDkpCTuf6ArVapVZ+a0ydSqU4/mrdpwcN9eRr4xhAsXzrNx3VpmfTWVabPnsnr57+zesY3z52NZtmQhAK+9FUz1WrWL+KxuLc7OzjzQ5yVmvj+U5ORk7mjXiYCKVVn+/XTKV69N3TtbsPHX+fy1eyvOzi54eJbkkQHDANu6iKizp1k19xtWzf0GgGff/gTP0mWK8pSUsktEqgJDgDuNMdEiMgPb6EWOrLqNgPuBF4AngN6Z2nYHJgFBxpgTIhKcru2fgBHACmCrMeZqhuavkHFaffp+pz5p5GrPMQfpn76SBOgUJgeJMSbPBwUFBQWHhoYGF3x3VGGqP2RR3n/4+ZBwqXCfXhK2589CjXf5gmOPry1Ilxa/XugxXcrfvDfAxyLjcq9UwCr7eBZ6zJvZ/3bl/8ONvHqi4a07SqeumkOPt7Nu1K9g+6T+HmPMEeumebsxZryI7AYeMsYctZKAb4DbAT9sn8q/boyZISKrSDcdKVMMX+BfY8x5EakPfGuMaSwiC4GxxpiV1tOMDmL7hN4Z2Aj8aIwJttr4HNv0nz7GmF+zORd34BC2aUZ/ich3QEljzAMi8h9sU6KeEpE7gC1AyhcYLUo3vSqnc0y9Flbdf7Ct46iEbQrYXdZ13wQ8jW0KU/q2hwCeKeekcqYjEEoppZRS168EbHP454qIC7ab6ynWvhBgqYicNsa0E5HtwAHgBLDebmtZBQJfp3vC0RvWvzOAKSISDzTHtn5hD3DW6kN6s4FuZD/9CWNMgjX1aLGIXALWAilfjPMT8IyI7MV2g38omzZ25nCOGa5FumO2WUnXZqvoS2PMdhGpkl1fVe50BOIWpiMQBUtHIG58OgJx49MRCHWDuKm+YMf69L60Mebtou6LKhw6AqGUUkoppa6KiMzHNt3onqLuiyo8mkAopZRSSt0CRGQT4Jap+GljzO6rbdMY081OnPlA1UzFrxtjfrvaOOr6ogmEUkoppdQtwBjTrJDiZEkq1M1FvxZVKaWUUkop5TBNIJRSSimllFIO0wRCKaWUUkop5TBdA3EL2/FK9dwrFSDjXKxQ40GjQo0myVcKNR5A8S4fFWq85T99An9fzZeMXp0/DocXWiyAEVUL79xS+bQo1HBJx3YWajwA58qF97v4aJki+Bmij3FVSt1adARCKaWUUkop5TBNIJRSSimllFIO0wRCKaWUUkop5TBNIJRSSimllFIO0wRCKaWUUkop5TBNIJRSSimllFIO0wRCKaWUUkop5TD9HgiVgTGGD774kjWbtuLh7sb7Q1+iXq2s3xcx/qtv+WXZSmIvXCR0yfep5T/8spTvfl6Ck5MTxT08CB78IjWqVMw53oSprN20BXc3N95/YzD1atXIGm/aTH75bTnn4+LYsnReannozt189HkIh/4+yifvDOO+ti1zP798xJv5wzx+Wvwbzs7OeHuV5t3XB1G+bEDuMT+fxppNobZr+vog+9f0y1nWNY0j9Nf/pZb/8MuvfLcg5Zq6E/xqf2pUqZRjzOyEjOhF59aNCI86z+2Pv3NVbWS2J3Qj300ZR3JyEq06PkjnJ57JsH/ZvO9Yu3QhTs7OlCztRa9X3sQnoFzq/viLF3nn+e40vrs1PV58Ndd4Z/ZtY9u8aZjkZKo1v5d69z5mt96JHX+yfvpH3DdkDN6VahIXGcavowdQ0j8QAJ8qtbjzyRdzjWeMYXTIHNZs3Y27WzFGv9yH22pUzlAnPuEygz6azIkz53BycqJd00a82vNxALbsOcgH077j0D8n+XToC9zfIijXmIXNGMPoSV+zZst2PNzcGD3kRerVrJal3rivv+OX39cQGxfH1l9mZdj36+o/mThrLiJCnWqV+eSNlwur+7kyxjB64nTWbN6Oh1sxRg8dYP/8ps/hl99XE3vhIlsXfZtaPv+3lYwJmYW/rzcAPbp25LHOHQqt/0opdb3REQiVwdpNWzl26gy/zppM8OAXGTVuit16bZvfyfeTPslS3qV9axZ8NYF508bR+8lufDx5ei7xQjl+8hRLZn9J8JCXeHfsF/bj3d2M76eOy1Jezt+f994YTOf2bXM/uQKIV7dmdX4IGc/8rydxb5uWfDol5/OzxdzKsVOn+fXbqQS/2p9Rn03OJuadfD95TJbyLu3bsGD658z7cjy9n3qEjyd9lWvM7HyzcD0P9B971cdnlpyUxOyJYxj07qe8O3UOm1f9weljRzPUqVS9FsMnTGfk5Fk0admOudMnZdi/YFYINRs0dixechKhc6fS5oURdHrzC45vXUvsmeNZ6iUmXOLQ6oX4VK6VodzTtywdXx9Hx9fHOZQ8AKzZuptjp8NYOvUDRvZ/llGTv7Fbr3e3+1kyZTTzxgezff8R1oTuAqC8nw8fDOpDlzbNHIpXFNZs2c6xU2dZ+vUERg7qx8gJX9qt1+6uJvzw+egs5f+cOsO07xcw+7N3WThtLMNe6HmNe5w3azZv59ipMyyd+TkjX3mBkeND7NZrd1cQP3zxod19ndrezfypY5g/dYwmD0qpW54mECqDFX9u5qF72yIiNKpXmwtxFwmPjMpSr1G92vj5eGcp9yxRPPV1fEICIpJjvJXrNvLQ/e1t8W6rk3282+rYjRdYLoDa1avi5OTYWzm/8Zre0QgPd3dbnXp1CAuPyDXmivWbeOi+dtY1rcOFi9ldU/sx83pNc7Ju2yGiYy9e9fGZHT20D//yFfArF4iLqytN23Rgx8a1GerUadQEN+uaVa9zG9ER51L3/XP4AOejo7jtjqYOxYs6dpiSfmXx9C2Ls4srle5oxandm7PU2714DnU7PIqTa/6//XzFxu10veduRITGdapz/uIlzkXFZKjj4e5Gs4Z1ASjm6kK96pU5GxkNQGCAL7WrVsRJrt8/tyv+DKXrva1t79G6taz3aHSWeo3q1sLPp0yW8h+XLKf7Q/dTuqQnAD5lSl/zPufFij+30DX171otLsRdsn9+9eyfn1KFTUT+LOo+5EZE2orI3Vdx3D8i4nst+uRg/DeLKvbN5Pr9P5oqEucioijrn/Z7HeDnQ1hE1pvdnMxZsISOPZ5nbMhM3hzwXI51wyIiKOvvly6er0M35VerIOPNW/IbrZrlPh3lXERkxpi+PoRFROYp1pz5i+nYox9jp87kzYH98tzXayU6IpwyfmlTuMr4+hEdGZ5t/bXLFtEg6C4AkpOT+d+0z3n8uYEOx4uPiaS4V9r708PLh/jYjNcy6sRfXIqJoPxtWX82cZFhLP1oEMvHv8m5v/Y6FDMsMpqyvmmJXVkfb87ZuflMcT7uEis376B5o7oOtX89OBcZRVm/dL/3vj6E2Ulys/PPydP8c/IMPQa9zVMvvcXaLTuuRTev2rmISMr6+aRuB/h55/l3cNnajTzcdzCDRo7hzLlr9zdKKQBjTJ5vzNMTEeeC6ksO2gL56mcR0QSiAGgCoQpc94c7s3T2VF7p9wxTvp1b1N25JhYuW8Heg4fp9ZT9+fcFrXu3LiydHcIr/Z5lyqwfCiVmQduwYinHDh3g/kd7ALBq0Twa3Nkcbz//AothkpPZPn86jR/ulWWfRylvHhr5JR1fH8ft3XqzYeanJMZfKrDYAFeSkhjyyRT+82AHKpYtuPO63iUlJ3Ps1BlmjBnBmDdeZsRnUzkfV3AjXUWt3V1B/PHtZBZMG0vzJg1582P7Ux+VKigiEmf9W05E1ojIDhHZIyKtcjpGRD4VkZ1AcxH5j4hsto6dmpJUWPU+E5G9IrJcRPys8uoislREtorIWhGpY5U/KCKbRGS7iPwhIgEiUgV4AXjFar+ViPiJyE8issX6r4V1vI+ILLPifQnkOIwuIm+LyEERWSci34nIEKt8lYgEWa99ReQf67WziHxixdwlIs9nd+1E5EPAwyqbfbU/H6WLqLMICgoaBrgXdT8Kw8afbYsg5yxYwo+LlwFQv3ZNzqb7dC0sPJIA36zTahzRuV0r3h03NUv5d/MX8uOi39LFS/vEOiw8ggC/gh3ZLOh4G0K3EzLrB2ZM+IhixVzt1pkzf3HaNa2TKWZEJAG+PnaPy03ne1rx7jj7ayiKQhlfP6LDw1K3oyPCKePjl6Xevu1bWPz9TIZ+PBHXYrZpRX/t38PhvTtZtWgelxPiuZKYiJu7B4/1zn5tgoeXD5di0t6f8TGReJROu5aJl+OJPXOMFZ8PByDhfDRrQt6ndb+38K5UE2dX28/Lu1INPH3LcSH8FN6VamaJM3vxcn78bQ0A9WtW5Wy6UbizkVH4ZzPNZcQXM6lcPoBnu96X7TlcL+b8spS5S5YD0KB2dc6mG4kLi4gkwM50uuwE+HrTsE5NXF1cqFDOn8oVynHs1Bka1M76gILCMufnX9POr1Z1zoanjTiEhUfl6XfQq3TJ1NePdWrPpyHf5lBbqdyJSD8g/XByiDHG3uKc7sBvxpj3rQSguJ06KUoAm4wxr4pIXeB1oIUxJlFEJgE9gG+seqHGmFdE5B1gBDAACAFeMMYcFpFmwCTgHmAdcJcxxojIc8BQK8YUIM4YM8Y6pznAZ8aYdSJSCfgNqGu1v84YM0pEugB9crgudwKPAo0AV2AbsDWHc8ZqL9YYc6eIuAHrRWQZ8Ejma2eMWSsiA4wxji28U9nSBCIr99DQ0OCi7kRhuHJq/wiwjRh0f7gzAKs3hjJnwRI639OKXfsP4VmihN15+dk5dvI0lSuUT22rcmC5LHX+r9uD/F+3B211Nmzmu3kL6dS+Dbv2HcxzPEcUZLz9h/5i5KefM/WTd/Ep45Vtve7dutC9Wxcr5hbmLFhM53tas2v/QTxLFM/nNS3v8LHXWpVadQk7fZLws6cp4+PH5tV/0Pf14Ax1jh85yKwJHzHovc8o5ZV23unrrf99Mf8cPpBj8gDgXakmF8LPEBcZhkdpb45vW0vzZ9Oe3FTMowSPfJB2c7d8wlvc/nBPvCvVJOFCLMVKeOLk5ExcxFniwk9Twqes3Tg9urSnR5f2AKzaspM5i5bTuXUzdh78m5LFi+PvnfVnP27WPC5cjOfdgT1zPIfrRfeHOtL9oY4ArN60jdk/L6Vz2xbsOnCYkiWK52ktQPu7m7Jk5Toeub8d0bHnOXbyDBXL5fx0smute9dOdO/aCYDVG7cy++df6dyuBbv25/38wiOjU+uv3BBKtUqB16TP6tZhJQv2V/NntAWYLiKuwAJjTE7zA5OAn6zX7YEmwBZr3ZwHkLIALRlIGcr+FpgnIp7YpiPNTbfOzs36twLwg4iUA4oBGZ+UkaYDUC/d8aWsdltju5nHGLNYRLKfAwotgJ+NMQlAgogszKFuivuAhiKSMiWgNFCTvF07lUeaQKgMWjdrwppNW+n0nxdwd3fjvaEvpe57pO8g5k2zPZlozNQZLFm+loTLl7nniT482rkD/Xv+H3MWLGHD1p24uDhTqqQno1/P+VGOre+6k7Ubt9Cpex883Nx4d9grqfse7TOAn76yTRX4dPJXLFm+ioSEy7R/7Gke6XI//Xv9h937DzHo7Xc5fyGOVX9uYuLX3/LzTPtPjiqIeJ9O+YpL8QkMHvEBAOX8/fjigxG5nGOQdU2fx93NjfdeT3dNn3uZeV+Ot13TKV+zZPka2zV9vBePdrmX/j27M2f+YjZs3YGLi4vtmg4blGO8nMz64HlaN6mNr5cnfy8dw6gpPzNjwdrcD8yGs7ML3f87mHHDXyE5KYkW9z1AYOVqLPhmGlVq1aHxXa2Y+9VEEhLimTLaNirg7RfAwOCPryqek7MzTR7rx+pJwSQnJ1PtrvaULleJ3Ytn412pBoENsn/SUfhfe9m9ZA5Ozi6ICEFP/Be3EiWzrZ+iTVBD1oTu4v5+w6zHuPZO3dftpRHMnzCSsxFRTP3fIqpVKMejg0YC0L1Lex6/vzW7Dx1l4OgvOB93kZVbdvD57AUsmvTeVZ3/tdK66e2s2byNjj1fwt2tGO8PSUvkur3wGvOn2J64NmbatyxeuY6Ey//SrvsLPNrxHgY88wQtgxrx59adPPDcKzg7OTGk73/wKpX7tS0srZvdYTu/ZwbYHt/8Wrrze34I86fann42JmQWi1fY/q61e6ofj3Zqz4Bnn2TW/CWs3LAFF2dnSpf0ZPTQAUV1KuoWY4xZIyKtgS7ADBEZa4yx/yg4SDDGJFmvBZhpjHnDkTDYprTHZPPJ/OfAWGPMLyLSFgjOph0nbCMVCekL8/Pgj0yukDb1Pv1MEQEGGmN+y3xAHq6dyiMxxuT5oKCgoOCb9VP6m/ncMrtyan/ef/j5YJzz/0Sc65kkXyn0mMW7fFSo8Zb/lPXRvdfSH4ezX5B9LYyomreFtQXBqVaLQo2XdGxnocYDcK7cqNBiJZ3YXWixUjhXbFDoMdUNL7d1AHHGGE8RqQycNMYkicgAoIYxxu6nSCnHWK/rAT9jm8J0TkS8gZLGmGMiYoD/M8Z8LyLDgQBjzEDryU+fGWPmiu2uv6ExZqeIbAeeM8ZsFZGvgarGmLYi8ipQyhgzwoo5B9hujPnE2m5sjNkhIhOAc8aY90SkE7AE8DPGZHkagTWFaSq20RAXbFOYQowxY6z1E1uNMZNFZBAwyBhTxZoO1hl43JquVQs4Bfjau3bWCIi/MSbRkR+Usk8XUSullFJKXZ/aAik38U8C4x05yBizDxgOLBORXcDvQMqc4otAUxHZg22NwyirvAfQx1qEvRfoapUHY5vatBVIf9O/EOiWsogaeAkIshYy78O2yBpgJNBaRPZim8qU9ct70vq9BfgF2AX8CuwGYq3dY4D/Wtci/eLFL4F9wDbrnKZiSz7aYv/ahQC7dBF1/ugUJqWUUkqp60jKSIIxZiYwMy/HpNv+gbS1DpnrDrZTdhToaKf8Z2yjGZnLDwENMxU/aadeJLZ1Co4aY4wJFpHiwBqsRdTGmAOZ4g23ypOxPZo18+NZ7V47Y8zr2BaYq3zQBEIppZRSSl0vQqwpWO7Y1nFsK+oOqaw0gVBKKaWUukGIyCbSnpCU4mljjEMLgDKPVBQFEfEBltvZ1d4Y072w+6PyThMIpZRSSqkbhDEm+8fN3SCsaU36XQw3MF1ErZRSSimllHKYJhBKKaWUUkoph2kCoZRSSimllHKYroFQ6gZW2F/s1v7R1wo13oDgLE8avKaG7C/NmLqxuVdUSimlbmGaQNzCXALrFnUXVH79XfjfnHyzK+xvhi5shfmt0EVBvxVaKaWuPZ3CpJRSSimllHKYJhBKKaWUUkoph2kCoZRSSimllHKYJhBKKaWUUkoph13zRdRBQUHDAPdrHacAVSnqDiillFJKKXW9KoynMLmHhoYGF0KcAhEUFBRc1H1QSimllFLqeqWPcVXqJrMndCPfTRlHcnISrTo+SOcnnsmwf9m871i7dCFOzs6ULO1Fr1fexCegXOr++IsXeef57jS+uzU9Xnw1X30JGdGLzq0bER51ntsffydfbaWo4+/Jww3K4QRsPB7NisMRdus1LFeKnk0rMXb1EU7GJOAk8GTjQCp4eeAkEHoihuXZHKuUUkqp7OkaCKVuIslJScyeOIZB737Ku1PnsHnVH5w+djRDnUrVazF8wnRGTp5Fk5btmDt9Uob9C2acU5b9AAAgAElEQVSFULNB4wLpzzcL1/NA/7EF0haAAI80LE/Ihn/4aMUR7ggsTUBJtyz13FycaFXNh2NRl1LLGpcvjYuT8MnKI4xd/RfNq3hTxsO1wPqmlFJK3So0gVDqJnL00D78y1fAr1wgLq6uNG3TgR0b12aoU6dRE9zcbcuSqte5jeiIc6n7/jl8gPPRUdx2R9MC6c+6bYeIjr1YIG0BVCrjQcTFy0RdSiTJGLafiqV+2ZJZ6nWq48+KI+EkJpvUMgMUc3HCScDVyYkryYbLV5ILrG9KKXW9E5ElIuJV1P24FkRklYgEWa//ERHfXOq/mWn7z2vZv5uNJhBK3USiI8Ip4xeQul3G14/oyPBs669dtogGQXcBkJyczP+mfc7jzw285v28WqXdXYmJT0zdjom/Qmn3jKMIgaXd8fJwZX9YXIbynadj+fdKMsH31+Ht+2qz6kgElxKTCqXfSilV0ETEOa/HGGM6G2NirkV/bkAZEghjzN1F1ZEbkSYQSt2iNqxYyrFDB7j/0R4ArFo0jwZ3Nsfbz7+Ie3b1BOhavxw/7zmbZV+lMsVJNhD82wHe//0gbWv44l1cpzAppa4/IlJFRA6IyGwR2S8iP4pIceuT9Y9EZBvwuIjcJyIbRGSbiMwVEU8R6Sgic9O11VZEFlmvUz+ZF5HBIrLH+m9Qurh70h07RESCrdcvicg+EdklIt/n0HdPEflaRHZbdR+1yieLSKiI7BWRkenq/yMiI61z2C0idXJpJ8s553ItF4jIVituP6vsQ8BDRHaIyGyrLM76V0TkE+u67BaRJ9Ndx1XWzyLlZyMp7aW7NmMc+iHf4HQRdT7dgI+pTRUaGlrUXVAFrIyvH9HhYanb0RHhlPHxy1Jv3/YtLP5+JkM/nohrsWIA/LV/D4f37mTVonlcTojnSmIibu4ePNb7xULrf25iExLxSrduwcvDhdiEtBEJNxcnypZ0o3/LqgCUdHOhT7PKfLXpGHdUKM2Bc3EkG4j7N4mjkZeo6OVB1KXELHGUUupasm5k+6UrCjHGhGSqVhvoY4xZLyLTgZQ/xpHGmDusRGAe0MEYc1FEXgcGA6OBEBEpYYy5CDwJZLjhF5EmQC+gGbbPXjaJyGogOoduDwOqGmMu5zIN6m0g1hjTwIpVxip/yxgTZY2cLBeRhsaYXda+COucXgSGAM/Za8c65+F2znlUDv3pbcX1ALaIyE/GmGEiMsAYY2/B3yNAY6AR4Gsds8badztwG3AaWA+0EJH9QDegjjHG3KxTxDLTBCL/bqjH1GYyoqg7oApWlVp1CTt9kvCzpynj48fm1X/Q9/XgDHWOHznIrAkfMei9zyjl5Z1anr7e+t8X88/hA9dV8gBwIiYevxJueBd3JTb+CrcHlmbW1pOp+xOuJPPO0gOp2y+2qMove89wMiaBWr6J1PQrwdaTMRRzFip7e7Dmb30Kk1Kq8FnJQuaEIbMTxpj11utvgZes1z9Y/94F1APWWx+EFwM2GGOuiMhS4EER+RHoAgzN1HZLYL6VYCAi84BWwC859GcXMFtEFgALcqjXAXgqZcMYk5KUPGElTi5AOavvKQnEPOvfrdhu4O22IyIP2DvnHPoC8JKIdLNeVwRqApE51G8JfGeMSQLCrMTqTuA8sNkYcxJARHZg++6wjUAC8JU10rMol/7cFDSBUOom4uzsQvf/Dmbc8FdITkqixX0PEFi5Ggu+mUaVWnVofFcr5n41kYSEeKaMHg6At18AA4M/vib9mfXB87RuUhtfL0/+XjqGUVN+ZsaCtbkfmI1kA/N2naZf8yo4ibD5eDRhFy7TsY4/J2Li2Xv2QrbHrjsaxVO3BzK0XQ0Q2HI8hjPnL191X5RS6hoz2WynPJlCgN+NMf9n59jvgQFAFBBqjMn+j2NGV8g4vT39DIsuQGvgQeAtEWlgjLniSKMiUhXbyMKdViIwI1PbKX+Mk8j53jSnc7YXty22RKS5MeaSiKwif7NG0v9PIwlwsRK2pkB74DFs1/2efMS4IWgCodRNpmHTu2nYNONasIef6Zv6+tUPJuTaRot7u9Di3i757svTb0zNdxuZ7T8Xx/7lhzOULT1wzm7dSevTHmH7b1Iy34SeKPD+KKXUNVJJRJobYzYA3YF12KbQpNgITBSRGsaYIyJSAgg0xhwCVgPTgb5kmr5kWQvMsNYCCLYpOE8DYYC/iPgAccADwFIRcQIqGmNWisg6bCMDnoC9Bdm/A/2BlHUVZYBS2BKfWBEJADoBq3I5f3vt5HTO9pQGoq3koQ62UZsUiSLiaozJPI91LfC8iMwEvLElTa8BdewFsNZgFDfGLBGR9cDfuZzXTUEXUSullFJKXX8OAv2tOfZlgMnpdxpjwoGewHcisgvbVJ461r4kbFNpOmFnSo0xZhswA9gMbAK+NMZst26mR1nlvwMpc0KdgW9FZDewHZiQw9Oc3gPKWIuQdwLtjDE7reMOAHOwrR/Ijb12sj3nbCwFXKxr+CG2BCRFCLArZRF1OvOxTa3aCawAhhpjsj6ZI01JYJHVn3XY1mTc9HQEQimllFLq+nPFGPOfTGVV0m8YY1Zgm5+fhTFmALbpNOnLqqR7PRbI8k2fxpgJgL2h6paOdNoYEwc8a6e8Zzb10/cpFGibSzt2z9kY09Zem9iSKHtxXwdeT7ftaf1rsI04vJap/irSjZpY1zdFwXx50g1ERyCUUkoppZRSDtMRCKWUUkqp64gx5h+gflH3Iyci0gt4OVPxemNM/6LojypcmkAopZRSSqk8McZ8DXxd1P1QRUOnMCmllFJKKaUcpgmEUkoppZRSymGaQCillFJKKaUcpmsgskoICgoKzkP9KteoH0rl6o/D4YUab0Bw4T7e+ovgLE8YvKYuLXyVKyf3FmpMlwq3FWo8pZRSKr80gcgkNDT0w7zUz2OyoZRSSiml1A1NpzAppZRSSimlHKYJhFJKKaWUUsphmkAopZRSSimlHKYJhFJKKaWUUsphmkAopZRSSimlHKYJhFJKKaWUUsph+hhXpW4yZ/ZtY9u8aZjkZKo1v5d69z5mt96JHX+yfvpH3DdkDN6VahIXGcavowdQ0j8QAJ8qtbjzyRdzjVfH35OHG5TDCdh4PJoVhyPs1mtYrhQ9m1Zi7OojnIxJwEngycaBVPDywEkg9EQMy7M5Ni9CRvSic+tGhEed5/bH38l3e8YYPpj4FWs2bcPDzY33hw6gXq3qWeqN/2o2v/y+itgLFwldPCfL/mVrNvDKyE/4YdLH1K9dI9/9UkoppYqKjkAodRNJTk4idO5U2rwwgk5vfsHxrWuJPXM8S73EhEscWr0Qn8q1MpR7+pal4+vj6Pj6OIeSBwEeaViekA3/8NGKI9wRWJqAkm5Z6rm5ONGqmg/Hoi6lljUuXxoXJ+GTlUcYu/ovmlfxpoyHa57PObNvFq7ngf4F9wV0azdv49jJM/z6zUSCB7/AqPEhduu1bR7E9xM/srvv4qV4vp23mIZ1axZYv5RSSqmiogmEUjeRqGOHKelXFk/fsji7uFLpjlac2r05S73di+dQt8OjOLkWy1e8SmU8iLh4mahLiSQZw/ZTsdQvWzJLvU51/FlxJJzEZJNaZoBiLk44Cbg6OXEl2XD5SnK++gOwbtshomMv5rudFCvWb+ah+9oiIjSqV5sLcRcJj4zKUq9Rvdr4+XjbbWPC13Po89TDuBXL3/VWSqn8EJElIuJ1jWMEi8iQAminrYgsslP+kIgMy2/7BU1EqohI96Luhz0iMkNE7E9HsF+/iojsyamOJhBK3UTiYyIp7uWbuu3h5UN8bGSGOlEn/uJSTATlbwvKcnxcZBhLPxrE8vFvcu6vvbnGK+3uSkx8Yup2TPwVSrtnHEUILO2Ol4cr+8PiMpTvPB3Lv1eSCb6/Dm/fV5tVRyK4lJjk0HkWpnMRUZT1S7umAX4+hEVkTSCys+/QX5wNj6TNXVmvt1JKXS0Rcc7rMcaYzsaYmGvRn8JijPnFGPNhUffDjirAdZlAXAu6BiL/EoKCgoKLuhNXIzQ0tKi7oAqZSU5m+/zpNOvxUpZ9HqW8eWjkl7iVKEXU8SOs/XI0nd/4AleP4lcdT4Cu9cvx3baTWfZVKlOcZAPBvx2guKszA1pV41B4HFGXErM2dINKTk7m4ykzeH/owKLuilLqOiIi/YB+6YpCjDEh6fZXAZYCW4E7gL3AM8A+4AfgXuBjEYkCRgJuwF9AL6Al0McY87jVVltgiDHmARH5BwgyxkSIyGCgtxXyS2PMOCvuImNMfevYIYCnMSZYRF4CXgCuAPuMMU/lcIqNRGQD4At8bIyZJiICfAx0wjYI/Z4x5ofsyjNdrzuBEOAxoJV1DgNEZAZwHggCygJDjTE/iogT8AVwD3ACSASmW/s+BB6yzmOZMcbuaEkObWfX3w+BuiKyA5hpjPnMTpvOwEdARyAZmGaM+VxE3gEeBDyAP4HnjTFGRFYBm4B2gBe2n+vaHNppAowFPIEIoKcx5kymPtitY5VPt6ots3dN0tMEIp9CQ0OvxyzYUSOKugOqYHl4+XApJm0hcnxMJB6lfVK3Ey/HE3vmGCs+Hw5Awvlo1oS8T+t+b+FdqSbOrrbRA+9KNfD0LceF8FN4V8p+3n5sQiJe6dYteHm4EJuQlgC4uThRtqQb/VtWBaCkmwt9mlXmq03HuKNCaQ6ciyPZQNy/SRyNvERFL4/rIoGYs+BXflzyOwD1a9fgbHjaNQ0LjyTA1/5UpcwuXorn8NHj9Bz8NgARUTEMePsDvnj3DV1IrdQtzEoW7C+oSlMb2w3jehGZDqQsTIs0xtwhIr7APKCDMeaiiLwODAZGAyEiUsIYcxF4Evg+fcPWzWIvoBm2z3o2ichqIDqH/gwDqhpjLjswDaohcBdQAtguIouB5kBjoBG2xGKLiKwB7s6mPKWvdwOfA12NMcdFpFWmWOWwJU11gF+AH4FHsI0I1AP8gf3AdBHxAboBdawb9NzOI7u27fV3GFailkN7/ax+NTbGXBGRlP+ZfGGMGWWd7yzgAWChtc/FGNNURDpju2/rYK8dEXFNd53CReRJ4H3SkkRyqfM1MMAYs0ZEPsnlumgCodTNxLtSTS6EnyEuMgyP0t4c37aW5s++mrq/mEcJHvng29Tt5RPe4vaHe+JdqSYJF2IpVsITJydn4iLOEhd+mhI+ZXOMdyImHr8SbngXdyU2/gq3B5Zm1ta00YaEK8m8s/RA6vaLLaryy94znIxJoJZvIjX9SrD1ZAzFnIXK3h6s+Tv/T2EqCN0f7kT3hzsBsHpjKHMW/Erndi3Ztf8QniWKZ7vWIbOSniVYP39m6nbPwW8z5PlnNXlQSjnihDFmvfX6WyBl6Djl0/m7sN0gr7d9KE4xYIN1Q7kUeFBEfgS6AEMztd0SmG8lGIjIPGyf7P+SQ392AbNFZAGwIJe+/2yMiQfiRWQl0NSK+Z0xJgkIsxKWO3MoPw/UxZZo3WeMOZ1NrAXGmGRgn4gEpDu/uVb5WasPALFAAvCVtb4iyxoLB9vOrr+56QBMMcZcATDGpMyHbSciQ4HigDe2EaeUBGKe9e9WbEmD3XZEpD5QH/jdej84AxlGH7AlpVnqWImUlzEmJXGbhW2EJVuaQCh1E3FydqbJY/1YPSmY5ORkqt3VntLlKrF78Wy8K9UgsEGzbI8N/2svu5fMwcnZBREh6In/4lYi64Lo9JINzNt1mn7Nq+Akwubj0YRduEzHOv6ciIln79kL2R677mgUT90eyNB2NUBgy/EYzpy/fNXnnmLWB8/TukltfL08+XvpGEZN+ZkZC9ZedXutmzVhzaZtdHr6Rdzd3XjvtQGp+x7pN5h5IbYnPo2Z+g1LVqwh4fJl7nnyOR7t3IH+z+Y0wq+UUjky2WynPCVCgN+NMf9n59jvgQFAFBBqjMn+j3FGV8i4PtY93esuQGtsU23eEpEGKTeweeh7Xp2x+nA7kF0Ckf5/HJJTY1Zy1RRoj2061ABs05yy43DbV0tE3IFJ2KZlnRCRYDJe95Q+JJHzfbsAe40xzfNa52oW1msCodRNpvxtQVkWSDfo0sNu3fYvvZ/6umLju6nY+O48x9t/Lo79yw9nKFt64JzdupPWH019/W9SMt+EnshzvNw8/cbUAm1PRHj75X5296UkDwBDnn+GIc8/k2NbM8a+W6B9U0rd1CqJSHNjzAZsi3PXYbuRTrERmCgiNYwxR0SkBBBojDkErMY2n70vmaYvWdYCM6z1AIJtWs/TQBjgb031icM2lWaptaagojFmpYisA57CNoc+uwXZXUXkA2xTmNpim97jDDwvIjOxfcreGngN272ovfI6Vvt9sH1iftEYs8rBa7ceeNZq08/qwxwR8QSKG2OWiMh64G8H20tvbTb9DQRy/tQNfreOXZluClPK4wcjrP49hm2qVF7bOQj4pbxnrOlKtYwx6Z+Ikm0dEYkRkZbGmHWA/ZuGdPQpTEoppZRS15+DQH8R2Q+UASan32mMCQd6At+JyC5gA7abbqzpNYuwTUPJMk3HGLMNmAFsxrZI90tjzHZjTCIwyir/HUiZg+oMfCsiu4HtwIRcnua0C1iJLcl515p+NN8q3wmswLYo+WwO5Sl9DcOWyEwUkeyH0TP6CTiJbdH5t8A2bNOXSgKLrOu1DtuakbzKrr+7gCQR2Skir2Rz7JfAcWCXiOwEulvXcRqwB/gN2OJAH+y18y+25OMjq2wHtvUlqXKp0wvbNd6BA6MtYkzeR5WCgoKCQ0NDgwu6rip0VzukqK4TI347kHulAnQhIbvR6mvji+CC+0I4R1xa+GrulQqYS4XbCj2mUqrI5XiDlvlpSCrvRMTTGBNnjaZsBlqkT0xU/ugUJqWUUkopdbNZZM3tL4ZtFESThwKkCYRSSiml1HXEGPMPtqflXLdEpBfwcqbi9caY/kXRn8yMMW0dqScibwGPZyqea4x53159B9u8H9v3NKR31BjT7WrbvN5oAqGUUkoppfLEGPM1tu8OuKFZicJVJwvZtPkbtvUMNy1dRK2UUkoppZRymCYQSimllFJKKYfpFKZb2Nq/Iws1XlUvt0KNl5x7lQJ1FQ80y7cRVQv3ZwgwZH/pQotV2E9FKv7gp4UaD+Df7dMLNd73O08VajyApxoFFlqsI+GOfl9Wwanhl9uj39X1TN8zSuWdJhBKqTwZ27Xw1vVdObk390pKKaWUKlQ6hUkppZRSSinlME0glFJKKaWUUg7TBEIppZRSSinlME0glFJKKaWUUg7TBEIppZRSSinlME0glFJKKaWUUg7TBEJlsCd0I2899xRv9H6cJf/7Jsv+ZfO+4+1+3Rnx36cZM2wgkWFnUvf17dKSkf2fZWT/Z/k8eGieY2/e8CfPPvkITz/Wle+++TrL/l3bt/H8s925t2VTVq/4I8/tA2zZ8Ce9nnyEZx/ryvfZxPjvs925v2VT1mSK8cagATx8bxuGv/py3mJu/JPeTz1Cz8ezj/liz+50bJUx5l+HDvJy35707fE4zz/9JKv+WOZQPGMM70+dzf39htF14DvsPXIsS534hMs8P3IcnV94kwdeHM6nM+am9XfPQR55OZj6XZ/jt/WheTrXwmCMYfQXX9Lx6Rfp9twr7Dv0l91647+aTfun+hLUpbvd/cvWbOC29o+w5+CRfPUnZEQvTi4fx/a5o/LVTlE6vGMzE15+hvED/8PaBXOy7N+y7BcmvtqHya/15au3X+LcyX8AOHlkP5Nf68vk1/oy6bXn2L95bSH33DGhG/+k3/89wnNPPsz/Zs3Isn/Pjm281LsHD7ZpxrqVWf+2XLoYxzPdOjN57EeF0Ft1PdD3jFI50++BUKmSk5KYPXEMg0ePp4yvP++93IfGzVpRvnLV1DqVqtdi+ITpuLm7s3LRPOZOn8QLb7wLQLFiboyYOPOqYiclJTHh0w/5ePwk/PwDeLH30zRv1YYqVaul1vEvW5ahb49k7uxZVx3j808/5KPxk/D1D2CAFaNyphivZRPj8R7PcDkhgcULfspTzC/GfMiHVsyBfezHHDJ8JD/OyRjTzd2doe+MIrBiJSLDw+nfuwdBzZrjWTLnLyBas3U3x06HsXTqB+w8+DejJn/DD5++naVe727306xhXf5NvELv4Z+wJnQXrYMaUt7Phw8G9WH6/KUOn2dhWrt5G8dOnuHXbyaya/8hRo0P4fuJWf8n3bZ5EN0f7kSnZwZk2XfxUjzfzltMw7o1892fbxauZ9IPy/n63efy3VZRSE5OYvFX43lm+CeU8vEj5I3/UjvobvwrVEmt06Ble+687yEADoSu57eZk3n6rY/wr1iVfh9OwdnZmQvRkUx+rS+1mtyNs7NzEZ1NVklJSUwe+xHvfTYRX/8AXnnuGe5q2ZpK6X4H/QLK8sqbwcz7zv7fllnTplC/0e2F1WVVxG6E94yI9ASCjDFZ/8Cl1akCLDLG1BeRxkB5Y8ySa9YpdUvREQiV6uihffiXr4BfuUBcXF1p2qYDOzZm/ESxTqMmuLm7A1C9zm1ER5wrkNgH9u0lsEJFygdWwNXVlXYd7uPPNasy1ClbrjzVa9REnOSqYhzct5fyFSpSzorRNpsY1bKJccedTSleoni+YrbpcB9/rnUsZoVKlQmsWAkAHz8/vMp4ExsTnWvMFRu30/WeuxERGtepzvmLlzgXFZOhjoe7G80a1gWgmKsL9apX5mykre3AAF9qV62Ik1yffx5WrN/MQ/e1RURoVK82F+IuEh4ZlaVeo3q18fPxttvGhK/n0Oeph3ErVizf/Vm37RDRsRfz3U5ROXXkAN5lA/EOKI+Liyv1776HA1v+zFDHvXiJ1NeJCQkgtvdqMTf31GThSuK/qeXXk0P7M/4Otu5wHxvXrc5QJ6BcearWqIk4ZX3PHz6wn5joSG5veldhdVkVsZv0PdMY6FzUnVA3j+vzDkEVieiIcMr4BaRul/H1IzoyPNv6a5ctokFQ2h/IxH//5d2XejN6UF+2/7k62+PsiQg/h59/Wmw//wAiwrOPfTUyx/C9BjHsxgxId15+AUReRcwD+/aQmJhIucAKudYNi4ymrG/ajXNZH2/ORWafeJyPu8TKzTto3qhunvtVFM5FRFHWzzd1O8DPh7CIrAlEdvYd+ouz4ZG0uSvoWnTvhnM+KoLSPv6p26V9fLkQlfU9umnpAsYN7MGy2SF07pX2oefJw/v5YnAvJr3ahwf7DrquRh8AIsPP4Zv+997Pn8hwxz74SE5O5qsvPqNP/0HXqnvqOnQ9vGdEZIGIbBWRvSLSzyrrJSKHRGQz0CJd3Rki8li67bhMbRUDRgFPisgOEXlSRNpYr3eIyHYRyXloW6lMdAqTuiobVizl2KEDvPbxxNSyj2bOo4yvH+FnTjFm2EACq1THv3zuN7wqZ5ER4Xw86h1eGz4SJzufduXHlaQkhnwyhf882IGKZf1zP+AGl5yczMdTZvD+0IFF3ZUbTrOOD9Os48PsWrec1T99yyMDhgFQoWZdBoz9mvCTx5g/8UNqNG6GawGM7FwPFs+fS1DzFhluJpXKSQG+Z3obY6JExAPYIiKLgZFAEyAWWAlsd6QhY8y/IvIO6aY8ichCoL8xZr2IeAIJ+e2wurXcdAlEUFDQMMC9qPtxI/jsf79l2C7j60d0eFjqdnREOGV8/LIct2/7FhZ/P5OhH0/McKNQxtdW169cILUb3sHxvw45nED4+vkTfi4tdvi5MHz9ssbOj8wxIq5BDLsxw9KdV3gYPnmIefFiHG8PeZme/V6kbv0G2dabvXg5P/62BoD6NatyNt0n8mcjo/D3KWP3uBFfzKRy+QCe7Xqfw30qCnMW/MqPS34HoH7tGpwNj0jdFxYeSYCv/alKmV28FM/ho8fpOdi2JiQiKoYBb3/AF+++Qf3aNQq+4zeAUt6+xEamfboaGxlBSe/s36P1727HomnjspT7VahMMXcPzp04SmD12tekr1fDx8+fiPS/9+Hn8PFzLFk+sGc3e3duZ/H8H0mIv0Ri4hXcPYrT67+agN7MCuM9Y40q9EtXFGKMCUm3/ZKIdLNeVwSeBlYZY8Kt438AauX13NJZD4wVkdnAPGPMyXy0pW5BN10CAbiHhoYGF3UnbgRr/44ckX67Sq26hJ0+SfjZ05Tx8WPz6j/o+3pwhmOOHznIrAkfMei9zyjllXbTdvHCeYq5ueNarBgXYmM4sm8XHR/r4XBf6tStx6kTJzhz+hS+fv6s/GMZb418P38nmEntTDFW/bGMNwo4ht2YJ9Nirv5jGcOCHYuZmJjIyGFD6NDpAVrf0yHHuj26tKdHl/YArNqykzmLltO5dTN2HvybksWL4+/tleWYcbPmceFiPO8O7Jnn8yps3R/uRPeHOwGwemMocxb8Sud2Ldm1/xCeJYpnu9Yhs5KeJVg/P22hf8/BbzPk+Wdv2eQBoHz1OkSdOUX0uTOU9PZlz58reOyltzLUiTxzEp9ytg8DDm/biE+5QACiz52hlI8/zs7OxISfJeL0Cbz8yhb6OeSkVh3b7/3Z06fw8fNnzR/LeG3Eew4dm77e70sWcuTAPk0ebgGF8Z6xkoWQLDsAEWkLdACaG2Muicgq4ABQL5uw/8/efYdHUXUBHP4dUghITbIJCSChF1FBovQAIkhREAsW9JOioFIUBAFRQUBsoICiIXQQC6CggCCIIl0IndClSE2XhJ5k7/dHlrAhCWxIsoHkvM/Dw86dO/ec2Uxgz86dmURsU9JFpABww1OAxpiPbGc12gBrReRhY8zeG22n1BV5sYBQN8nFxZXnXu3H2Hf6Yk1KomHLRyhdrgILZk4ioEo1atVrzNwpE7h48QLBo94BwNPiS+9hn3Dq2FFmffExIgUwxkrrji+kunvTDWO7utL7zbcY+EYvrNYkWj/SnoAKFZkW8jVVq9egQeMm7N0dxtBB/TkbH8f6NauZMXkiU7+de8PCT80AACAASURBVOPB7WL0evMtBttiPGyLMT3ka6rYYuzbHcYwW4wNa1Yzc/JEJtti9H2lG8eOHuHC+Qs82641/d5+l/vrNbhxzH5v8XbfXliTrsacMelrqlSrQX1bzPcH9yfeFnPWlIlMmj2Xv1YsZ+e2LcTFnWHZrwsBGDBkGBWrXP/b3SaB97AqdAcPdx+ER0F3Rr3eNWVdhz5DmT/+fU5HxTBxziIqlPHjiTfeB+C5ts156uEgdu4/TO9RXxJ39hx/btrGF7MXsOgrx/7zdIagunVY9fcWWr/wGh4eBRk54Op8/Me79+OnkM8AGD1xJr/+sYqLly7x4NMv8USbh+j54jPZns+sD3sQVKcq3iWKcGjpaIYH/8z0Bbfm7UzT4+LiQpuuvZn1wUCs1iRqN2uNT9ny/PHDNPwrVqFaYEP+XrqAQzs34+LiikeRonToORCAf/fuZPWC73BxcUUKCG27vc4dxYrn8h6l5uLqyqv9BvBuv95YrUm0aNuOchUqMmtyMJWrVadeoybs3xPGyLcHcDY+jo1rVzN7SghffzMnt1NXueQWOGaKA7G24qEaUA8oBDQRES8gDngK2G7rf4TkqU1zgHaAWzpjxgMp1zmISEVjzE5gp4jcD1QjuUhRyiFijMn0RoGBgcMc/ZY/M32zg7Pj3c5WH4rO/A8/C8qXKOjMcFidGg1u4lcpy8pGb79xp2xWoErDG3fKJonHw5wWC6Dwo2OcGg/g8tapTo33/fYTTo0H8My9pZ0W62BkvNNiXVHJotef3s5y6ZjJ8JZlIlIQWAAEAPuAEsAwoDwwGPgP2AZcNsb0EhFf4GeSi4ylJF/bUOSa27h6Ar+RXFx8CDQCmpH8X2UY0NkYcynbd1TlWXoGQimllFLqFmH7IN86nVUrgTRPIzXGhJN8luKKgbb2I0BN2+sY4H67Pj9kT7Yqv9LbuCqllFJKKaUcpgWEUkoppZRSymFaQCillFJKKaUcpgWEUkoppZRSymFaQCillFJKKaUcpgWEUkoppZRSymFaQCillFJKKaUcps+ByMcKu7k4NV74uUR879BDTil77rW73rhTNpoyZbhT4ymllMp79NOccqoynkVyO4W8xct5T4XODa5l7nJqPGc/FdrZxUN+oE+FVpmlx4xSmadTmJRSSimllFIO0wJCKaWUUkop5TAtIJRSSimllFIO0wJCKaWUUkop5TAtIJRSSimllFIOuy3uwhQYGDgI8HCwe0AOpqKUUkoppVS+dlsUEIBHaGjoMEc6BgYGOtRPpW/7xvXM/OozrFYrzVq3o92zL6Zav3jet6z89WcKuLhSrEQJuvd/B4uvH0cO7mfquI+5cP4cBQq48NhznanfrEUu7YVSeVfI0C60CbqXyJg4aj/1XraM+c+2jfw2cwLGaqVWszY0bP9sqvWbly8kdPnPFChQADePQrR9qS+WMgH8F3ma4De74OVfFoDSlarT5qW+2ZKTUkqpW9ftUkAoJ7AmJTHti08Z/PEXeFl8eKdnZ+5r0Jgy5Sqk9AmoVIWRX82goIcHy3/5ke9CvqTPux9Q0MODVwcOxa/MncRGRTLktRe55/563FFE76+tVHaauXAtX/2wgmkjXsqW8azWJJZMG0+ntz+hmJeFKUNeo0qd+ljKBKT0qdnwQeq0eBSA/aHrWD4rmOcGfwRASV9/Xv4oJFtyUUqlT0SmA4uMMfOuafcHxhtjnhSRpkB/Y8wj6Wx/BAg0xkQ5IV2VD9xsAXExE9/0B9xkDOVkB/ftxte/DL7+pQGo37QFm9euSlVA3FUrMOV15eo1WbtiKQB+Ze5MaS/pbaFYiZLE/RerBYRS2WzNlv2U8/PKtvFOHtyLZ6nSlPT1B+Cu+s3YH7ouVQFRsPAdKa8vX7qISLaFV0plgTHmJPDkzWwrIgKIMcaavVmp/OCmCojQ0NCPHO2rU4puH7FREXj5+KYse1p8OLg3LMP+fy79hXvvr5+m/eDeMBITE/H1L5MjeSqlsk98bBTFvCwpy0W9LJw8uCdNv9BlC9iweB5JiYm88M7olPb/Ik8zaVAPChYqTNOnu3BntXuckrdSeZmI/A/oDxhgB5AEBIlIP6AU8JYxZp6IBJB8ZqLmNdt7Ad8BpYH1gNjaA4DfgL+BOkAbEekIdAQKAvONMUNt/ZYAa4AGwAmgvTHmQk7ts7q96F2Y1E1Z8/sSDu/bwyMdn0/VHhsdxdcfDaNH/3coUEAPL6XyisCWj9Fr3Dc0f+5lVs//BoAiJTzp/cW3vPzRRFq88CrzvxjFpfPncjlTpW5vInIX8A7woDHmXuB12yo/oBHwCHCjL3KHAmuMMXcB84E77dZVBr6yratqW34AqAXUEZEgu34TbP3+A57I6r6pvCNfXwORybs75TkT5/+earmktw/REeEpyzGREXjafTN5xc7NG1nw7XTeHfM1bu7uKe3nz53l0yH96Nj1FSrXuDvnEldKZZuiJb2Ji45MWY6PjqRoSe8M+99VvxlLpowDwNXNHVe35H8D/CpUoaSvP9GnjuNfsWrOJq3UbU5EugPd7ZpCjDFXLiZ6EJh75XoFY0xM8mwjFtimG+0WEV+uLwh43Lb9YhGJtVt31Bizwfa6pe3PVttyEZILh3+Bw8aYbbb2zeiUdGUnXxcQZOLuTnnR5mP/DbVfrli1OqdPHCPi1Ek8vS2sX7mcXm+PSLXNkQP7mDL2IwZ+OJbiJT1T2hMTEvh82EAat2hN3aDmztkBpVSW+VesRszpE8RGnKKYpzdh6/+kQ68hqfrEnDqOp1/ylMQDWzfgWSr5Oqlzcf9RqEhRChRwITb8JLGnj1PS18/p+6DU7cZWLGT27gOX7F5n5Uok+9OEAnxojJlo38E2hck+XhJQKAsxVR6T3wsIZcfFxZXOvfvz0aA+WK1WmrZ6lDIBFZg7fSIVqlSnToMgZod8wcUL5xk/4m0AvHxK0X/EaDb89Tt7d2zlbNwZVi1bDECPAe8RUKlKbu6SUnnOrA97EFSnKt4linBo6WiGB//M9AWrb3q8Ai4utOrcm+8+HIjVaqVW09ZYygawcu40/MtXpUpgAzYtW8DhnVtwcXXF444itHt1IAD/7tnBX3On4+LqiojQutsbFCpSLLt2Van86g9gvoh8ZoyJFhHPG26R1irgOWCkiLQGSmbQ7zdghIjMNsacFZHSQMLNpa3yEy0gVCq16zakdt2Gqdqe6twj5fWQT79Md7tGD7Wm0UOtczQ3pRS8MHjijTtlUqXadalUu26qtqZPdUl5/fCLvdLdrnrdIKrXDUp3nVLq5hhjwkTkA+AvEUni6vSizHgf+E5EwoB1JE9JSi/WMhGpDqy3TZM6CzxP8hkHpTKkBYRSSiml1C3EGDMDmHGd9UVsfx8BatperwRW2l5Hk3xtw7WirvS3G2scMC6dvjXt+oxOZ73Kx/Q2OUoppZRSSimHaQGhlFJKKaWUcpgWEEoppZRSSimHaQGhlFJKKaWUcpgWEEoppZRSSimHaQGhlFJKKaWUcpgWEEoppZRSSimH5cXnQFwMDAwc5mDfgBzM45Y3bMkep8dc2L2+02PmZUlHtzs9pku5e50e01m+337CqfGmTBnu1HgA3bq959R4+5aP43BUvFNjlvcu6tR46vZ2MNK5xydAJYseo+r2lucKiNDQ0I8c7ZuJQkMppZRSSimFTmFSSimllFJKZYIWEEoppZRSSimHaQGhlFJKKaWUcpgWEEoppZRSSimHaQGhlFJKKaWUcpgWEEoppZRSSimH5bnbuKqsua9MCV5uEEABEZbvDWfe9pOp1jevYqFL3XJEn7sMwOKw0yzbF8HdfsV4qX5ASr8yJQrx6Yr9bDga68z0FWCMYdRX01i1aSuFChZkVP/XqFG5Qpp+Y6d9xy/LV3Hm7Fk2/zIr1bolf61jwqy5iAjVKpTj08GvOyv9W86BbRtZMu1LjNXKfc3b0Pix51Kt37TsFzb+9jMFChTA3aMQj/boh0+ZAI4f3MPCiZ8BYDA0e+pFqj/Q2KGY/2zbyG8zJ2CsVmo1a0PD9s+mWr95+UJClyfHdPMoRNuX+mIpE8B/kacJfrMLXv5lAShdqTptXuqbpf0PGdqFNkH3EhkTR+2nsucZEqEb1vH12NFYrVZaPfoYT7/QOdX6ndu2EDxuDIf/Ocjg9z+gcbOHAAg/fYrhg/tjrIbExETaP9mRth2ezJaclLIXumEdIeOSj9GWjzxGx2uO0V3bthAyPvkYHTjsAxrZjtErzp87yyvPd6R+4ya82m+gEzNXyjm0gFApCgi80qg87y7eTfS5y3zW4W7+PhrLsf8upOq3+lA0E9ceTtW281Qcr/+0A4AiBV0Jebo2W4+fcVru6qpVm7Zy9MRplk4bz469B3h//GR++GJUmn7N6tWhU7tWtOrSJ1X7kROnmPT9AmZ/PoLiRYsQHZt/f45WaxKLp4zjf+98SjEvCyGDX6VqYAN8ygSk9Lm7UXPub9kOgL2ha/ltxte8MORjfMqWp/tHwbi4uBAfG83XA16mSp0GuLi43DDmkmnj6fT2JxTzsjBlyGtUqVMfi13Mmg0fpE6LRwHYH7qO5bOCeW5w8iNwSvr68/JHIdn2HsxcuJavfljBtBEvZct4SUlJTBjzMaPGTsDbx5c+L/2Peo2CKFf+apFr8S3Fm0OG8eN3qQtbTy9vPp84DXd3dy6cP0+PF56mXqMmeFks2ZKbUpB8jH792ceM/Dz5GO1rO0bvvOYY7fv2MH665hi9YtakYGreWzvHchSRzkCgMaZXjgVR6jp0CpNKUdlShFNnLhIef4lEq2HVP1HUDSiZ6XEalvdk87FYLiVZcyBLdSN/rAulfYsgRIR7q1ch/tw5IqPTngm6t3oVLF5pf77zfl3Bc+0epnjRIgB4lSye4znfqk4c3ItnqdJ4+vrj6upGzQYPsnfTulR9PArfkfI64eJFEAHAvaBHSrGQmHA5pf1GTtpilvT1x8XVjbvqN2N/aOqYBe1iXr500dGhb8qaLfuJPXMu28bbtycMvzJl8StdBjc3N5o0b8n61X+l6lPKz58KlSojkvq/KDc3N9zd3QFISLiMMfpvjMp++/eE4W93jAY91JINa1Ifo75+/pSvVBkpkPZj1IG9e/gvNpraD9RzVsopRES/GFZOoQeaSuF1hztR5y6lLEefu0wVn6Jp+jUo78ldpYpy8sxFJq8/QpRtOtMVjSt68/POk2m2U84RER1DKYt3yrKvtxfh0THpFgvpOXI8+WfX6Y13SbJa6fnCUzS+v1aO5Hqri4uJoriXT8pycS9vjh/Yk6bf30sXsH7xXJISE+n83piU9uMH9rDg6084ExnO470H3/DsA0B8bBTFvK5+o17Uy8LJg2ljhi5bwIbF80hKTOSFd0antP8XeZpJg3pQsFBhmj7dhTur3ePw/jpDdGQEFh/flGVvHx/2he1yePvI8NO8O+ANTh0/Rreer+vZB5XtoiMj8LY/Ri0+7Nvt2DFqtVqZ8uXn9H9vBFtDN950DiKyACgLeADjjDEhItIFGAz8B2wHLtn6TgcuArWBtUC/mw6slIO0gFCZsvFoLH8djCLRamhV3Yc3mlbincW7U9aXLORGgGdhthzLv9NebndJVitHT5xi+uihhEfG8L83h7IgZDTFitxx443zqbqtHqNuq8fYsWYFf/34DY/3GgRAmcrV6fXZNCKPH2X+hI+oVKsubrZv0LMqsOVjBLZ8jF1rV7B6/je0f20QRUp40vuLbylctDinDu1nzpj3eOXTKanOWNzuLL6lCJ75PdGRkbw/+E0aN2tOSU+v3E5LKQAWz59LYP2GqQqQm9TVGBMjIoWATSKyGHgfqAOcAf4Ettr1LwM0MMYkZTWwUo7I7wXExcDAwGG5nURu8ev+Rarl6HOX8b6jYMqy1x3uRNudkQCIv5SY8nrZ3gg61y2Xan2jil6sPxJDkjE5kLHKyLe/LGXurysAuLtqRU5HRqWsC4+KxtfL0+GxfL09uadaZdxcXSnj50O5Mn4cPXGKu6tWyva8b3XFPL05Ex2RsnwmOoqinhl/412zQTMWTRqbpt1SphzuHoWIOHaY0hWrXjdm0ZLexEVHpizHR0dStKR3hv3vqt+MJVPGAeDq5o6rW3KB4lehCiV9/Yk+dRz/G8R0Ji+LD5ER4SnLUREReFl8rrNFRuNYCKhQkV3bt6ZcZK1UdvCy+BBlf4xGOn6M7t21k7DtW1k8fx4XL5wnISERj0KF6fJq71T9RKQ70N2uKcQYY3/xUh8R6WB7XRZ4AVhpjIm0bf8DUMWu/1wtHpQz5esCIjQ09KPcziE3PRqyfqj98oHIs/gX98C3aEGiz10mqKI3o/84kGqbkoXciL2QAMAD5Tw5Fpv6Auugit7M3PhvDmeurvVcu1Y8164VAH/9vYXZPy+lTdOG7Nh7gKJ3FHZ4+hJA8wYP8Oufa3j84WbEnonj6PFTlPXL8rdptyX/itWIOXWC2IhTFPX0Zte6P3iyz5BUfaJPHcfLrwwAB7ZswMuvNACxEaco5uWDi4sL/0WeJurkMUpYSjkW83RyzGKe3oSt/5MOvVLHjDl1HM8rMbduwLNUcsxzcf9RqEhRChRwITb8JLGnj1PS1y/L70N2qlqtBiePH+P0yRN4WXz4a8UyBg4d6dC2kRHhFCtenIIFPYiPiyNsx3Y6PN0phzNW+U2VajU4cezqMbrq92UMcPAYte+3/NeFHNy7O03xAGArFtK924GINAUeAuobY86LyEpgL1DjOqGz70IlpRyQrwsIlZrVQPDaw7zfujoFCgi/74vg39gLdKpTlgNRZ9l4NJZHa/pRt1xJkowh/lIi41YeTNnep0hBLEUKsutUXC7uhQp6oDarNm6hVec+eBR054P+r6Ws6/DKAOYHfwrA6EnfsPjPNVy8dJlmz73CE60epNf/OtIo8F7Wbd7OIy/1xaVAAfq//DwliqW9FiY/cHFxoU3X3sz6YCBWaxK1m7XGp2x5/vhhGv4Vq1AtsCF/L13AoZ2bcXFxxaNIUTr0TL5l4797d7J6wXe4uLgiBYS23V7njmI3viC9gIsLrTr35rsPB2K1WqnVtDWWsgGsnDsN//JVqRLYgE3LFnB45xZcXF3xuKMI7V61xdyzg7/mTsfF1RURoXW3NyhUpFiW3oNZH/YgqE5VvEsU4dDS0QwP/pnpC1bf9Hgurq681ncAQ/r1xpqURMtH2hFQoSIzJwVTuVp16jduwr49YYwYPID4+Dj+XruaWZNDCJk9h2NHDhPy5VhEBGMMTzz7POUr5r8zYypnubi68mq/AbzbrzdWaxIt2rajXIWKzJqcfIzWa9SE/XvCGPn2AM7Gx7Fx7WpmTwnh62/mZFcKxYFYW/FQDagHFAKaiIgXEAc8RfJ1EErlCjE5PNUkMDBwWGho6DAH+w4i+YKhawWEhoZ2zs68FDwast7p84wWdq/v7JB5WtJR5///4VLuXqfHdJbvt59waryEJOdP9evWLXue5eCofcvHOTUeQHnv/FnwqptzMDLe6TErWYpmeO80ESkILAACgH1ACWAYUJ6rF1FvAy4bY3rZLqJeZIyZl7NZK3XVrXYGwiO9YiM/X6eglFJKqfzDGHMJaJ3OqpXAtHT6d87hlJRKQ58DoZRSSimllHKYFhBKKaWUUkoph2kBoZRSSimllHKYFhBKKaWUUkoph2kBoZRSSimllHKYFhBKKaWUUkoph2kBoZRSSimllHLYrfYcCOVE4x6vmdspKKWczNkPdqva4nWnxtu2ZCy7T8c5NWaNUll72ndmWQ+FOjUeQIEKgU6P6SwBCSdzIWrVXIipVPbRAkKp21hefip0bnjm3tK5nUKOOxzl/KfuKqWUylt0CpNSSimllFLKYVpAKKWUUkoppRymBYRSSimllFLKYVpAKKWUUkoppRymBYRSSimllFLKYVpAKKWUUkoppRymBYRKJXTDOl565nG6dnyMObOmp1m/c9sWenXpRNuguqz+8/c068+dO8vzj7XhqzEfOyFbpVR2CN2wjm7PPE6Xjo/xQwa/9z27dKLNNb/34adP0bNLJ1578Tm6d+rI4vnzspxLyNAuHF8xlq1zh2d5rCu2/L2Ons8/wavPdeDH2dPTrA/bvoU3X3qeJx6sx7qVK1Ktm/H1ePq82JFeLzzF5HGjMcZkW17ZyRjDB1/P4OGu/Wj/6iDCDh5Ot9/Y6XNo9kJv6nTomqr9ZEQULw4cyeM936b9q4P4a+M2Z6R9yzLGMGp8CK06dadDt97s3v9Puv3GTZ5F845dCWzdMVX79DkLeLRzTzp0603Xfu9w8nSEM9JWymm0gFApkpKSmDDmY0aMGc/E2XNZ+ftvHD18KFUfH99SvDlkGM1aPJzuGLMmBXN3rdrOSFcplQ2u/N6PHDOekAx+7y0Z/N57ennz+cRpfDXjW8ZNms4P38wgOjIyS/nMXLiWR3p+lqUx7CUlJREy9hPe/WQc42fMYc2KZRw7cs3++ZSi9+ChBDVPvX97d21n767tfD71O8ZN/54De3cTtm1LtuWWnVZt2s7Rk6dZOmUM7/fpxvAvp6Xbr2nd2vwwLm1xFvzdAlo1rsdPE0YxZlAvhk9If/v8YvXfmzl64iRLvpnIsDd7Mvzzr9Pt17TB/Xz/9eg07dUrV2BO8GfMn/IFLZs0YMzE6TmcsVLOpQWESrF/Txj+ZcriV7oMbm5uNGnekg2r/0rVx9fPn/KVKiOS9tA5sHcPsTHR3Hd/PWelrJTKon17wvC75vd+/TW/96X8/KmQzu+9m5sb7u7uACQkXMYYa5bzWbNlP7FnzmV5nCsO7AnDr3RZSvkn71+jB1uwcU3q/fPx8yegYmWkgFyztXD58mUSExNITEggKSmR4iU9sy237PTHhs20b94YEaFW9crEnT1PRExsmn61qlfGx7NkmnYR4ez5CwDEn7+Aj1faPvnJH2v/pl3LZogI99aoRvy5c0RGx6Tpd2+Nali80h4TdWvfQyGPgrY+VTkdGZWp+CKy7uYydy4RaSoiizK5zUoRybuPNs8ntIBQKaIiI7D4+KYse/v4EB3p2GlXq9XKpC8/56Veb+RUekqpHBCdhd97gMjw07zyv2d4oUNbnur0Il4WS06kedNioiLxtts/L4sv0VGOnSWpVvMe7q5dh66Pt6br462odX89ygaUz6lUsyQ8OoZS3l4py6W8PYmISltAZKTn84+z8M81NH2+F6+89wnvvPpiTqR524iIiqaUz9Vj2dfbi/Co6Jsa68dfl9O4bp1MbWOMaXBTwZRyEi0gVLZY9NNc7q/fMNUHEaVU3mfxLUXwzO+Z+sMCfl+yiNiYm/uQdSs6dfwYx48eYfLcxUye9ys7t4Sye/vW3E4rR/y6cj0dHgpi5TdfEjz8LQZ++hVWa9bPKOV3C5f/Sdi+g3R9+vFMbSciZ21/+4nIKhHZJiK7RKTxdbZpJSJbRGS7iKywtT0gIutFZKuIrBORqrb2ziLyk4gsFZEDIvLJDca5Q0SmishG21jt04mfbh8RKSQi34vIHhGZDxTK1JuhbkmuuZ1AdgkMDBwEeOR2HreTOUv/TLXsbfEhMiI8ZTkqIgIvi49DY+3ZtZOwHVtZ9NM8Ll44T0JCIh6FC9P11d7ZmrNSKnt5ZeH3PvU4FgIqVGTX9q00bvZQdqaYJZ7eFqLs9i86Mhwvb8fOkmxYvZIqNWpSqHBhAO6rW599YTupce+tcZ3X7IXLmGf7d7xmlQqctvuG/HRUDD7ejk9DmvfbSiaNHAhA7eqVuZSQQGxcPF4limdv0rewb+cvZt7iZQDUrFaZ0xFXz1SFR0Xja3eGxxHrN28j5Ju5TB87Cnd3t1TrRKQ70N2uKcQYE5LOMM8BvxljPhARF6BwerFExAJMAoKMMYdF5Mq8qr1AY2NMoog8BIwCnrCtqwXUBi4B+0TkC+BiBuMMAf4wxnQVkRLARhG59k4qGfXpAZw3xlQXkXuAW/NCIpUpeaaAADxCQ0OH5XYSt5NDUfFD7ZerVKvByePHOH3yBF4WH/5asYyBQ0c6NNbAYVf7LV+8kAN7d2vxoNRtoGoWfu8jI8IpVrw4BQt6EB8XR9iO7XR4ulMOZ5w5lavV4NTxfwk/dQJPbx/W/LGcvu+OcGhbi68vyxctICkxEUPy3ZoeffLZnE04Ezo92pJOj7YEYOXGrXy7cBltmtRn+96DFL2jULrXOmTE38eLDdt20aFFE/759wSXLifgWbxYTqV+S3quQ1ue69AWgL/Wb+LbBYtp82AQO/bso8gdhdO91iEjew78w/uffcXEj4fhVbJEmvW2YiG9guFam4CpIuIGLDDGZHR7rHrAKmPMYdv4Vy7YKA7MEJHKgAHsK5kVxpgzACKyGygHlMxgnJZAOxHpb1v2AO68JoeM+gQB423j7RCRHQ7st7rF5aUCQmWRi6srr/YdwDv9epOUlETLR9pRrkJFZk4Kpkq16tRr3IR9e8IYMXgAZ+Pj+Hvtar6ZHMLE2XNyO3Wl1E1ycXXltb4DGNKvN1bb732A7fe+crXq1Lf7vY+3/d7PmhxCyOw5HDtymJAvxyIiGGN44tnnKV+xUpbymfVhD4LqVMW7RBEOLR3N8OCfmb5gdZb27+U33uL9/n2wWpNo3qYdd5avyLdTgqlUrToPNGzCgT1hfPzuW5yNj2PTujV8P20i42fMoX6T5uzcEsrrXZ5FRKj9QH3ubxiUpf3LKU3ur8WqTdt4uGs/PDzcGdW3R8q6Dj0HM3/ChwB8OuVbFv+5jguXLtP0+V482aoZvZ5/grde6sR74yczY/5SRODDfj0Qufai8vwjqF4gq/7eTOvne+BRsCAjB/ZJWff4S6/z0+RxAIwOnsavK1Zx8dIlHnyqC0+0bUHPzs8xOng65y9coO+w5Fua+/lamPDBO5nOwxizSkSCgLbAdBH5zBgzMxNDjAD+NMZ0LGmZqgAAIABJREFUEJEAYKXdukt2r5O4/mdCAZ4wxuxL1Sji60CfTKSrbheS0/e0DgwMHObomYGM+joyRmbiqGSHouKdfkPzCt5FnR1SKWXncFS8U+NVbfG6U+NtWzLWqfEAapRy7jf11kOhTo0HUKBC3r1pTuLJfTfulM1c/ate91O1iJw1xhQRkXLAcWNMkoj0AioZY9LcrcQ2hWkLdlOPjDExtmsOvjHG/Cgiw4DOxpgAEekMBBpjetm2XwSMBsIyGGcUUAzobYwxIlLbGLNVRJoC/Y0xj1ynTz+ghjHmJRGpCWwD6hljnH8gq2yjF1ErpZRSSt2amgLbRWQr8DQwLr1OxphIkq+p+ElEtgM/2FZ9Anxo2/6Gs06uM84Ikqc/7RCRMNvytTLq8zVQRET2AMOBzTfKQ936dAqTUkoppdQtxBhTxPb3DGCGg9ssAZZc07YeqGLX9I6tfTow3a7fIzcY5wLJF0NfG3MltmlR1+lzAXjGkX1Qtw89A6GUUkoppZRymJ6BUEoppZS6TYjI30DBa5pfMMbszI18VP6kBYRSSiml1G3CGFM3t3NQSqcwKaWUUkoppRymBYRSSimllFLKYVpAKKWUUkoppRx2u1wDcTEwMHDYDfoEOCGPPOVE3GWnxnMpAKfPRjstXvhZ5+5fgtXpz+XjiZLOez+vcCl7t9NjOsvBSOc+ZA2gkiVvP1zR2Q92q9U6zTO2ctS+5eOc/nA+ilWlXJzzH36mlFJX3BYFRGho6Ec36uNAgaGUUvleeSc/DX736Tinxssv8vKToZ3N1b9qbqeg1G1HpzAppZRSSimlHKYFhFJKKaWUUsphWkAopZRSSimlHKYFhFJKKaWUUsphWkAopZRSSimlHKYFhFJKKaWUUspht8VtXJXz7ArdwHfBY7Fak2jc6lHadPxfqvXLfvqO1UsXUsDFhaLFS9Cl79t4+foB8HLbRpQJqAiAp8WX3sM+uWG8nZs28G3wWKxJSQS1fpS2T6eO99uP37HKLl7Xfm/j7etHVPgpvhg+GGM1JCUm8lD7J2n2SIcbxtu39W8WTv0SY03i/uZtafp4p1TrN/z2M+uXLqBAgQK4exTi8Vf641s2gHPxZ5j96VCO/7OXOk1b0f5lx+81f2DrRhZPS45Zp3lbgjo8l2r9xmW/8LddzPY93sSnbAAHt4eyfHYIiYmJuLq68vALr1Dh7vtuGM8Yw6gJU1m1cSuFCroz6q1e1KhcIU2/sVO/5Zflf3Em/hybF32T0j7/tz8ZHTILH29PADq1b8WTbR5yeH/zmtAN6wgZNxqr1UrLRx6j4wudU63ftW0LIePHcPifgwwc9gGNmqV+r86fO8srz3ekfuMmvNpvoBMzv3Vt+XsdU74Yg9Vq5aG27XmiU+dU68O2b2HqF59x5NBB3nzvAxo0bZ6ybsbX49m8YQ1Wq6FWYF269XkTEclSPiFDu9Am6F4iY+Ko/dR7WRoLko+Zr8cmHzOtHn2Mp685ZnZu20LwuORjZvD7H9DYdsyEnz7F8MH9MVZDYmIi7Z/sSNsOT2Y5H6WUym5aQKgU1qQkZk8YTb9R4yjp7cPI17tRq25j/MuVT+lzZ8UqvDN+KgU9PPhz0U/MnfoVrwweAYC7e0GGTpiRqXizJoym/4fj8PT2YXjvbtSq15jS18R774vkeH8s/Ik5k7/itSEjKOHpzTufh+Dm7s7FC+d5p8fz1KrfiJJeluvG+3nSOLq9N5riXha+HPgK1e9viG/ZgJQ+tRo/RL2H2wOwe9NaFk+fQNd3P8XNzZ2Wz3bl9L+HCf/3cKb2ceGUcXR+91OKeVoIHvwK1QIb4GMX855GzXmgZTsA9mxay5IZX/HiO59QuFhxOg0aRTFPb8L/PcyMkW/xVsjcG8ZctXErR0+cYumML9ix5wDvjwvhhy/TPkqlWb1AOrVvTasXe6dZ17ppA97p/ZLD+5lXJSUl8fVnHzPy8wl4+/jS96X/Ua9REHeWv1qQWXxL0fftYfz03ax0x5g1KZia99Z2Vsq3vKSkJELGfsKwMV/iZfHlrR4v8kDDIMoG2L2nPqXoPXgoP3//Tapt9+7azt5d2/l86ncAvN3rZcK2baFm7TpZymnmwrV89cMKpo3I+jGflJTEhDEfM2ps8jHTx3bMlLvmmHlzyDB+vOaY8fTy5vOJ03B3d+fC+fP0eOFp6jVqgpcl43/XlFIqN+gUJpXi8P7d+PiXweJXGlc3Nx5o8hDbNqxO1afavXUo6OEBQMVqdxEbFXHT8Q7tS47ncyVe04fYuj51vOq17OJVvxrP1c0NN3d3ABITEjAOPAX62MG9eJUqjVcpf1zd3Li30YPs3rQ2VR+PwnekvL588SLYvtl09yhEQPV7cHVzz9Q+Hj+4F69S/nj6Jse8u+GD7AnNOGbCpYsp36b6l69MMU9vAHzKBpB4+RKJCTd+uvYf6zbRvkVTRIR7a1Qh/ux5IqNj0/S7t0YVLF4lM7U/+c3+PWH4lymLX+kyuLm5EfRQSzas+StVH18/f8pXqowUSPvP6YG9e/gvNpraD9RzVsq3vAN7wvArXZZS/snvaaMHW7DxmvfUx8+fgIqVkQLXnlkQLl++TGJiAokJCSQlJVK8pGeWc1qzZT+xZ85leRyAfXvC8LM7Zpo0b8n61an3r5SfPxUqVUYk9THj5uaGu+3ftYSEyxhjzZac1O1HRM5m0zidReTL66x/TERq2C0PF5H8e8pZOUzPQKgUsVGRlLT4piyX9LZwaN/uDPuvXraIuwOvfjBKuHyZEX264lLAhdYdn6d2gybXjxcdiaddPE9vC//szTjeqqWLuPv+q/GiI8IZ+15/Ik4ep+NLva579gEgLiaS4t5X+xT3tHDsQNp465fMZ/XCuSQlJvDysM+vO+aNxMVEUdzLJ1XM4wf2pOn399L5rF00j6TEBLoO/SzN+rANq/CrUNmhAiYiKppSFq+UZV+LJ+FR0ZkqFpat3kDojt0ElPFn4Kud8fPxdnjbvCQ6MgJvn6vHqLfFh327dzm0rdVqZcqXn9P/vRFsDd2YUynedmKiIlO9p14WX/bvcew9rVbzHu6uXYeuj7cGY2jdoSNlA8rfeEMnio6MwGJ/zPj4sC/Msf0DiAw/zbsD3uDU8WN06/m6nn1QOe0xYBGwG8AYk/U5fCpf0DMQ6qas/2MpR/fv5eEnrl5D8PGMn3h3/FReHjiM7yeOI+Lk8WyLt27FUo4c2EvrJ6/G8/LxZUTwLD6aNoe1y3/lTGxMtsSq37oDb331La1f6MEfP6Y/LSW71W3VgX5fzqZlp+6svCZm+LHDLJsdQvvu/ZySS7N6gfz+zdcsmPQZ9evcw9ufZPjllbqOxfPnEli/YaoPyyprTh0/xvGjR5g8dzGT5/3Kzi2h7N6+NbfTylYW31IEz/yeqT8s4Pcli4iNic7tlFQuE5EBIrJJRHaIyPt27QtEZLOIhIlId7v2LiKyX0Q2Ag2vM24DoB3wqYhsE5GKIjJdRJ60rT8iIh/a1oWKyH0i8puI/CMir9woP5W3OeMMxMXAwMBhDvYNcFIcBXw+57dUyyW9LcRGhqcsx0ZFpvut/u6tm1j8/Qze+mRCyjSiK9sDWPxKU/We+/j3n/34+JfJMH5JLwsxdvFioiJTxrAXtmUTi76bwaDRqePZj1M6oAL7d23j/sYPZhivmKeFM1GRKctnYiIpdp2zFvc0fJD5IVk7A1HM05sz0VeneZ2JiaSoV8bf5t/d8EEWThp7tX90JN99+h5P9BqEZ6nSGW737c9LmPvriuQxqlTkdOTVDx3hkTH4entltGkaJYoXTXn9ZOvmjAn55jq98zYviw9REVeP0ajICLwsPtfZ4qq9u3YStn0ri+fP4+KF8yQkJOJRqDBdXk17zUl+4ultSfWeRkeG45XO7316NqxeSZUaNSlUuDAA99Wtz76wndS4ha4x8bL4EGl/zEQ4fsykHsdCQIWK7Nq+NeUia5V32D7wd7drCjHGhKTTryVQGXgAEOAXEQkyxqwCuhpjYkSkELBJRH4E3IH3gTrAGeBPIN0q2xizTkR+ARYZY+bZ4l3b7V9jTC0R+RyYTnJB4gHsAoJvkJ/Kw3K8gAgNDU179WYGslIAZCaOSrb6UPRQ++WAKtUJP3mcyNMnKellYeNfv/PywGGptvn34D5mjf+YN0Z+TrESV+cen4uPw72gB27u7sSf+Y+Du3fQ6snUdzi6Vvmq1Yk4YRdv5e/0GJQ63tGD+5gx/mP6fZA6XkxkBEWKFce9YEHOxcdxIGwHLR9/5rrxylSqSvSp48SEn6KYpzfb1/zBs2+8k6pP1MnjeNuKnr2bN+Dtl/GHdkeUrlSN6FMniA0/RVFPb3au/YOnXk8dM/rUcbz8kmPu37IBL1vMC+fOMuvDQbTo9DLlqt193TjPtW/Nc+1bA/DXhs3M/nkJbZo1ZMeeAxS9o3Cmpi9FRsem9P9zfSgV7szae3A7q1KtBieOHeP0yRN4WXxY9fsyBgwd6dC29v2W/7qQg3t35/viAaBytRqcOv4v4adO4Ontw5o/ltP33REObWvx9WX5ogUkJSZiSL5b06NPPpuzCWdS1Wo1OHn86jHz14plDHTwmImMCKdY8eIULOhBfFwcYTu20+Hp6/87qm5PtmIhTcGQjpa2P1eKgCIkf2BfBfQRkSu3Hyxray8FrDTGRAKIyA9AlSyk+ovt751AEWNMPBAvIpdEpMQN8lN5mF4DoVK4uLjy3Kv9GPtOX6xJSTRs+Qily1VgwcxJBFSpRq16jZk7ZQIXL14geFTyh+Art2s9dewos774GJECGGOldccXUt29KaN4nXr2Y8zbfZNvG9vyEUoHVGD+jOR4tes3Zs6kCVy6cIGvRibH8/Lx5fX3P+HUv0f4ftIXCILB0OrJZylbvuIN47V76XWmjhiA1Wol8MHW+N5ZnmXfTaVMparUuL8h65bM5+COzbi4ulDojqJ07DU4ZfuPXnmaSxfOk5SYQNjGNXR7b3SqOzilH9OFR7r1YcYHb2G1WrmvWWt8y5ZnxfdT8a9Yler3N2TDkvn8s3MzLi6uFCpSlMd7DQKSr4uIOX2SlXNnsnLuTABefPdTihS/fjEQVPc+Vm3cQqv/9cKjYEE+GPBayroOPfozf+JoAEaHzGLxH6u5eOkSzZ7pzhOtm9PrxaeZNf9X/ly/CVcXF4oXLcKot3pdN15e5uLqyqv9BvBuv95YrUm0aNuOchUqMmtyMJWrVadeoybs3xPGyLcHcDY+jo1rVzN7SghffzMnt1O/Zbm4uvLyG2/xfv8+WK1JNG/TjjvLV+TbKcFUqladBxo24cCeMD5+9y3Oxsexad0avp82kfEz5lC/SXN2bgnl9S7PIiLUfqA+9zcMynJOsz7sQVCdqniXKMKhpaMZHvwz0xesvvGGGezfa30HMKRfb6xJSbR8pB0BFSoyc1LyMVO/cRP27QljxOABxMfH8ffa1cyaHELI7DkcO3KYkC/HIiIYY3ji2ecpX7FSlvdP3dYE+NAYMzFVo0hT4CGgvjHmvIisJPnMQHa7ZPvbavf6yrJrRvmpvE+MufHda5wlMDBwWGho6LDcziO/WH0o2qk/fBcnX3ETfvbGdyzKTgkO3Akquz1R0vnzo13KXv9syO3sYGS802NWshS9cafb2O7TcU6NV6u1489oyQ77lo9zarwrynvn7eMmH7juw0tE5KwxpohtitAIoLkx5qyIlAYSgPrAS8aYR0WkGrANaAXsAzYA9wFxwB/AdmNMut8EicgXwBZjzDTb8nRsU5pE5AgQaIyJEpHOtte9bP2OAIG2OGnyM8bc/C0a1W1Bz0AopZRSSt2CjDHLRKQ6sN52fcJZ4HlgKfCKiOzhatGAMeaUiAwD1gP/kVxYXM/3wCQR6QNk+qmF18lPC4g8TgsIpZRSSqlbiDGmiN3rcUB6p7paZ7DtNGCag3HWAjXsmjrbrQuwez2d5Iuo01uXUX4qD9PbuCqllFJKKaUcpmcglFJKKaXyMBEZAjx1TfNcY8wHuZGPuv1pAaGUUkoplYfZCgUtFlS20SlMSimllFJKKYdpAaGUUkoppZRymE5hUkoplWc4+7kMVVu87tR4kHvPnlBKqSu0gMjHGlfwyu0UVJb553YCeUpef6hbbqhRqphT4x2Ocv7DAJ2taovXubx1am6noZTKx3QKk1JKKaWUUsphWkAopZRSSimlHKYFhFJKKaWUUsphWkAopZRSSimlHKYFhFJKKaWUUsphWkAopZRSSimlHKYFhFJKKaWUUsphWkAopZTK00I3rKPbM4/TpeNj/DBrepr1O7dtoWeXTrQJqsvqP39PaQ8/fYqeXTrx2ovP0b1TRxbPn5ct+YQM7cLxFWPZOnd4toynlFLOpgWEUkqpPCspKYkJYz5m5JjxhMyey8rff+Po4UOp+lh8S/HmkGE0a/FwqnZPL28+nziNr2Z8y7hJ0/nhmxlER0ZmOaeZC9fySM/PsjyOyh9EZJiI9M/B8duJyKCcGj+DmL+KSAnbn9fs2v1FJHsq9atjprx/IjJcRB66Tt/HRKRGdsbPq7SAUEoplWft2xOGX5my+JUug5ubG02at2T96r9S9Snl50+FSpURSf1fopubG+7u7gAkJFzGGGu25LRmy35iz5zLlrGUyipjzC/GmI+cHLONMeY/oATwml37SWPMkzkY9z1jzO/X6fIYoAWEA7SAUEoplWdFR0Zg8fFNWfb28SE6MsLh7SPDT/PK/57hhQ5tearTi3hZLDmRplKpiMgQEdkvImuAqra2l0Vkk4hsF5EfRaSwiBQVkcMi4mbrU+zKsoj0EZHdIrJDRL6/TqzOIvKl7fV0ERkvIutE5JCIZPhhXkQmiEg72+v5IjLV9rqriHxge71ARDaLSJiIdLfb9oiIeAMfARVFZJuIfCoiASKyyy6vn0RkqYgcEJFP7LbvZnt/NorIpCv5O/C+Tr+yTyLykd37M1pEGgDtgE9t+VR0ZMz8yjW3E7iewMDAQYBHbueRV4WGhuZ2CkopdUuz+JYieOb3REdG8v7gN2ncrDklPb1yOy11m7N9mO5u1xRijAmxrasDPAPUIvlz2hZgM/CTMWaSrc9IoJsx5gsRWQm0BRbYtvvJGJNgm5ZU3hhzSURKZCI9P6ARUA34BchoStFqoLGtT2nbdtjarhQsXY0xMSJSCNgkIj8aY6LtxhgE1DTG1LLtV8A1MWoBtYFLwD4R+QJIAt4F7gPigT+A7ZnYP0TEC+gAVDPGGBEpYYz5T0R+ARYZY7J1GlVedEsXEIBHaGjosNxOIg8bmtsJKKVUTvKy+BAZEZ6yHBURgZfF5ybGsRBQoSK7tm+lcbMMp1Ar5RBbsRCSwerGwHxjzHkA24dagJq2wqEEUAT4zdY+GXiL5AKiC/CyrX0HMFtEFtjWOWqBSZ6vt1tEfK/TbzXwhu2agd1ASRHxA+oDfWx9+ohIB9vrskBlIDrNSBlbYYw5AyAiu4FygDfwlzEmxtY+F6iSiTEBzgAXgSkisghYlMnt8z2dwqSUUirPqlqtBiePH+P0yRMkJCTw14pl1GsU5NC2kRHhXLp0EYD4uDjCdmynzJ0BOZitUtc1HehljLkbeB/bDA1jzFogQESaAi7GmF22/m2BCSR/U79JRBz90viS3WvJqJMx5gTJxUwrYBXJBUVH4KwxJt6Wz0NAfWPMvcBWMj+rxD6XJLLpi29jTCLwAMlnVx4BlmbHuPnJrX4GQimllLppLq6uvNZ3AEP69caalETLR9oRUKEiMycFU7ladeo3bsK+PWGMGDyA+Pg4/l67mlmTQwiZPYdjRw4T8uVYRARjDE88+zzlK1bKck6zPuxBUJ2qeJcowqGloxke/DPTF6zOhr1VecQqYLqIfEjy57RHgYlAUeCU7XqHTsAJu21mAt8CIwAk+Y4AZY0xf9quo3iG5LMW/2VzrhuAN4AHAS+SP5Bfmf5THIg1xpwXkWpAvXS2j7ftV2ZsAsaKSEnb9k8AOzMzgIgUAQobY34VkbXAlVuz3Uw++ZIWEEoppfK0Bxo04oEGjVK1/e/lV1JeV61+F98s+DXNdvc9UI/gmRlee3rTXhg8MdvHVHmHMWaLiPxA8rz+CJI/MEPyvP+/gUjb3/YfdGcDI4HvbMsuwDciUpzkswjjbXc9ym6rgZbGmIMichTwtLVB8rf6r4jIHmAfycVGKsaYaBFZa7twegnJZ0yuyxhzQkRGARuBGGAvyVOSMqMo8LOIeJD8/vSztX8PTBKRPsCTxph/MjluviHGmNzOIUVgYOAw+2serl1W2e7W+eErpVQ2OBwV79R4VVu87tR4V1zeOjVX4qpsk+HUoJsaLPnOQu2NMS9k57i3KhEpYow5a5uWNR+YaoyZn9t55Sd6BkIppZRS6jZluzNRa6BNbufiRMNsD4TzAJaRuYvEVTbQAkIppZRS6jZljOntSD8R6QJce8psrTGm5w22uxuYdU3zJWNMXcezzF7GmDRP5haRIcBT1zTPNcZ84Jys8hctIJRSSiml8jhjzDRg2k1st5Pk5zHc0myFghYLTqK3cVVKKaWUUko5TAsIpZRSSimllMO0gFBKKaWUUko5TK+BUEoplWOsh0KdGq8ccLRYVafGzA3utbs6NZ7eNlYpZU8LCKWUUnlKeW/nPUg2Nz5YO7t4UEqpa+kUJqWUUkoppZTDtIBQSimllFJKOUwLCKWUUkoppZTDtIBQSimllFJKOUwLCKWUUkoppZTDtIBQSimllFJKOUxv46qUUsqpjDGMCp7Jqk3b8Sjozqg3e3BXpfJp+o2dPoefV6wm7uw5Ns+/ervUkxFRDB4TTPzZ8yRZrfTr8gxNHqjlzF24rYQM7UKboHuJjImj9lPv5XY6Sqk8QM9AKKWUcqpVm7Zz9ORplk4Zw/t9ujH8y2np9mtatzY/jBuepj34uwW0alyPnyaMYsygXgyfkP72KtnMhWt5pOdnuZ2GUioP0QJCKaWUU/2xYTPtmzdGRKhVvTJxZ88TERObpl+t6pXx8SyZpl1EOHv+AgDx5y/g45W2j7pqzZb9xJ45l9tpqFuAiASKyPgsjnFERLyzKydnyChnEVmXG/nkBTqFSSmllFOFR8dQytsrZbmUtycRUbHpFgvp6fn847w05CNm//IbFy5dYuqot3MqVaVynYgIIMYYa1bHMsaEAqHpxHA1xiRmdfysyKkcRMQlo3XGmAbZHS+/uNXOQFwMDAwcduUPEJDL+SillLrF/LpyPR0eCmLlN18SPPwtBn76FVZrlj9bKXXLEJEAEdknIjOBXcC7IrJJRHaIyPt2/YaIyH4RWSMi34lIf1v7ShEJtL32FpEjttdNRWSR7fUwEZklImuBWSJiEZEfbXE2iUhDWz8vEVkmImEiMhmQG+T+P1ue20Vklq3tURH5W0S2isjvIuKbXg4ZjNdZRH627dMBERlqt26BiGy25dbdrv2siIwRke1Afbv2QiKyRERevtLP7n1ZKSLzRGSviMy2FW6ISBtb22YRGX/l/csg1yYiss32Z6uIFLWNvUpEFtt+psEiUsDW/2sRCbXlb/9zvV9E1tnew422cVxE5FO746CHra+fbfxtIrJLRBpf7+eTXW6pMxChoaEf2S/bigj75UGAhzNzystCQ9N8CaGUUjli9sJlzFv6JwA1q1TgdFR0yrrTUTH4eDs+DWnebyuZNHIgALWrV+ZSQgKxcfF4lSievUkrlUNsH3a72zWFGGNCrulWGXgRKAY8CTxA8of3X0QkCDgHPAPUIvnz3BZgcyZTqQE0MsZcEJFvgc+NMWtE5E7gN6A6MBRYY4wZLiJtgW7X2a+7gHeABsaYKBHxtK1aA9QzxhgReQl4C3jz2hyuk+cDQE3gPLBJRBbbzqZ0NcbEiEghW/uPxpho4A7gb2PMm7a8AIoA3wMzjTEz04lRG7gLOAmsBRqKSCgwEQgyxhwWke+ukyNAf6CnMWatiBQBLtrlXwM4CiwFHgfmAUNs+bsAK0TkHmAv8APwtDFmk4gUAy6Q/L6fMcbcLyIFgbUissw21m/GmA9s4xS+QY7Z4pYqIBzgERoaOiy3k8hDht64i1JKZV2nR1vS6dGWAKzcuJVvFy6jTZP6bN97kKJ3FHJ4+hKAv48XG7btokOLJvzz7wkuXU7As3ixnEpdqWxnKxauLRiuddQYs0FERgMtga229iIkFxdF+T979x0nZ1X2f/zzpZcQauhKkypSQ+8gKApIUx5BpEmRCCgPKKgPBEQpAoqAdEJHCL0XpQRCTUINRf0BKqA06b18f3+cM2R2spudJPeZmd1c79drXztz78x93bO7s3uf+1znuuBK2+8BSLpmEg7lmroT968Cy+STbYCB+SR4XdJJKravlzT+gqVxNgSG2341P/6/efuCwCWS5gOmA57t4Rh6cmseGCDpCmBtUirWvpK2yo/5Aun78hrwKXB5wz6uBo6xfWEPMR6w/XyO8TApC+Yd4BnbteO9mK4Dv0YjgeMlXQhcYfv5/P18wPYzed8X5+O/DPhOHkxOA8xHGmQY+LftBwFsv5WftwmwnKRtc6xZ8+t9EDhb0rTAVbYfnsDxVabTUphCCCH0c+utsgILzjs3X9t1fw75w5kcMmSXz7+21ZCDP7/927MuYv3v/Yj3P/yI9b/3I066IJ0P/PQHOzD8ptvZcu+DOeDokzhy/z2pO+kJDc4/ck9GnPsLllhoXp656Vh23rIlGQ5h8tVWvgs40vYK+eNLts/q5bmfMO4cb0KZG/Wr66cizRLU4ixg+51JO/TxnAicZPsrwJ4Nx9TMCn833pe0PmnQs4bt5UkDrNp+P7D9acNzRgJfV89/LD6su/0pk3CR3fZRwA+AGUkzBEtN4PgXIc1YbGR7OeB6JvyzErBP3c9nEdu32B5BGuS9AJwj6fsTe9yToq/NQIQQQujjJHUZNNS78uTds/N/AAAgAElEQVQjP7994G7bc+Bu24/3mC8ttCAXHTe01OH1OzsefFq7DyFMnpuBX0m60PY7khYAPgZGkE4YjySdz21OSrcBeA5YGXiAlP7UjFuAfYDfAkhaIV/NHgFsDxwhaVNgQtOFtwFXSjre9muS5sizELOSTnAhpWVNrI1zOtT7wJbArsACwOu238sn6qv3so9D8sfJwN5Nxn0aWFTSwrafA7ab0IMlLWb7MeAxSasASwFvAKvmAcM/8j5OJ6WmvQu8mdeEbArckWPOJ2mVnMI0S37dNwM/lHSb7Y8lLUH6ns4FPG/7jJzatBLQXYpWpWIGIoQQQgihQ9m+BbgIuFfSY6TUl1lsjyHlyj8C3EhKZak5lnSy+RDpBLMZ+wKD8wLdJ4C98vbDgHUljSWlMv1zAsc6Fvg1cGdewFxrQDIUGC5pNPBqk8dT7wFSStKjwOV5/cNNwDSSngSOAu5rYj/7ATNKOqaZoDm1am/gpnzsbwNvTuApP84LmR8lDfJuzNsfBE4CniSlb11p+xHSrMlTpJ/vyBzzI9Ig48T8PbyVNDNxJvAEMEbS46TB4jTA+sAj+We9HXBCM69tcslunFXpHIMHDx5av+ah8X6YbJ37ww8h9AufPdP6Yg1TLTq45TFbaboVd215zI8eOrv3B4WJUXnOnaShwDu2j6163+0kaWdgsO0ftSn+gDzzI9Lsxd9s/24inr8+cIDtzUodYzvEDEQIIYQQQgjd2z0vqh5LSsWKnEBiDUQIIYQQQp9ne2irYkmaE/hLN1/aqFYtaRL2+TXg6IbNz9reCjhnUvZZhTzb0GXGQdIupHSoeiNtD+nm+XeQ1jb0KzGACCGEEEIITcuDhBUq3ufNpIXCHc/2MGBYu4+jnSKFKYQQQgghhNC0GECEEEIIIYQQmhYDiBBCCCGEEELTYg1ECCGEECao1aVjo2xsCJ0tBhAhhBCK6e89Gdqh1SfX7eg7EULobJHCFEIIIYQQQmhaDCBCCCGEEEIITYsBRAghhBBCCKFpMYAIIYQQQgghNC0GECGEEEIIIYSmxQAihBBCCCGE0LQYQIQQQgihMqcfugvP/+X3PDT88HYfSgihkBhAhBBCCKEy5107ks2GHN/uwwghFNTpjeQ+GDx48NC6+wu36ThCCCGE0IS7x/yVheabs92H0adJWhi4zvayk7mf54DBtl9t2H6P7TUnZ99Nxn/H9oDScULrdfQAYtSoUUfV328YTIQQQgghhInUisFD6N8ihSmEEEIIofNMI+lCSU9KukzSTJI2kvSQpMcknS1peoCettdImlHSjZJ2z/ffyZ/Xl3RH3v9TOZ7y176Rt42W9AdJ1/V0oJIGSBqW4z8qaZu6r/1a0iOS7pM0T962uaT78zH/uW770Hz8d0h6RtK+dfv5P0lPS7pb0sWSDsjbF5N0Uz7OuyQtlbd/W9LjOfaIan4koaajZyC6M3jw4IOAGdp9HP3BqFGj2n0IIYQQwhRH0h7AHnWbTrd9esPDlgR2sz1S0tnA/sCewEa2/yrpPOCHkk4FzmncDvw+72cA8CfgPNvndXM4KwJfBl4ERgJrSRoFnAasa/tZSRf38pL+D3jT9lfy65s9b58ZuM/2LyQdA+wOHAHcDaxu25J+APwU+N/8nKWADYBZgKclnQKsAGwDLA9MC4wBRte+d8Betv8maTXgj8CGwCHA12y/IGm2Xo4/TKQ+N4AAZhg1atTQdh9EP3Fouw8ghBBCmNLkwULjgKHRv2yPzLcvIJ2kP2v7r3nbucAQ4PYettcGEFcDx9i+sIc4D9h+HkDSw6T1pu8Az9h+Nj/mYroOeBp9Ffifutf3er75EVCbuRgNbJxvLwhcImk+YDqgFgfgetsfAh9KehmYB1gLuNr2B8AHkq7NxzsAWBMYnidOAGqzLyOBcyRdClwxgWMPkyBSmEIIIYRQmfOP3JMR5/6CJRaal2duOpadt1yn3YfUV7nh/huTuJ+RwNdVd4bd4MO6259S7cXlj23XXkf9vk8ETsozFnvSNbNkYo5nKuAN2yvUfSwNYHsv4JfAF4DRkmJlf4ViABFCCCGEyux48GkstMn+zLzqHiz69QM456q72n1IfdUXJa2Rb28PjAIWlvSlvG1H4E7g6R621xwCvA6cPBGxnwYWzdWgALbr5fG3kmY9gC4pTD2ZFXgh396pieMZCWwuaYY867AZgO23gGclfTvHlaTl8+3FbN9v+xDgFdJAIlQkBhAhhBBCCJ3naWCIpCeB2YHfAbuQ0nUeAz4DTs1pPeNtb9jXfsCMeR1Cr2y/D+wN3CRpNPA28OYEnnIEMHtt0TJpDcOEDM3HOxp4tZfHYvtB4BrgUeBG4LG649kB2C3HHQt8K2//bV7U/ThwD/BIb3FC8zRuZqnz1cq4xhqIyvSdH34IIYS2mG7FXVse86OHzm55zBbrKZ2oY0gaYPudnPp0MvA327/rgOOZCRgB7GF7TLuOZ0oXMxAhhBBCCKHR7nlR9VhSytFpbT6e0/PxjAEuj8FDe/XFKkwhhBBCCKGgPNvQZcZB0i6kdKh6I20PoTDb25eOEZoXA4gQQgghhNAr28OAYe0+jtB+kcIUQgghhBBCaFoMIEIIIYQQQghNiwFECCGEEEIIoWmxBiKEEEIIHaXVpWOngLKxIVQqBhAhhBBC6FGrT67b0XcihDBxIoUphBBCCCGE0LQYQIQQQgghhBCaFgOIEEIIIYQQQtNiABFCCCGEEEJoWgwgQgghhBBCCE3rawOID4CF230QIYQQQgghTKn61ABi1KhRRwHPtfs4QgghhNAZTj90F57/y+95aPjh7T6UEKYYfWoAEUIIIYRQ77xrR7LZkOPbfRiVkrSwpMcn4vF3SBrczfYtJB1U7dGFEI3kQgghhNCH3T3mryw035ztPoyOZPsa4Jp2H0fof2IGIoQQQgih80wj6UJJT0q6TNJMkg6R9KCkxyWdLkl1j99R0sP5a6sCSNpZ0kn59iBJl+fnPyhprbx9vfy8hyU9JGmWNrzW0MfEACKEEEIIofMsCfzR9tLAW8DewEm2V7G9LDAjsFnd42eyvUJ+3Nnd7O8E4He2VwG2Ac7M2w8AhuTnrgO8X+TVhH6lz6cwDR48+CBghnYfR180atSodh9CCCGEMMWRtAewR92m022f3vCwf9kemW9fAOwLPCvpp8BMwBzAWODa/JiLAWyPkDRQ0mwN+/sqsEzdpMVASQOAkcDxki4ErrD9/OS/wtDf9fkBBDDDqFGjhrb7IPqoQ9t9ACGEEMKUJg8WGgcM4z2sm/t/BAbb/pekoXS9gNrd4+tNBaxu+4OG7UdJuh74BjBS0tdsP9XbawhTtkhhCiGEEEKfdf6RezLi3F+wxELz8sxNx7Lzluu0+5Cq8kVJa+Tb2wN359uv5pmDbRsevx2ApLWBN22/2fD1W4B9anckrZA/L2b7MdtHAw8CS1X7MkJ/1B9mIEIIIYQwhdrx4NPafQilPA0MkXQ28ARwCjA78DjwH9LJfr0PJD0ETAvs2s3+9gVOlvQo6fxvBLAX8GNJGwCfkVKibizwWkI/EwOIEEIIIYQOYvs5up8J+GX+aHz8+j3s5xzgnHz7VfIsRcNj9mncFkJvIoUphBBCCCGE0LQYQIQQQgghhBCaFgOIEEIIIYQQQtNiABFCCCGEEEJoWgwgQgghhBBCCE2LAUQIIYQQQgihaTGACCGEEEIIITStL/aB+GDw4MFD6+4v3Kbj6POe/+87LY+54BwDWh4zhGb9/ZW3Wx7zS4NmaWm8Vr/GhT9+saXxAKaZf8mWxwx923Qrdtd3rZyPHjq7pfFCqFqfG0CMGjXqqPr7DYOJEEIIIfRhrT65bvXgIYT+IFKYQgghhBBCCE2LAUQIIYQQQgihaTGACCGEEEIIITQtBhAhhBBCCCGEpsUAIoQQQgghhNC0GECEEEIIIYQQmhYDiNCjB+69h52225odt/0WF583bLyvP/rQGPbcaXs2XntV7rztz204whDKG3XfPezx3a35wXZbcun554z39ccfHsO+u+7A5uutxt23j/8+eO/dd/j+Vt/glOOPbsHRTppWv0bb/OYPp/P1HfZgq9324Ym//r9uH3fCmeez0Xd2ZfCm3+my/ZxLr2LznYew1W77sOv+v+TF/7zcVNwQqnL6obvw/F9+z0PDD2/3oYTQFjGACN369NNP+cNxR3Hk8X/g7Isv47Zbb+a5Z5/p8pi5552Xn/7fYWy08dfbdJQhlPXpp59yyvFHc9ixf+CUC4Yz4s8388+G98GgeeblJz8fyvpf/Vq3+zj/jFNZdvkVW3G4k6Qdr/Gu+0fzjxde5MYLTmPo/w7h8N+d0u3j1l9zFf50yrHjbV968UW59NTjufKsE9lkvTU57rRzmo4dQhXOu3Ykmw05vt2HEULbxAAidOupJ8aywIJfYP4FFmTaaadlg69uwj0j7ujymHnnm5/FvrQ4mkrtOcgQCvvrk2OZf8EvMF9+H6z71U247+47uzxmnvnmZ5EvLY6mGv/P6d+eepI3Xn+NFVddvVWHPNHa8RpvG3k/W2yyAZJYfpmlePvdd3nltf+O97jll1mKQXPOMd721VZcjhlnmD4/Zkn+88qrTccOoQp3j/krr7/5bpF9S5pN0t69PGZhSds3sa+FJT0+icdxsaRHJf1kUp4/OSTtJen7vTxmqKQDWnVMk0rSlpKWafdxVC0GEKFbr77yMoPmnufz+4PmnodXX3mljUcUQuu99srLzFX3Pphr0Ny89kpz6TKfffYZZ530O3Yb8uNSh1eJdrzGl199jXnnHvT5/XnmmpOXXn1tovZRc/kNt7LOaitP0nND6FCzARMcQAALA70OICaVpHmBVWwvZ/t3peL0xPapts+ran+SpqlqX5NgSyAGECGEEHp3/ZXDGbzGWl1Ozvubdr/Ga2+9nbFP/51dt9u6LfFDKOQoYDFJD0v6bf54XNJjkrare8w6+TE/yTMNd0kakz/WbCaQpBkkDcv7fkjSBvlLtwAL5P2v08Nz95X0RJ6l+FPeNlTS+ZLulfQ3SbvXPf5ASQ/mxx9Wt/37edsjks6v288B+fbu+XmPSLpc0kxNvrY7JP1e0ihgP0krS7pT0mhJN0uaLz9u5bzvR2rf67x9Z0kn1e3vOknr59ub5Nc4RtJwSQPy9qPqvifH5p/DFsBv8/dyse6+b31RO0dkVflg8ODBQ9t9EH3RVbfc0ePX5ho0N6+8/NLn9195+SXmGjSox8eH0B/NOWhuXq17H7z6ysvMOWjupp771OOPMfaRh7j+ysv44P33+PjjT5hhxpnY5Yf7lDrcSdKq13jRlddz2fW3ALDsUovzn5fHzWi+9OprzDPXnBN13PeOfpjTLxjOOb//DdNNN+1EPTeEdpO0B7BH3abTbZ+ebx8ELGt7BUnbAHsBywNzAQ9KGpEfc4DtzfL+ZgI2tv2BpMWBi4HBTRzKEMC2vyJpKeAWSUuQTnqvs73CBJ57ELCI7Q8lzVa3fTlgdWBm4CFJ1wPLAosDqwICrpG0LvAa8EtgTduvSho/ZxGusH1Gfp1HALsBJzbx2gCmsz1Y0rTAncC3bL+SB2K/BnYFhgE/sj1C0m9726GkufIxf9X2u5J+Buwv6WRgK2Ap25Y0m+03JF1D+l5elp/f0/etT+nzA4hRo0Yd1e5j6Kue/+87h/b0taWWXoYX/vUv/v3iC8w1aG5u//Mt/OKwX7fy8EJouyWWSu+D/7z4AnMOmpsRf76FAw89oqnn1j/u1huu5e9PPdFxgwdo3Wvcfqtvsv1W3wTgznsf5KKrrucbG67Lo08+zYCZZ+p2rUNPnvzb/+Ow4//IaUcPZc7Z++z/3zAFy4OF03t9IKwNXGz7U+AlSXcCqwBvNTxuWuAkSSsAnwJLNHkoa5NPxm0/Jekf+bmN++/Oo8CFkq4CrqrbfrXt94H3Jd1OGjSsDWwCPJQfM4A0oFgeGG771XwM4y+GgmXzwGG2/Lybm3xtAJfkz0uSBjG3SgKYGvh3PoGfzfaI/LjzgU172efqpJSkkXlf0wH3Am8CHwBnSboOuK6H5/f0fetT+vwAIpQx9TTTsM///pSf/fhHfPbZp2y62bdYeNHFGHb6KSy59DKsuc56PPXEWA496ADeefst7r37Ls498zTOvmh4uw89hMpMPc00/HD/A/m//ffhs88+ZeNvbsFCiy7G+WeeyuJLLc3qa6/HX58cyxE/P5B33n6LB0bexYVnnc4pF1za7kNvWjte47qrD2bE/aPZ9Ht7MsP003PEz/b9/Gtb/2A/rjjzBACOPXUYN/xlBB98+CEbfnsXtvnmxgzZeXuOPfUc3nv/fX4yNJWNnW+eQZz8619O3jcihIlw/pF7su7KSzLXbAN45qZjOfzUqznnqrvaeUg/AV4inZBPRTqRLe2bwLrA5sAvJH0lb3fD40yadTjS9mn1X5DUzFWVc4AtbT8iaWdg/Yk4xtpKdwFjba/REH9CVyA+oWuq/wx1+7rV9ncbnyBpVWAjYFvgR8CG3ex3vO+b7U+aeC0dRXbjzzlMKZ7/7zst/+EvOMeAVocMoWl/f+Xtlsf80qBZWhqv1a9x4Y9fbGk8gGnmX7LlMUPfNd2Ku7Y85kcPnd1j+UJJcwJjbC8kaWtgT+AbwBzAKGA1YAHgeNvr5ef8Dnje9nGSdgHOTlk0WpiUPrNsD7H2B75se7ecunQraQZivl6eNxXwRdvP5fSgf5Cuyv+YtGj48xSmfHtZ4FfARrbfkbQA8DEwCLgSWMP2a5LmsP1fSUOBd2wfK+nVvO/XgRuAF2zvXP+YHo7xDlKa1yhJ0wFPADvavjcf8xK2x0p6FNjb9t2Sjga+aXtZSWsDx5BmTxYAxpJSu8YCo4ENbf9d0sz56y8CM9l+WdKswDO255R0Yv55Duvp+2b7je5eQyeLGYgQQgghhA6RT6RH5sW8N5JSXh4hXcn/qe3/SHoN+FTSI6Qr9H8ELlcqfXoT46689+aPwCmSHiNdcd855+b39rypgQvyibKAP+R8f/Lx3k5as/Er2y8CL0paGrg3P+Yd4Hv5BP7XwJ2SPiUNOHZuiPV/wP3AK/nzRF91sf2RpG2BP+Rjngb4PWkwsAtwtiSTFo/XjASeJQ08ngTG5H29kmdCLpY0fX7sL4G3gaslzZC/J/vnr/0JOEPSvsD/kFKcunzfJvb1dIKYgZiCxQxECF3FDET1YgYidLpOm4Hoy3qbFeh0vc3YhHGijGsIIYQQQgihaZHCFEIIIYTQj0n6GnB0w+ZnbW/VxHNPBtZq2HyC7WGNj7U9dJIPchJNzPH1xvZzpPUaoRcxgAghhBBC6Mds38zElT+tf+6Qig+nUp1+fP1VpDCFEEIIIYQQmhYDiBBCCCGEEELTYgARQgghhBBCaFqUcQ0TTdIetk/vzzEjXt+O146YEa9vx2tHzIjX92O24zWG0AliBiJMij2mgJgRr2/Ha0fMiNe347UjZsTr+zHb8RpDaLsYQIQQQgghhBCaFgOIEEIIIYQQQtNiABEmRTvyPVsdM+L17XjtiBnx+na8dsSMeH0/Zqx/CFOkWEQdQgghhBBCaFrMQIQQQgghhBCaFgOIEEIIIYQQQtNiABFCCCGEEEJoWgwgQtMkLSTpq/n2jJJmafcxVUnSzJKmyreXkLSFpGkLxZpe0vaSfi7pkNpHiVgNcc9vZlvoXSt/X9pB0tSSjm1hvJUm9FE49tqSdsm3B0lapGCs8fZdON5akmbOt78n6XhJC5WK10382SUtV3D/bXsfSppK0sBWxAqh08Qi6tAUSbuTGubMYXsxSYsDp9reqGDM/YBhwNvAmcCKwEG2bykUbzSwDjA7MBJ4EPjI9g4FYt0EvAmMBj6tbbd9XNWxGuKOsb1S3f2pgcdsL1Mo3jHAEcD7wE3AcsBPbF9QcZzHgB7/mNmu/ASmlb8vdTGnB7YBFgamqW23fXihePfZXr3EvruJdXu+OQMwGHgEEOl3ZpTtNQrFPTTHW9L2EpLmB4bbXqtQvC7vwbxttO2VC8V7FFie9H08h/S39Du21ysRL8e8A9iC9Ds6GngZGGl7/wKxWvo+lHQRsBfp7/aDwEDgBNu/LREvhE41Te8PCQGAIcCqwP0Atv8mae7CMXe1fYKkr5H+OewInA8UGUCQBtTvSdoN+KPtYyQ9XCjWgra/Xmjf45F0MPBzYEZJb9U2Ax9RtgzhJrZ/Kmkr4Dlga2AEUOkAAtgsfx6SP9dmVYqdzNPa35eaqxk38PywcCyAhyRdAwwH3q1ttH1F1YFsbwAg6QpgJduP5fvLAkOrjldnK9LFiTH5OF4sMbsqaSngy8Cskrau+9JA0qCplE9sW9K3gJNsn5V/Z0ua1fZbkn4AnGf70DyQKaHV78Nl8mvbAbgROIj0fowBRJiixAAiNOtD2x9JAkDSNEzgim9FlD9/Azjf9ljVDqBQPElrkE46a/9gpy4U6x5JX6mdJJVm+0jgSElH2j64FTGz2t+Yb5Ku6r5Z4kdo+x8Akja2vWLdlw6SNIb0T75qrfx9qWnpwJN0YvsasGHdNgOVDyDqLFn/vrD9uKSlC8b7KJ9gG1JKTKE4S5IGurMBm9dtfxvYvVBMgLfzBYQdgXVyuk/pFJ9pJM0HfAf4ReFYrX4fTptTpLYkDcg+LvtvKYTOFAOI0Kw7JdWuYG8M7A1cWzjmaEm3AIsAB+ergp8VjLcfcDBwZR6sLArc3stzJtXawM6SniVdSRbgEqk2Da6TNLPtdyV9D1iJNP3+j4LxniKlMP1Q0iDgg0KxIJ1MrGV7ZL6zJuXWerXy96Wm1QPPXVoRp8Gjks5k3CzVDkCpq9cAl0o6DZgtp2ruCpxRdRDbVwNXS1rD9r1V738CtgO2J83o/kfSFyl/tfxw4GZS2tKD+b3xt0Kxfkxr34enkWZTHwFG5PUkbxaMF0JHijUQoSn5qtVuwCakk92bgTNd8Bcox1wBeMb2G5LmBBawXeRkQtK3bQ/vbVtFsbpdxFjwRL4Wtx350HMAb9r+VNJMwEDb/ykUa2XgbGDWvOkN0onTmAKxWvb7Urf/J4AvAUUHnpJOZMJrSvatMl5D7BmAHwLr5k0jgFNsFxt45osin/9ts31rwVgtWRfUEHMhYHHbf87vwaltv10qXjtImsn2ey2Is4jtZ+vuC/iS7VIDpBA6UgwgQseS9JfGRdrdbaswXneLG8fbVmG85UmL/wDusv1IiTgNMcfYXkmp4tMLOR+62GvMMddk/EW/55WKl2POmuMUuzLY6t+XvP+WDDwl7ZRvrgUsA1yS738beML2XlXG6yb+jMAXbT9dMk5DzIF0/R39b6E4D9teIa8L2gzYHxhhe/lC8dpRAGMJ4BRgHtvLKlVh2sL2EQVirQGcBQyw/cX8d3VP23tXHSvHa+ki+BA6VaQwhabkVJvxRpu2Fy0QawZgJmAuSbMzbi3EQGCBAvE2Ja2zWEDSH+q+NBD4pOp4OeZ+pLznWi75BZJOt31iiXh1WpoPrVQidjHgYcZVmzJQZAAhaR7gN8D8tjeVtAywhu2zKozR8t+XGtv/kLQ26WrysJwSNqBAnHMBJP0QWNv2J/n+qcBdVcerJ2kLUorNdMAiklYADre9RaF4ewKHkVLrPiPP6gCV/23Lau+3ouuC6rSjAMYZwIGkdB9sP6pUvajyAQTwe+BrwDU51iOS1p3wUyae2rcIPoSOFAOI0KzBdbdnIF2JnKNQrD1Jea3zk6pb1P67vgWcVCDei8AoUtnB0XXb3wZ+UiAepHSw1Wy/CyDpaOBeoPQAotX50INJVUtaNdV5Dqn0b23h5l9JV88rG0CQfl9G09rfF6BryVHS65yWtFagSMlRUvWzgUDtavyAvK2kQ0knvHcA2H5YBfskAAcAy9p+tWCMete2eF1QOwpgzGT7gYaBUbHBte1/NcT6tKfHToZ2LYIPoSPFACI0xfZrDZt+r1R/u/LmZ7ZPAE6QtE8LrsiTU4cekXSR7Y9Lx8tE139ynzJuoFRMHjRcDiyeN70KXFkw5OPAvMC/C8aoN5ftS/MsC7Y/kVTpyUTd78sFtSvzLdSSkqN1jiKVcr2d9Pu5LmVLqgJ83M1V+ZInvP8PKJ47X2P7oLwOorYu6F3gWwVDtqMAxquSFiP/3CRtS7m/Af/KaZJWqo60H/Bk1UHauAg+hI4UA4jQFHXtBDsV6Spo0d8f2ye2OH9+VUlDgYVyvNoC1RKpDMOA+yXVTt63pNqr5N2qz4cmpRYtAJwKlMqHngt4QtID1PUtKJWOArybF9vXTlxWp+IKKaprWtdd6knhSlqtKjkKQE6TupmU8vYkqe79iyVjAmMlbQ9MnfP19wXuKRjvYFJ1q/vp+jta6UJxSRvavq0+/aXh96dUadyDSDOej5Fmd28gFU8oaQipv8xSkl4gLfr/XqFYewEnkP6WvUDqEzRkgs+YPFtJGksLF8GH0IliEXVoisZ1iYU0Ff0ccGzJRY495c+XqgCT0wp+wvjdoRtnX6qKtxKpnCukRdQPlYjTEPNhcj60c78ESY/Z/kqheN1Wd7J9Z6F4K5HSwJYlzX4MAratsnJXTwuZa0pW0pJ0AGn2aGPgSFLJ0YtKzdQpNQLbD1iQ9D5cHbjX9oYTfOLkxZyJlIJWX/HtV6WqMOXB7d2kE+zPy0TX1oFUGOcwp4Zqw7r5sm3vWmW8urgzAx/Y/jTfnxqYvkUVi2YGpupPFZ9avQg+hE4VA4jQsSQ9SQvz5yXdb3u1wjEGOnUx7Xb9SKnKL3Xx77e9mqSHbK+Y86HHlLxqnhc2r5LvPmD75VKxcrxpSPnKAp5uYVpaUUqXqxcElqJ1JUcfI/3s7ssnTUsBv7G9dS9P7TNq74V2H0cpku4Dvmr7nXx/AHCL7TULxty/m81vAqNtV9IlWtJPnbpOd1tyuOCFprG2v6zUq+Qy2zdJeiQGEDAk0PwAACAASURBVGFKEylMoSmSpge2Yfx0osMLhm1J/nxdetbtkn5LSiWoT2WosofARaSrVqPp+k+vdOWXmpbmQ0v6DmmR9h2k13iipANtX1ZxnJ5OaJeQhO3K00Mkvc24n+F0pAXN79oeWHUsSJeoJd2QZ4uKDRoafGD7A0lImt72U5KWLBFI0rVMuPdEqbS3GyXtQXof1L/vKx3M93BS/Tnbx1cZr84MtcFDjvNOnuUpaXD+qP1t2YzUDHAvScNtH1NBjNo6h1EV7GtitHoRfAgdKWYgQlMk3US+gkTX9J7jCsSqnUjMQmokVzR/viE9q5FLpmu0mlrcEFDSI8DGtVmH/M/2z1VfreshLaSmWHpIXXyRFsKubvuggnHOBU6y/WCpGA3xrgR2IVVF2xB4HZjW9jcKxJpgM8OCaW/PdrO58rVPuYJWj2wfVmW8urgjgX1qF0KUmi2eZHuNEvFyjBHANxpmPa4Hvk6ahVimQMyBpJ9b8XQpdW2OOTMwiws1xwyhU8UAIjRF0uO2l21RrLacSLSSWtwkr10a11fkAcwjpdZctFvpdJh85fNLwD+Ad6FMJ+oeYq9H6vB9k+2PCsdqeSO5TiHpYNtHVri/VYA/kRa/izSru53t0RN84uTFfAr4Si19MM9gP2J7qarfI5IGk4pSzEJ6fbXu80VeX5692Z/0+7lHXui/pO3rSsQLoVNFClNo1j2SvmL7sdKB2jVAaFHebkub5HUTv2UNAbObchWfi/P97UhVYIpQCxrJ1cWqT5uqVSYrncrwtcL771Gr3peSNgeOpXAjue6qItUrkfbWpG+TFshXwvaDee1KLfWsFeuCLiRVmbs6398cuChfrX+i4lhnA3vbvgtAqdHiMFJ1pBKGkWbia2tIXgCGAzGACFOUGECEZq0N7JxPQD+kBVc+G3LMa94k5bz+r+1nKg7ZirzdVjfJa9TKhoDYPlDSNoxrdHa67ZJ9J86hfCO5mvpmUrXKZEXq+dctuu831WwmYCitaSS3HnAbXX+ONaZcWdXelOgHsySwDOk9v1JeF1SqHDa2f5XTXmsn2XvZrq1V2KHicJ/WBg859t2SSvZnWcz2dpK+m+O9l1MYQ5iiRApTaEpPpSsLl6z8FfA8aeGxgP8hlXUdA/zQ9voVx2tZ3q5a1CSvGZJG21653cdRBUkP2l6lPk2iVnax3cc2OepmjgR8kbQWQaSuuP+0XbJTc0tJus/26g0/w0dLXayQtIjtZ3vb1iqSxtheqfdHNr2/Q4H1SQOIG4BNgbttb1tVjB7iTg3MQ9eiG/+scP+179H3gRlJs5wmzXJ+YHuCi9YnI+49pL45I22vpNQw72Lbq5aIF0KnihmI0BTb/8hTw4s7NZcaBAwoHHaLhsW2p+eTwZ/lSkJVm5u6xdrAx8A8tt+X9GEPz5kkTk3ylmXcVcHa9mJXBaHLP10o2BBQ0t221+5mFqk2c1WkUhGtaSTXbdnImhLlI2sDBElnAFfaviHf35TUhLA/aXUjucuBxhP2y4B2Daqrvpq9LbA88JDtXXKaX9GmZ5L2AQ4FXiIV3ahVmatyENhYwKN+kXrJK6OHkhrIfUHShaTZ1Z0LxguhI8UAIjQlX8UaTJoKH0YqWXkB41JTSnhPqQxoreTntozLMS/xD6Jlebs9XRUEig4g6PpPt5Z2852qg9heO3+epep992J/4BpgsVx9ZhDp96ZKtVSMtUg/v0vy/W9TfX53o9Vt7167Y/tGSVWk1nWSfUgpaB+SZh9vBo6oOkheF/BlYNaGdRADqRvUt8Hwivf3vu3PJH2SKxW9DHyh4hiN9iMtLC7ShBPA9gal9t1L3FsljSE1VRSwn+1X23EsIbRTpDCFpih1MF6R1HSseFpB3v+iwAnAGqQBw32kTtEvACvbvrtAzMGMGxSNrMvbrTrOY4y7Krh87aqg7Y1LxGsXSefb3rG3bRXHbEkjOaUGXWvb/iTfn5bUUXz1EvFyjJuBuxh3BXkHYF3bbVtc3VdJ+hZp9mYL0qCz5m3gT7aLzHpI+kM3m98ERtm+upuvTW68PwI/J6WA/i/wDvCw7V2qjlUX83ZS+eaSaxHq432TNBisn80t0qNI0rrdbbc9okS8EDpVzECEZn1k25JqqSEzlw6YF0l3t8AR0tX6Sqhrd+hn8kfta3O4THfodlwVbEmlqQZfbog/DQVTQyQNAS60PTbfn13Sd23/sUC42UlXq2u/HwPytpK+S0qhqC1EH5G39RuSbgW+bfuNfH920gl9pYOkfLJ+taQ1bN87geOptKwq6SR3KcbNNGwDPAssL2kD2z+uMBa29843T80LmwfafrTKGN14BrhD0vV07eFTebM8SaeSKtttAJxJmnF8oOo4dQ6suz0DacH/aFKflBCmGDGACM26VNJpwGySdgd2Bc4oEUjST20f01OueYEc83Z0hx4laTbS93A06apgjycxFWpFpSkkHUy66jmjpLdqm4GPgNOriNGD3W2fXLtj+/X8+1piAHEU8FC+2ipgXVIFoWLyYHa/kjE6wFy1wQN8/jOcu1SwCQ0eskrLqpLWAaxl+1MASaeQZpXWBoqUyc4pWmuT/p7dTXrPl/TP/DFd/ihpTdvL5RnxwyQdB9xYKpjtLhe1JH0B+H2peCF0qhhAhKbYPlbSxqRyo0sCh9i+tVC4J/PnIulDjWxvlj+3pJJNLvl3ZD5JauVVQYAFgZXqKk0dSqo0tS5pIFPJACJfsT1S0pG2D65in02aWpKcczNzJZgiJzC5mMDNwI6k39kbSc26Kqdx3dl7OpZKeyS02WeSvlir2JMrwLUz17bqRc2zk2araov7ZwbmcOpqXGmxBvg8helLjOvFsqekr9oeUnWsGhfqqt2D9/Pn9yTND7wGzNfC+M8DS7cwXggdIQYQoSk59eWSgoOGz9m+Nn8+t3SsRpIWABaia+nBSnNbcyrYDcBX8v3nqtx/L1pWaQrA9sE5BWVxuuYnl8oXvgm4JM+WQeq7cVOJQJJ+QJoNWBB4mLSo8l7KpDIcW2CfneoXwN2S7iSdvK8D7NHG46l68HIM8LCkOxg3c/WbnBb654pjQfp9XLpuUH0uMLZAnM/lKn0/Zfx1CSXeG9fl2dxjSBdBIKUyFdEwMz4VsAKptHgIU5QYQIRmzQLcIum/pKozw22/VDKgpCWAA4CF6XpCXyTXVNLRpBriT5BKD0L6R1HiZHeMpFVsP1hg3xPSyg6xrT7JBvgZadDww3z/VsqdTOwHrALcZ3uDXNXnNyUCuU3d2dvB9k253HBtMfqP21zlptIZCNtn5QsItb4BP7ddm7k6sIenTY6/k3qH1Hr2fCFvK+lC0v+JzYC9gJ2AVwrFOpb0fl+H9LflLuCUQrGg68z4J6QeECMLxguhI0UVpjBRJC1HOsneBnje9lcLxnoEOJV0Val2Qo/t0T0+afLiPQ0sZ7vyK/HdxHqKlFbwD+BdWtDZuy52j5WmJM1u+/UKYz3GuJPsFWon2ba37uWpHU/jmtY9DKxm+0NJY21/udcnT3ysS21/J38/u1sXVPz3ppVaMRM4Ecfyc9uVDgxb+fryTM4qjFtYvArpJPjNHLfy9Dfl5pT1lfpq75cCsS4lVc6qVSbbHpjVduXlqUMI48QMRJhYLwP/IeWZFlvYmH1iu+SVpEbPkPpbFB9AAG0ru5kHDD2tL/kL4zfVmhwf2P5AEpKmt/2UpCUr3H8XktYiLWSunZzVBmYlFsI/n1MnrgJulfQ6467yVq22cHqzQvvvGHUzgWOBz/LmymcCeyrSUFMr1lBg8NCS11fnkEL7nZBa6eR/5xKrLwJzFIq1rO1l6u7fLqlYP5aeBvG08CJQCJ0gBhChKZL2JjUcG0QqP7i77dJNs67Nca+kaynAEmVVAd4j5Sb/pSFe5Z2Fae+i0AmpesFoK0+yAc4i9QrpMmtVgu2t8s2huRLTrBRab2H73/lzye9dp9iS1ISs9EC+XQ0BW/X6akYxrmz0EqQSsjeW6o+SHSFpVlLfiRNJ5Y5/UijWGEmr274PQNJqlC3AUavwdH7+vEP+3MqLXSG0XaQwhaZIOpK0iLpEr4CeYj7bzeZSV5ORtFN320ss5q67iiXSIsNFSE3PKk9/mcjjGmO7yhmI+n2vRz7Jtv1RoRj3216txL47QS7HeTRp9k+Mu+o5sK0HViFJN5L6QLzTongtbQjYhtc3mrQ+YHZgJPAgqa/PDhN8Yoer+xs6Laky4D/z/YWApxpmJaqM+5BzM9W6bcX+bobQqWIGIjQlV9NZW9IuuXzlIGCA7e5O8quKOcGyqpI2rrIqVG8DBUmX296molhfadj3SsDePTy8z1FqyteoVuN+AOOar1Xtdkm/Ba6g6yxSf6mScgywue0ne31k39XKmUBofUPAVr8+2X5P0m7AH5167DxSKFYKmP4/7M74BTB2rTBMu9L5JGmt2sJpSWuSqjGFMEWJAURoSu4XMJh0pWcY6arPBYxbjNsOR5Oq7LRKkZkPSCe4eeq93apKYao15etuf6Wa8wHUvoeDG+L1ly6xL/XzwQPANfmjVVrdELDVr0+S1iCl2uyWt5U+4b2aVA3pzxRKJWxjOt9uwNk5RQvgDVJj1RCmKJHCFJqSK82sCIypTd/WV9ho0zGNN5VcOF5l09S5r0bNVKSFy3PaLrK4uocZgc/V1pVImqPgGpMwiXLqEsB6wLykNSX1V6+vaMdx9ReS5mXc4PN+2/9p5/FUSdK6pHLYI20fLWlRUmncUjMeSHrY9gql9t8JagMI22/29tgQ+qOYgQjN+ig3QKs1I5q53QdE5y5EbsYsdbc/IXWDvrxgvPoZgS8Cr+fbs5FyhxeB6hao55SsHpVMKcpVXxobWB1eKl6LbJ4/m5QCs0nd10xK2eoXJC0OHEla2Fz/Myw2A0gajP07x1tC0hJVl1VtVyne/DpG1N1/Bvh88CDpRNv7VBz2OknfsH1DxfttO0nzkPq9zG97U0nLAGvYPqvNhxZCS8UAIvRKkkj/EE4DZpO0O2nK9oz2HlnLVVahyPZhVe2ryXiLAEg6A7iy9o9d0qakqjBVO25Ch0OhlCJJpwIzARuQGshty7j6932W7V3g8y7C+9l+I9+fnQl/r/uiYcChwO9IP8ddKJhy08Jmh51aireyNFRJbzPuQsXPlbrbf0z/Wux/Dul39Bf5/l9JFbxiABGmKJHCFJqSr5rtT7ryKeDmKhcwT+IxXVFlQzJJKzc2qZO0me3r8u1NbN8ymTGuZcK15ytv6tQQ/7FuFnCPt62vqqXV1X0eQCpZuU67j60KPVSAaWkqX2l1Tcg+/72sbSsUr6XNDiUdbftnvW1rlaggNHE0roHk5++7KSFlK4RGMQMRmjUGeMP2gaUD1eV7d6uW713gH/wZkr5v+/F8HN8Ffgxcl+NN1uAhOzZ/3pqUy17rnvpd4KUK9t+bFyX9si7uDqQmT0VImok08Pyi7T1yesqStUFZAe/nz+9Jmp/U8HC+QrHaYSrVdQvPa1v629/xDyVNBfxN0o+AF0iVkUppabNDYGOgcbCwaTfb+ixJWwG31dYH5F4w69u+qr1HVol3Jc1JvhAkaXVyV+8QpiT97R9PKGc1YAdJ/wDerW0slLdby/eeG1gTuC3f3wC4h3L53tsCl0nanlQ3/ft0zTWfbLbvBJB0nO36SkHXSirZ/Kjmu6T0kCsZ1/32uwXjDSOtv1gz33+B1Iiw1ADiunyy8lvSoNekVKb+4jjgXknD8/1vA79u4/GUsB8pDW1f4Fek9323PVoq0pJmh5J+SCrVvKikR+u+NAupP0O7VN08EuBQ21fW7th+I1fy6w8DiP1JVbQWkzSS1Fx12/YeUgitFylMoSmSFupue8lSepJuAXaqdeGVNB9wTqlKRTnGEqR/cv8EtrL9fi9PmdQ4TwLfzAsakbQIcIPtpUvE6yb+zLbf7f2Rkx1nlO3BDdP9j9hevgWxpwdm6G9VUvKizVp+/m0u3xF+iqGCzQ5z1Z7ZSQvED6r70tvtrHwmaWfb51S8z/Eq9PWHVElJU5MGtieSSpqL1AC0ZFfvEDpSDCBCx5L0ZP0JdU5rGFv1SXY3VVHmJk1JfwhlZlkkfR04HXiG9E9oIWCPitKkJhR3TdIV+QG2vyhpeWBP20Wa2Em6B9iIVEJyJUmLARfbXrVQvCHAhQ2LjL9r+48l4oXqSbqV1Km5/mf4p8IXDtYGFnfBJpmSBtp+q6eSyqUGET2su3oTGAWcZvuDAjHPJvVHODlvGgLMYXvnqmO1mqQHSv39CqEviQFE6FiSTgIWBy7Om7YD/l51ycGeZldqSs2y5CvkS+W7T9n+sO5rlXbZrtvv/aTp9mvqZgQet71s1bHyvjcGfkkqyXkLqeLLzrbvKBRvvMWM/W2RcX/X6oXiqmuSaXuJvHZmuO1Km2RKus72ZpKeZfwmiy5VplbSCaQ0m/q/o2/lYxhoe8cCMWcG/g/4ao5zK/DrVsx6libpd6RGqpfQNZ23v3S7D6EpMYAIHS0vqK5V0BlRn1dbKF7xK5FNHkeRyiiS7re9WitTivKCw9VJJ0z32X61YKzHgOWc/7DllINHbX+5VMxQLUmjSemD/8z3FyKVHi5SKUgd2CSzSrWqQd1tkzS2He8Nlek90RJKHcsb2XZ/6XYfQlNiEXXoaLniUkuaZNVfiSQt/p2WVK2o0iuRzR5Oof3+K6cxWdK0pAWrTxaKVV+N5fp8fzZJWxasxnITcIlSzxKAPfO20Hf8Arhb0p2k98E6wB4F47W8SWZOy1qcro3yKm1cV2eApC/WDci+yLiqVpWu85gI7fibWgnbG0zo65J2sn1uq44nhHaJGYjQsfLsw9GkNQmicDOiTroSWXAGYi7gBFJqgUhpRfsWzL9uaUpRXiezB+n1QUqdONP2pyXihTLy7+nq+W6XWStJX7Y9tsJYB5BO5jcmLXDeFbjI9olVxWiI123julJXsCV9AzgV+H+k9/wipGpQdwC72/59ibi9HFO/7T3Rn19bCPViBiJ0smOAzW0Xu0LeoOVXIttgSds71G+QtBblykh210G42N8d25+RTpZO7e7rki63vU2p+KEaecDQU6nf84HKTtBsH5vX6rxFmn08pMT6ozr7Ma5x3QbKjetKBbN9g1L/ldp6q6frFk63fPAwBSg1exxCR4kBROhkL7Vw8ABwaU59mU3S7qQrkWe0MH695wrt90TGP/nqbltVRkk6nq7VWEZP4PGlFVmoGlqq8hO0PGAoOWio1+rGdQArAwuT/ucvLwnb5xWOOSH9+SQ70jrCFCEGEKGTjZJ0Cakvw+cVivK6iMq18kpkXih6NilV4vVujqXSLtuS1iA1cxskaf+6Lw0Epq4yVoN9SNVYLsn3byUNItol/rn3fZX8DCW93cO+iqZK0qLGdTWSzgcWI6VL1VL5DLRzAHFCG2OX1p8HRyF8LgYQoZMNBN6jazdoU3ZR9V9JJw9/ljSTpFlsv10gznbALsCDSh2ohwG31KoHFTAdaeHkNKTOtzVvUbCLai7beFCvDwyhxWzP0vujisTdKt8cmiv6zErZhf6DgWUK/m0ZTxO9J85p1bG0QTu7iofQMrGIOoQspy3tQWp4tFjOGz7V9kYFY04FbAacQro6OAw4oeCi5oVK9bXoId4SwAGMS58AoF0lD6MnRN8n6T7bq/f+yM4k6VfACOCeVvRFkDScVCjh36Vj1cVsee+J0hpmbsdj+/hWHUsInSBmIELHyiefpwDz2F5W0nLAFraPKBRyCLAqcD+A7b9JmrtQLPLr2QX4BnA5cCGwNnAbsMIEnjo53pP0W+DLdC0hWeqEfjhpQfOZjEufKEbSfrZPmMC2n5U+hjB58qL+h22/K+l7pPU5J9QGvn158JA9A3wX+ENOo7qL1OPm6kLx5gKekPQAXVNBtygUD2DNht4T19b3nigYt6S2zFiF0KliBiJ0rFwH/kDSlHcruiZ3abImaRpSSdfKy7jmNRBvAGcBl7trF+orql4DUbfvW0jrEQ4A9gJ2Al6xXeTEWtJo2yuX2HcP8cYroRizDn2LpEeB5YHlgHNIg8/v2F6vncdVNUnzAt8hvRdnL5VSJanb75vtO0vEyzGfBL7W0HviZttLx/sxhP4hZiBCJ5vJ9gNSlzVpnxSMd6eknwMz5sXUewPXFor1bdvPdPeFUoOHbE7bZ+Wr8neSXvODBeNdK2lv4Eq6Xv2sNEVL0neB7YFFJF1T96VZgCLpYKGYT3I55W8BJ+Xf193afVBVkXQmsAzwEmn2YVtgTKl4JQcKE/C/pGaAXXpP5NLYfbrJmqQZgN0YfxZ317YdVAhtEAOI0MlelbQYeTGepG2Bknm8B5H+MTxG6mB8A+nqZ+VsPyPpm4z/T+jwEvHqfJw//zvHfxGYo2C8nfLnA+u2merLqd5D+t2YCziubvvbwKMVxwplvS3pYOB7wLp5ndC0bT6mKs1Jqnz2Bmlw+6rtyi+MSLrb9trdVJsqXWWqv/eeOB94CvgacDiwA9DKcuMhdIRIYQodS9KiwOmk8qOvA88CO5RaBCxpI9LCxvdL7L8h1qnATMAGpEHKtsADtoteaZW0Gemq5xdI/R8GAofZvmaCT+xDJM1DatQF6Xv6cjuPJ0ycnNqzPfCg7bty+sv6be5bUDlJS5NOQn8CTG17wTYfUqUkrcn4xRP6/M+wLsX1UdvLSZoWuKsfrM0JYaLEACJ0LEkr2x6dp72nsv22pM1s99ShdnLjnQusQboqeBepUsrd3fVpqCBW7Z9P7fMA4Ebb61Qdq90kLUtK2aifaSlyIiHp28CxwB2kK63rAAfavqxEvBAmVh7ErwOsC8wG3Ec6AT27ULzdbJ/VsO0o28XKK/fUe8L2vqVitoqkB2yvKmkEKc31P6QLFdGkMkxRIoUpdLIzJH3f9uMAkv6HdLWuyADC9k45zvykGYGTgfkp8z6pzXK8l+O9BsxXIE4Xra5sJelQYH3SAOIGYFPgbso1sfolsEpt1kHSIODPQAwgOlw7U25a7OukCxQn2H6xBfG2kfSB7QsBJJ0MzFg4Zst7T7TQ6ZJmJzXIvIbUX+eQ9h5SCK0XA4jQybYFLpO0PemK3ffp2lSuUrlk5DrAV4BXgZNI/+hLuC53o/0taQGlKbTeosEZ5MpWALYflXQRUKo07rakijoP2d4lpxddUCgWpJmq+pSl14CpCsYLFbG9dv7cr8tl2v7RhL4u6V7ba1QYchvgGkmfkQYvb7Rgwe/jwLyUXbPWFrZrf6fvpPq1XCH0GTGACB0rLzT+H+Aq4J/AJoXXJ/we+H+kvgW3236uYKxjcunWyyVdR0rv+aCX51Sh1ZWt3rf9maRPJA0EXiatvyjlJkk307WB1Q0F44VQtRl6f0jvJNUXR/gB6e/oSOAwSXOUalaZtaP3RFGSvmf7gp4aykUjuTCliQFE6DiSHqNrCsMcpKol90uiRF8GANtzSfoyKTf517mKyNOFuqbeS2qQRR5IfChpTG1bQa2ubDUqz7ScAYwG3iG99iJsHyhpG2CtvOl021eWihdCAVWl/Yxm/FSwb+aPEpXQ6g0tuO92mTl/7tczZCE0KxZRh44jaaEJfb1gFaaBpBPP9UipTHMB99XWRlQUY15gAVIaz/akf+qQqiGdanupnp5bUfyWVrZqiL0wMNB2lFUNoQfdNUOcjH1NBaxhe2QV+wshhJoYQISOJmltYHHbw/KC2AG2ny0U61HSAt+7gRG2ny8QYydgZ9IiwwcZN4B4GzjH9hVVx+zhOD6vbFU4zlbAbbbfzPdnI5XkvKpQvK2Bo4G5Sd/b/rYAN/RzVXdqbmXn5ylhIXyu1ref7Tfy/dmB46KRXJjSxAAidKxcwWcwsKTtJXK1ouG21+rlqaWO50Tb+1S0r21sX17FviYy7pzAocDapH/wdwOH236tULyHba/QsK3YCY2kvwOb247GTqFPkrRsrfJcRfs7lpQ2eEU/rYrUUt39/WrlIC2EThHVSUIn2wrYAngXIJc8bGf+aZUDlwUlDVRypqQxkopVmKrzJ+AVUmWWbfPtSwrG6+5vTMm1Vy/F4CF0MklbS/qbpDclvSXpbUlv1b5e5eAh2xMYTlpnNV68EiSN1xBT0lElY7bQVHnWAfh8sXqsJw1TnPilD53sI9uWVFvwO3NvT+hDdrV9gqSvAXMCOwLnA7cUjjuf7V/V3T9C0nYF442SdDyppwbAENLizpLxLiFVnKmv/tKS1LAQmnAMLZwla1NZ3Hb0nmiV44B7JQ3P978N/LqNxxNCW8QAInSySyWdBswmaXdgV1I1n/6gtvbhG8B5tseqobZqIbfk0riX5vvbAjcXjLcPqeHSJaSUqVtJg4hSBgLv0bVfiIEYQIRO0fJZsnzFfHG6doMfUTBkO3pPtITt8ySNAjbMm7a2/UQ7jymEdog1EKGjSdqYdDIo4Gbbt7bxWCrLc5U0jFSNaRFSo7WpgTtsr1zF/icQ921SOcLPSCfWU5NTxGjDIscq15U0Ge9g20e2Kl4IjSSdQGqy1pJZMkk/APYDFgQeBlYH7rW94QSfOGmx6ntPzMK43hOHABTuPdEyrSzuEUKnigFE6Gi5pOvitv8saSZg6tKVgyZwLDvbPqeifU0FrAA8Y/uNvLh5gVqJU0lftj22ilidrMqSlZ0YL4RG+eJBI5e6Qp/76qxCKkm9gqSlgN/Y3rpArGdJFyZU97nGtvt85+ZOK+4RQrtEClPoWDltaQ9SI7nFSFfsTwU2KhRvCeBAYCHq3hu1K3VVDR7yvj4DxtTdfw2or4R0PgWayuU0qR2ARWz/StIXSOsiHqg6VodqRZpYCD2yvUuLQ35g+wNJSJre9lOSliwRyPYiJfbbYbYCViT//bb9oqRoLhemODGACJ1sCLAqcD+A7b9JmrtgvOGkAcoZwKcF4zSj1InuH0npSxsCvyJ1hj6ZdIVyShBThSRl7AAAGY5JREFUrqEtJP3U9jGSTqSb30Pb+xYK/Xzuv3IVcKuk14GijSMlTQv8EFg3b7oDOM32xyXjtkh/Lu4RQtNiABE62Ye2P6qtLZY0DWVPAD+xfUrB/U+MUq9zNdsrSXoIwPbrkqYrFKsZrZ4RiBmI0C61hdOjWhnU9lb55lBJtwOzAjcVDnsKMC3pggWkKnOnAD8oHLcV+nNxjxCaFgOI0MnulPRzYMa8mHpv4NqC8a6VtDdwJV0XN/aLhX/Zx5KmJg9Q8gLAz1oROK/7GGC7vgb9Ca2IXWd47w8JoXq2r82fz53Q40oUFpC0EuOaR460/VGV++/GKraXr7t/m6RHCsdsCdvH5v9HbwFLAIe0s7hHCO0SjeRCJzuI1OjsMVIzpBuAXxaMtxNpDcQ9pF4Fo2nx1cI6pf7B/4E0QJpb0q9Jnah/UygWki7KDfNmBh4HnpB0YO3rVa4ryfGOyfGmlfQXSa9I+l5dvGKvNYSKVLoYV9IhwLmkfjNzAcMklfw7CvCp9P/bu/dou8ry3uPfXxCKXCIgHFqRu5Y2AhGEcjEUhANHBRQVZCBQxGrxUk2lxcuwyim1laPAEMGjiJgSLhbLpSCKUG4CghASIJyKjJSbHUChgJLIxZjwO3/Md8FK3Ek2yZx7rjXX7zPGGnvNuXbm8+S29nrn+77Po637ctiK9peF1ulu4EbghvI8YuSkClMMLEn7ADfbfq7tXOpS7gQuk+05y3t9FWNPoirh+BTVRnQB1zRZk17SnaXyy+FUm8I/A8y2vX3D8d4FHAAcC9yw1N3QiIFVd6UwSfcCU20/X45fCdxpu5GN1CXGPsAM4H6q95nNgaNtX9dUzIlSyuJ+AbiW6ve2J3CC7e+0mljEBMsSphhkfwZ8Q9JTvHS35ybbv6wziKS9bV8racyyhjXXZz95Oa+Zl5oT1c72C5K+XnpZ/LypOEtZvWyoPAg43fZve5sPG9J7T9ufqrTi0xPTny9iYD1C1UDu+XL8e8DDTQa0fY2k1wO9Qcq9tn+zvF8zRI4DdiiV8ygluG8GMoCIkZIBRAws20cBlDrbB1NVC3oN9f+73ZPqbtKBY6VBjV2Mbb+lrmutpGskvQe42BMz/XgG8CBwF3BD6esxf7m/YtVcLunnwHPAR8oej+dX8GsiBkndI96ngX+X9G9U72f7ArdJ+ho0U/2p3DQ4hr4qTJK6UoXpSaC/F9EClizBHTESsoQpBlZZu74HsB3wBNV6/Rtt39JSPketaAPky7zetsAUqruDANieWdf1lxGz14l6EdUHazHBHaglvcL2ogavvwHwtO3FZe/Furb/q6l4EStrrMICdTasLNc7anmv1/me1hfz21RVmHrXPhJYbHvoqzBJmkn1M+lSqgHZO4G55YHtU9rLLmLiZAARA0vSE8B9VL0ZrrP9YMv51LY2uXQz3YtqAPFD4G1Uy7MOruP6q5BXrR2wywbO32H7hLpiLBVvrGVoTwN32368iZgRL4ek84EPU20qngVMBk61/ZWW8rnI9ntqvuZdS+87GuvcMCrv3ctk++8mKpeINmUJUwws2xtKegPVNPg/lDW199o+sqWU6lxacDAwFbjD9tGSNgbOrfH6K6vuDtjP9D1fk2pjc2ObtoE/B3YDeps196KqprWlpBNsn9Ng7IjxmGJ7fikscAWlsADQygAC2KqBay6WtLXt+6BbVZj6BwjLKE0dMRIygIiBJWkysBlVBY8tqBogTUjPgmWoc7ruubKpeVH5fT4ObFrj9VdWreuvbS+xaVzSScCVdcZYyiuAP7b9WIm3MTAT2IVqE34GENG2iS4ssCJNxD4OuE7S/eV4C+DoBuJMuLFmkCS1NoMU0Zb0gYhBdhPVxua5wKG2t+ltrG5JnR+ub5e0HlUH09nAHKCVvR1LafqDzFrAaxu8/qa9wUPxeDn3FNCFDZwx/HqFBdZmYgoLtOEnVL/PF6jKRp/BYLy/1WFKmXE4iGoGaUuqPR4RIyUzEDGwVtQroImOrSvwk7ouZPuj5ek3Jf0ImGx7bl3XHxSS7ualQclqwEZAI/sfiuslXc5LHaffU86tDfyqwbgR4/V121/rHUj6BdBmdbYm6hzPpBoU/X05fh/V7N8hDcSaaIM2gxTRigwgYpjV3bF1OlXzowXAt4EdgM/YvgrA9l/WGOsa2/uU6z649LkW1d0B+4C+54uAx5qswAR8jGrQ0Pu3MRO4qJSsbbuEbgTAPEkXAjNs31P+bTbyf0LSasBM24cv59s+3UDobW1P6Tu+TtLPGojThokuTR0xkLKEKeIlHyhT0/sB61NNS59YZwBJa5YyoxtKWl/SBuWxBbBJnbGWEf/N5W48ko6QdEr5AQiA7V3rjGf7IWA9qqVo76KqOtUYVy60/cnyuHCC+l1EjNdUYB5wlqSfSvqLsg+qdrYXA5tLWmM533NVA6HnSHrxvUTSLsDtDcSZcLa/ZnsT228v7y1LzCCtqGxuRFdkABHxkt5U/tuBc0o507qn94+h2vPwR1T7HmaXx6XA6TXHGss3gGclTQX+mqpMbmO9J8qsznnA/yiP8yQ1tuxM0rslzZP0tKT5khZIyt3BGBi2F9g+0/buVHf/jwcelXS2pNc1EPJ+4CeSPi/p2N6jgThIulvSXOBNwM2SHpT0ANX+h52aiNm2ctOifwZpemvJREyg9IGIoSXpDts71Hi9GVSzAFtS3SVcDbje9pvqitEX6+O2T6v7uuOIO8f2jqU/w8O2z6qzv8UY8eYCu9l+phyvDdyyov0tqxDvP4ADbTdZKjZipZVlRftTVSXagmpvwHlUTTP/0fYf1hxvzL4FTfQr6J/NXEbMh+qOOWjq/rkUMaiyByKG2ak1X+/PgTcC99t+VtKraa704BmSPkHV4wLgeuAM201XClog6bPAEcCfljrmqzcYTyxZ/30xzWza7Hksg4cYcPOo+pR8xfbNfecvlPSny/g1K20iG5uNwgBhHHJXNkZCZiBiYEn6Q6p64pvTN9i1vXdD8S4CvgNcYbvRfhOSvk31wf3scupIYLHtDzYc9/epKqLMsn2jpM2AvWw3soypLJU4CriknDoI+CfbX20o3qnA7wP/Cvymd972xU3Ei3i5JK1j+9cTGG8j4FPAG6iaOQLNvY+OusxAxKjIACIGlqS7gG9S7RF48S627dkNxfufVDMOu1KVAZ1h+96aY7zC9iJJd9meutRrv3OuCyTtCEwrhzfavqPBWDPGOG3bH2gqZsTLIWlNqtnOpT/QN/JvVNJVwAXA31A1QDsK+G/bTVRf6ixJ022fKunNtpdZ0lvS6XVW7IsYVBlAxMCSNLuJ/QfjiPsq4DDgc8B/UjV7O7eO5UV9exDmAIfYvq+c3wq4sMG9CDfZniZpAUtOsYvqA3atVWAkTbY9v1Sc+h2lsVvEyJH0L8DPqWYCTwAOB+6x3cjm2977qKS5vb1HkmbZ3rmJeF0l6U7bb2xyz1jEMMkeiBhk35f0UarlL/3LURr78Fn2PRxBtaToDqrNjdOo7trtVUeI8vVvqGqj31+Ot6C5/RbYnla+rttUjKWcT9UDYjZjDFiAreoMJulTtr8s6TTGWINs+xN1xotYBa+zfYikd9o+W9L5wI0Nxuvd+HhU0v7AI8CYA/tYrnskzQNeU4pD9PRuwjRSGCJiUGUAEYOsV0/7uL5ztX/47JF0CbANVVWUA20/Wl66QFJdNcw36iuheAZVpSeolmjtQLW5cujZPqB83XKCQvY2Tnei1nx0Wu8D/a8kbQv8F1WJ46Z8scyq/jVwGjAZ+GSD8TrJ9mFlD9mVwDvazieibVnCFFFIeovtRj/AS3qUqhfDmJWIJrJiykSQdBnwXeBS289OQLwtbT+w1Lmdbc9qOnbEeEj6IHARsB3wT8A6wOdtn9FmXjF+pTFfr9zuvRNQPS9i4GQAEQNH0t62r5X07rFeb7KiTrkjOIUlNzfWVqFo1NbPStoTOJSq7v0s4J+By20/31C82cA7bD/cF/9029s1ES9ivJbRvK13I8G2T2ko7tnAdNu/KsfrAyensMDKKe8pM4EHqf7+NgWOsn1Dm3lFTLQsYYpBtCdwLXDgGK8ZaGQAURou7UU1gPgh8DbgJurt1NxkD4SBY/vHwI9L86y9gQ9RlcqtddN2nw8D/yrpQGBH4EtUncUj2tbbf7QNsDNwWTk+ELitwbjb9wYPALZ/KSllRlfeKcB+vQp9pdz4d6m6b0eMjMxAxNCSdJTts1f8neO+3t1UHajvsD1V0sZU1Zf2rTHGBqNWgUjSK6k+JB1K9aH+ctsfbzDeblT7S54H9rf9303Fini5JN1A9e9yQTleF/iB7dqbyJXr30XV6+WX5XgD4MeZlVs5/dWslncuousyAxHDbDovNWKrw3O2X5C0SNJk4HGq6enajODg4XvAnwA/Ak6n+uBSe5M+Sd9nyepLawFPA2dJwnY2Pcag2BhY2He8sJxrysnALaV8LMAhwD80GK/rbi+NQM8tx4eT4g0xgjKAiGFW93Kg2yWtR9X3YTbwa+CWmmOMmrOAw2wvXuF3rpqTGr5+RF1mAreVqm9QurM3Fcz2zFJFrtd5+t22f9Z7XdL6vdmJGJePAB8DeqWhbwT+b3vpRLQjS5hiaDW5IVnSFsBk23NX8K2xHJLWAo4FNrP9F5JeD2xj+/IGY25MtcYc4DbbjzcVK2JllO7se5TDG5rszj6OXEaqsEPTJF1k+z1t5xHRtEltJxCxCmqdgZB0Te+57Qdtz+0/FytlBtUSjd3L8cPAF5sKJum9VBtSDwHeC9wq6eCm4kWsDNtzbJ9aHq0NHoqRKuwwARrpUxQxaLKEKYbZT+q4iKQ1qdbMb1hKHPZ+oE4GNqkjxgjb2vahkg4DsP2spCY/sHwO2Lk36yBpI+Bq4MIGY0YMsyxDqFf+PGMkZAYiBpak6ZImq3KWpDmS9uu9bvsvawp1DNWehz8qX2dTbYq7lKpza6y8haUKkwEkbQ38psF4k5ZasvQkeZ+LiIioVX6wxiD7gO35wH7A+sCRwIl1BynLCLakqkzyxvJ8BnA/2US9qo6nqsC0qaTzgGuATzUY70eSrpT0fknvB35A1dMjIsaWJUz1yp9njIQsYYpB1nsjfjtwju1/b3j5y8G2T5A0japiyUnAN4BdGozZWZImUQ383g3sSvX3Od32E03FtH1c6WA+rZz6lu1LlvdrIrqo9HtYpr6S0vtMQDqj5NNtJxAxEVKFKQaWpBlUexC2pGrwthpwve1GOn5KusP2DpK+BNxt+/zeuSbijQJJt9veaYJjbkzVe8KkClOMKEkPUP0fELAZ8MvyfD3gF2WmNcapNBod6wOTAKeRXIyaDCBiYJU72G8E7rf9K0mvBjZpqrSqpMupqgTtS9Ux+TmqD6BTm4g3CiSdCDwBXAA80zvfVEO9UoXpK8D1VD/Y9wCOs51N1DGSJJ0JXGL7h+X4bcBBto9pN7PhImnz5b1u+6GJyiViEGQAEQNL0kXAd4ArmuhePEa8tYC3Us0+zJP0B8B2tq9qOnZX9d0FXYLtRkodSroL2HfpKkwZBMaoknS37e1WdC4i4uXIJuoYZN8A3gfMk3SipG2aDGb7WdsX255Xjh/N4GGVTQG+DtwF3ElV1eoNDcZLFaaIJT0i6W8lbVEenwMeaTupYSVpV0mzJP1a0kJJiyXNbzuviImWH6wxsGxfbftwquVEDwJXS7pZ0tGSVm83uxins4E/Br5GNXiYUs41JVWYIpZ0GLARcAlwcXl+WKsZDbfTqf785gGvBD5IdZMkYqRkCVMMtLLv4QiqEq6PAOdRVdjZzvZeLaYW4yDpZ7anrOhczTH7qzDdmCpMESBpbdvPrPg7Y3l6hSEkze1tnE6xjRhFKeMaA0vSJcA2wDnAgbYfLS9dIOn29jKLl2GOpF1t/xRA0i5UTfqadDOwGHgBmNVwrIiBJml34NvAOsBmkqYCx9j+aLuZDa1nJa0B3Cnpy8CjZDVHjKDMQMTAkvQW29e1nUesPEn3UA0Cf1FObQbcCyyigdKHkj4IfAG4lqoK057ACba/U2eciGEh6VbgYOCy3l1ySf/P9rbtZjacSjWmx4A1gE8CrwK+bvu+VhOLmGAZQMRAk7Qt1br5NXvnbM9sL6N4OSa69KGke4HdbT9Zjl8N3Gy70Q34EYNK0q22d+lfZiPprlQmWzmSpts+dUXnIrouS5hiYEk6HtiLagDxQ+BtwE1ABhBDooXa6E8CC/qOF5RzEaPqP8syJpfiE9OBe1rOaZgdBSw9WHj/GOciOi0DiBhkB1N1oL7D9tGlw/C5LecUA0jSseXpfwC3SrqUqv/EO4FGGg9GDIkPU3243YSqUeZVwMdazWgISTqMqqz4lpIu63tpMtBIY8yIQZYBRAyy52y/IGmRpMnA48CmbScVA2nd8vW+8ui5tIVcIgaCpNWAU0s57Fg1N1NtmN4QOLnv/AJykyJGUAYQMchul7QecCYwG/g1cEu7KcUgsv134/k+SafZ/njT+UQMAtuLJW0uaQ3bC9vOZ5iV5ZgPAbuV2fCdy0v32F7UXmYR7cgm6hgKkrYAJtvOnZ5YaZLm2N6x7TwiJoqkmVTNHC8DXuwDYfuU1pIaYpIOAU4Crqeq9LYHcJztC9vMK2KiZQYiBpaka2zvA2D7waXPRUTECvWW9U3ipaV+sfL+FtjZ9uMAkjYCrgYygIiRkgFEDBxJawJrARtKWp/qLg9Um9U2aS2xiIghM97lfTFuk3qDh+JJ0kguRlAGEDGIjgH+CngN1d4HUVXUWQCc1mJeMfy04m+JGH6Svmr7ryR9n+r9cwm239FCWl1whaQrge+W40OpyoxHjJQMIGLglIY8p0r6AvBV2/MlfR7YkWyijnGSNAlYx/b8vtOp1R6j4pzy9aRWs+geA2cA08rxt4Bd20snoh3ZRB0DS9Jc29tLmgb8PdUPwi/Y3qXl1GJASTqfqu79YmAW1bK3U21/pdXEIqITxirE0PtZ1VZOEW3Iur0YZIvL1/2BM23/AFijxXxi8E0pMw4HAVcAWwJHtptSRHskPSDp/qUfbec1bCR9RNLdwDaS5vY9HiB9IGIEZQlTDLKHJZ0B7Av8H0m/Rwa9sXyrS1qdagBxuu3fSso0a4yynfqerwkcAmzQUi7D7HyqmxJfAj7Td36B7XSijpGTJUwxsCStBbwVuNv2PEl/AGxn+6qWU4sBJekTwKeBu6hmrjYDzrW9R6uJRQwQSbNtv6ntPCJieGUAERGdJukV6RQbo0pS/3r9SVQzEh+xPbWllCKiA7KEKSI6o1TuGssJE5pIxOA4ue/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          },
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      ],
      "source": [
        "#sorted correlation chart of numerical values\n",
        "\n",
        "mask = (df_3.corr() < 0.1)&(df_3.corr() > -0.1)\n",
        "sns.clustermap(df_3.corr(),cmap='RdBu',center=0,figsize=(11, 10),annot=True,mask=mask)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UV0SzAkaZNRQ"
      },
      "source": [
        "##### 1. Why did you pick the specific chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DVPuT8LYZNRQ"
      },
      "source": [
        "- A correlation heatmap helps us to visualize the correlations between multiple variables"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YPEH6qLeZNRQ"
      },
      "source": [
        "##### 2. What is/are the insight(s) found from the chart?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bfSqtnDqZNRR"
      },
      "source": [
        "- `adr` seems to be positively correlated with children,adults,total_of_special_requests,is_cancelled\n",
        "- `lead_time` seems to be positively correlated with stays_in_weeknd_nights,stays_in_week_nights \n",
        "- `is_repeated_guest` seems to be negatively correlated with adr, is_cancelled,lead_time\n",
        "-is cancelled seems to be positively correlated with lead_time and adr also negatively correlaed with total_of_special_requests\n",
        "Answer Here"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JcMwzZxoAimU"
      },
      "source": [
        "## **5. Solution to Business Objective**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8G2x9gOozGDZ"
      },
      "source": [
        "#### What do you suggest the client to achieve Business Objective ? \n",
        "- The Booking patterns consistently peak in July and August, increase staffing and inventory during these months to accommodate the increased demand.\n",
        "- Optimize pricing and availability of room types based on customer preferences. Since room type A is the most preferred, ensure that it is available during peak booking months and adjust pricing accordingly\n",
        "- Increase customer satisfaction by improving the online booking process and customer support.\n",
        "- Since breakfast in bed is the most popular meal,consider offering it more often or as a package deal to increase revenue and customer satisfaction.\n",
        "- negative correlation between ADR and stay duration suggests that customers staying for shorter periods generate more ADR. This information can inform the company to focus on attracting more of these customers to increase revenue.\n",
        "- The information on the average stay duration being relatively higher for the months June to September can help the hotel management to better allocate resources to meet customer needs during these periods.\n",
        "- To reduce the cancellation rates, the management could encourage customers who are making a deposit by providing additional offers and reduced rates.\n",
        "- The hotel should direct its marketing efforts \n",
        "towards online mediums to capture more bookings\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gCX9965dhzqZ"
      },
      "source": [
        "# **Conclusion**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fjb1IsQkh3yE"
      },
      "source": [
        "In summary, the hotel booking data can be used to gain insights into booking patterns and customer behavior, which can inform strategies for increasing occupancy, revenue, and customer satisfaction.\n",
        "\n",
        "The hotel should focus on increasing staffing and inventory during peak booking \n",
        "months, improve pricing strategy, improve customer support, offer popular amenities \n",
        "and services as add-ones during booking, build relationships with online travel agents, \n",
        "encourage deposits during booking also direct marketing towards online and social \n",
        "media.\n",
        "\n",
        " By implementing these strategies, the hotel management will be able to achieve their business objectives."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4S1X0D-lxtyd"
      },
      "source": [
        "# **Scope**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQmFXc26xtyd"
      },
      "source": [
        "Further analysis needs to be carried out to improve customer satisfaction by incorporating customer feedback in the dataset\\\n",
        "With the available data a machine learning model can be created to predict room cancellations or booking trends "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FgFillB7xtye"
      },
      "source": [
        "### References\n",
        "1. [Almabetter - Full Stack Data Science](https://grow.almabetter.com/data-science/learn/full-stack-data-science)\n",
        "2. [Codebasics - Pandas Tutorial (Data Analysis In Python) ](https://youtu.be/CmorAWRsCAw)\n",
        "3. [Udacity - Intro to Data Analysis](https://www.udacity.com/course/intro-to-data-analysis--ud170)\n"
      ]
    }
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