Team_Project_Hotel_Booking_Analysis_Capstone_Project.ipynb

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        "<a href=\"https://colab.research.google.com/github/abinashkng/Capstone-EDA/blob/main/Team_Project_Hotel_Booking_Analysis_Capstone_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "## <b> 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!\n",
        "\n",
        "## <b>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. </b>\n",
        "\n",
        "## <b> Explore and analyze the data to discover important factors that govern the bookings. </b>"
      ]
    },
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)"
      ],
      "metadata": {
        "id": "EC4NDoJGeMtG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**INTRODUCTION**\n",
        "\n",
        "HOTEL industry is one of the most important industry, an individual can travel as many places by booking a hotel at that perticular place or place near by , the booking of the place depends on certain factors  such as type of hotels, seasonality, days of week and many more. Past hotel booking data helps the owner  analyzing the patterns available in the past data and to perform certain activities to boost their business\n",
        "\n",
        "\n",
        "We will be using the data available to analyze the factors affecting the hotel bookings\n",
        "\n",
        "hotels avilable for booking\n",
        "\n",
        "*  hotels avilable for booking\n",
        "*  revenue of the hotels \n",
        "*  The types of booking\n",
        "*  booking month wise \n",
        "*  booking year wise\n",
        "*  booking country wise\n",
        "\n",
        "and many more \n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "IrFtmDmenVCM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## **To do very first import packages**"
      ],
      "metadata": {
        "id": "8KKxOlJyWYY5"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dByMsuzT8Tnw"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NfaM1OU2XuI2",
        "outputId": "af9db50b-43c5-4c19-92dc-1dcb38c0d56d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Let us read our data first**"
      ],
      "metadata": {
        "id": "HNJsSsQYYaXU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hb_data_path = '/content/drive/MyDrive/Almabetter/Python for Data Science/20. EDA Project (Hotel booking analysis)/Hotel Bookings.csv'\n",
        "hb = pd.read_csv(hb_data_path)\n"
      ],
      "metadata": {
        "id": "EKCSUMEgYZDc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# To know the number of rows and columns in given data set\n",
        "hb.shape"
      ],
      "metadata": {
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        },
        "id": "p5rFteTSZv3V",
        "outputId": "11b24576-018a-4042-ed14-c81be375a557"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(119390, 32)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This is used to show the first 5 rows including all the columns of data \n",
        "hb.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "Jv-DQeUEZcNb",
        "outputId": "b37b16ca-0665-48b1-f02b-e10e247f0bda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          hotel  is_canceled  lead_time  arrival_date_year arrival_date_month  \\\n",
              "0  Resort Hotel            0        342               2015               July   \n",
              "1  Resort Hotel            0        737               2015               July   \n",
              "2  Resort Hotel            0          7               2015               July   \n",
              "3  Resort Hotel            0         13               2015               July   \n",
              "4  Resort Hotel            0         14               2015               July   \n",
              "\n",
              "   arrival_date_week_number  arrival_date_day_of_month  \\\n",
              "0                        27                          1   \n",
              "1                        27                          1   \n",
              "2                        27                          1   \n",
              "3                        27                          1   \n",
              "4                        27                          1   \n",
              "\n",
              "   stays_in_weekend_nights  stays_in_week_nights  adults  ...  deposit_type  \\\n",
              "0                        0                     0       2  ...    No Deposit   \n",
              "1                        0                     0       2  ...    No Deposit   \n",
              "2                        0                     1       1  ...    No Deposit   \n",
              "3                        0                     1       1  ...    No Deposit   \n",
              "4                        0                     2       2  ...    No Deposit   \n",
              "\n",
              "   agent company days_in_waiting_list customer_type   adr  \\\n",
              "0    NaN     NaN                    0     Transient   0.0   \n",
              "1    NaN     NaN                    0     Transient   0.0   \n",
              "2    NaN     NaN                    0     Transient  75.0   \n",
              "3  304.0     NaN                    0     Transient  75.0   \n",
              "4  240.0     NaN                    0     Transient  98.0   \n",
              "\n",
              "   required_car_parking_spaces  total_of_special_requests  reservation_status  \\\n",
              "0                            0                          0           Check-Out   \n",
              "1                            0                          0           Check-Out   \n",
              "2                            0                          0           Check-Out   \n",
              "3                            0                          0           Check-Out   \n",
              "4                            0                          1           Check-Out   \n",
              "\n",
              "  reservation_status_date  \n",
              "0              2015-07-01  \n",
              "1              2015-07-01  \n",
              "2              2015-07-02  \n",
              "3              2015-07-02  \n",
              "4              2015-07-03  \n",
              "\n",
              "[5 rows x 32 columns]"
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>hotel</th>\n",
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              "      <th>arrival_date_year</th>\n",
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              "      <th>adults</th>\n",
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              "      <th>0</th>\n",
              "      <td>Resort Hotel</td>\n",
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              "      <td>27</td>\n",
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              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
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              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Transient</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>2015-07-01</td>\n",
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              "      <th>2</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>75.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>2015-07-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>304.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>75.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>2015-07-02</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <td>240.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>98.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>2015-07-03</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 32 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-61f0baac-369d-41a5-a178-f416d67dee9a')\"\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-61f0baac-369d-41a5-a178-f416d67dee9a');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
          "execution_count": 5
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hb.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zcKDVDNJa44k",
        "outputId": "d8eab023-4fe8-44df-83b7-5221a90d11d4"
      },
      "execution_count": null,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <u>**<code>describe()</code>**</u>\n",
        "\n",
        "## This method gives the overall descriptive summary of the dataframe. This works only for numerical columns"
      ],
      "metadata": {
        "id": "AAxc49BqdKf4"
      }
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    {
      "cell_type": "code",
      "source": [
        "hb.describe()"
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      "metadata": {
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          "height": 364
        },
        "id": "7aaBt9Sca-F5",
        "outputId": "9d8411f9-50eb-4755-e70b-15e0143ea8bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         is_canceled      lead_time  arrival_date_year  \\\n",
              "count  119390.000000  119390.000000      119390.000000   \n",
              "mean        0.370416     104.011416        2016.156554   \n",
              "std         0.482918     106.863097           0.707476   \n",
              "min         0.000000       0.000000        2015.000000   \n",
              "25%         0.000000      18.000000        2016.000000   \n",
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              "\n",
              "       arrival_date_week_number  arrival_date_day_of_month  \\\n",
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              "mean                  27.165173                  15.798241   \n",
              "std                   13.605138                   8.780829   \n",
              "min                    1.000000                   1.000000   \n",
              "25%                   16.000000                   8.000000   \n",
              "50%                   28.000000                  16.000000   \n",
              "75%                   38.000000                  23.000000   \n",
              "max                   53.000000                  31.000000   \n",
              "\n",
              "       stays_in_weekend_nights  stays_in_week_nights         adults  \\\n",
              "count            119390.000000         119390.000000  119390.000000   \n",
              "mean                  0.927599              2.500302       1.856403   \n",
              "std                   0.998613              1.908286       0.579261   \n",
              "min                   0.000000              0.000000       0.000000   \n",
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              "max                  19.000000             50.000000      55.000000   \n",
              "\n",
              "            children         babies  is_repeated_guest  \\\n",
              "count  119386.000000  119390.000000      119390.000000   \n",
              "mean        0.103890       0.007949           0.031912   \n",
              "std         0.398561       0.097436           0.175767   \n",
              "min         0.000000       0.000000           0.000000   \n",
              "25%         0.000000       0.000000           0.000000   \n",
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              "max        10.000000      10.000000           1.000000   \n",
              "\n",
              "       previous_cancellations  previous_bookings_not_canceled  \\\n",
              "count           119390.000000                   119390.000000   \n",
              "mean                 0.087118                        0.137097   \n",
              "std                  0.844336                        1.497437   \n",
              "min                  0.000000                        0.000000   \n",
              "25%                  0.000000                        0.000000   \n",
              "50%                  0.000000                        0.000000   \n",
              "75%                  0.000000                        0.000000   \n",
              "max                 26.000000                       72.000000   \n",
              "\n",
              "       booking_changes          agent      company  days_in_waiting_list  \\\n",
              "count    119390.000000  103050.000000  6797.000000         119390.000000   \n",
              "mean          0.221124      86.693382   189.266735              2.321149   \n",
              "std           0.652306     110.774548   131.655015             17.594721   \n",
              "min           0.000000       1.000000     6.000000              0.000000   \n",
              "25%           0.000000       9.000000    62.000000              0.000000   \n",
              "50%           0.000000      14.000000   179.000000              0.000000   \n",
              "75%           0.000000     229.000000   270.000000              0.000000   \n",
              "max          21.000000     535.000000   543.000000            391.000000   \n",
              "\n",
              "                 adr  required_car_parking_spaces  total_of_special_requests  \n",
              "count  119390.000000                119390.000000              119390.000000  \n",
              "mean      101.831122                     0.062518                   0.571363  \n",
              "std        50.535790                     0.245291                   0.792798  \n",
              "min        -6.380000                     0.000000                   0.000000  \n",
              "25%        69.290000                     0.000000                   0.000000  \n",
              "50%        94.575000                     0.000000                   0.000000  \n",
              "75%       126.000000                     0.000000                   1.000000  \n",
              "max      5400.000000                     8.000000                   5.000000  "
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              "        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>is_canceled</th>\n",
              "      <th>lead_time</th>\n",
              "      <th>arrival_date_year</th>\n",
              "      <th>arrival_date_week_number</th>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <th>stays_in_weekend_nights</th>\n",
              "      <th>stays_in_week_nights</th>\n",
              "      <th>adults</th>\n",
              "      <th>children</th>\n",
              "      <th>babies</th>\n",
              "      <th>is_repeated_guest</th>\n",
              "      <th>previous_cancellations</th>\n",
              "      <th>previous_bookings_not_canceled</th>\n",
              "      <th>booking_changes</th>\n",
              "      <th>agent</th>\n",
              "      <th>company</th>\n",
              "      <th>days_in_waiting_list</th>\n",
              "      <th>adr</th>\n",
              "      <th>required_car_parking_spaces</th>\n",
              "      <th>total_of_special_requests</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119386.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>103050.000000</td>\n",
              "      <td>6797.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.370416</td>\n",
              "      <td>104.011416</td>\n",
              "      <td>2016.156554</td>\n",
              "      <td>27.165173</td>\n",
              "      <td>15.798241</td>\n",
              "      <td>0.927599</td>\n",
              "      <td>2.500302</td>\n",
              "      <td>1.856403</td>\n",
              "      <td>0.103890</td>\n",
              "      <td>0.007949</td>\n",
              "      <td>0.031912</td>\n",
              "      <td>0.087118</td>\n",
              "      <td>0.137097</td>\n",
              "      <td>0.221124</td>\n",
              "      <td>86.693382</td>\n",
              "      <td>189.266735</td>\n",
              "      <td>2.321149</td>\n",
              "      <td>101.831122</td>\n",
              "      <td>0.062518</td>\n",
              "      <td>0.571363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.482918</td>\n",
              "      <td>106.863097</td>\n",
              "      <td>0.707476</td>\n",
              "      <td>13.605138</td>\n",
              "      <td>8.780829</td>\n",
              "      <td>0.998613</td>\n",
              "      <td>1.908286</td>\n",
              "      <td>0.579261</td>\n",
              "      <td>0.398561</td>\n",
              "      <td>0.097436</td>\n",
              "      <td>0.175767</td>\n",
              "      <td>0.844336</td>\n",
              "      <td>1.497437</td>\n",
              "      <td>0.652306</td>\n",
              "      <td>110.774548</td>\n",
              "      <td>131.655015</td>\n",
              "      <td>17.594721</td>\n",
              "      <td>50.535790</td>\n",
              "      <td>0.245291</td>\n",
              "      <td>0.792798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2015.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-6.380000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>69.290000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>69.000000</td>\n",
              "      <td>2016.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>179.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.575000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>160.000000</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>229.000000</td>\n",
              "      <td>270.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>737.000000</td>\n",
              "      <td>2017.000000</td>\n",
              "      <td>53.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>72.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>535.000000</td>\n",
              "      <td>543.000000</td>\n",
              "      <td>391.000000</td>\n",
              "      <td>5400.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-14f4237c-36fa-4641-a963-3b15d97dd8cc')\"\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",
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              "  </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",
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              "      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",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      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-14f4237c-36fa-4641-a963-3b15d97dd8cc 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-14f4237c-36fa-4641-a963-3b15d97dd8cc');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Now we will do data cleaning**"
      ],
      "metadata": {
        "id": "CywYJDp2fPMs"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "First replacing null value with 0"
      ],
      "metadata": {
        "id": "9WgTUA6rSN5n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Find Null values\n",
        "hb.isna().sum().sort_values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LD8tHHnTdSV3",
        "outputId": "e43ee386-f10e-435a-eee7-3b2639395877"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "hotel                                  0\n",
              "total_of_special_requests              0\n",
              "required_car_parking_spaces            0\n",
              "adr                                    0\n",
              "customer_type                          0\n",
              "days_in_waiting_list                   0\n",
              "deposit_type                           0\n",
              "booking_changes                        0\n",
              "assigned_room_type                     0\n",
              "reserved_room_type                     0\n",
              "previous_bookings_not_canceled         0\n",
              "previous_cancellations                 0\n",
              "is_repeated_guest                      0\n",
              "reservation_status                     0\n",
              "distribution_channel                   0\n",
              "reservation_status_date                0\n",
              "meal                                   0\n",
              "babies                                 0\n",
              "adults                                 0\n",
              "stays_in_week_nights                   0\n",
              "stays_in_weekend_nights                0\n",
              "arrival_date_day_of_month              0\n",
              "market_segment                         0\n",
              "is_canceled                            0\n",
              "arrival_date_week_number               0\n",
              "arrival_date_month                     0\n",
              "arrival_date_year                      0\n",
              "lead_time                              0\n",
              "children                               4\n",
              "country                              488\n",
              "agent                              16340\n",
              "company                           112593\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are trying to find null values across different columns in the data frame. If present in some of these, we will replace them with appropriate values"
      ],
      "metadata": {
        "id": "x08fFPlQ17Fw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have 4 features with missing values.\n",
        "\n",
        "\n",
        "* In the agent and the company column, we have id_number for each agent or     company, so for all the missing values, we will just replace it with 0.\n",
        "\n",
        "*   Children column contains the count of children, so we will replace all the missing values with 0.\n",
        "\n",
        "* And our country column contains country codes representing different countries so also replace it with 0.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Oi5VdYfgceVH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Replacing Null values with izero \n",
        "\n",
        "hb.fillna(0, inplace=True)"
      ],
      "metadata": {
        "id": "vAuNDvFjgHen"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hb.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "seRjUzICha1T",
        "outputId": "2815c4b7-e4cb-42f0-a829-63e3620c7038"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "hotel                             0\n",
              "is_canceled                       0\n",
              "lead_time                         0\n",
              "arrival_date_year                 0\n",
              "arrival_date_month                0\n",
              "arrival_date_week_number          0\n",
              "arrival_date_day_of_month         0\n",
              "stays_in_weekend_nights           0\n",
              "stays_in_week_nights              0\n",
              "adults                            0\n",
              "children                          0\n",
              "babies                            0\n",
              "meal                              0\n",
              "country                           0\n",
              "market_segment                    0\n",
              "distribution_channel              0\n",
              "is_repeated_guest                 0\n",
              "previous_cancellations            0\n",
              "previous_bookings_not_canceled    0\n",
              "reserved_room_type                0\n",
              "assigned_room_type                0\n",
              "booking_changes                   0\n",
              "deposit_type                      0\n",
              "agent                             0\n",
              "company                           0\n",
              "days_in_waiting_list              0\n",
              "customer_type                     0\n",
              "adr                               0\n",
              "required_car_parking_spaces       0\n",
              "total_of_special_requests         0\n",
              "reservation_status                0\n",
              "reservation_status_date           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can see there is no null value in hotel booking dataframe\n"
      ],
      "metadata": {
        "id": "caZb0HRnQLwv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Now we will check for the wrong entry\n",
        "\n",
        "As we can see in the data set there are 3 categories of guests which are children , adults and babies"
      ],
      "metadata": {
        "id": "GLG2KBl3SY2N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#checking value of children\n",
        "hb['children'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RIUS6mQ7hk5R",
        "outputId": "2c8c1f7d-8ea4-4ca8-91f0-3e49e8398353"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  1.,  2., 10.,  3., nan])"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking value of adults\n",
        "hb['adults'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uomZ54xgVcj0",
        "outputId": "e2cec291-c3c8-4e66-f07b-68b79864aa50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2,  1,  3,  4, 40, 26, 50, 27, 55,  0, 20,  6,  5, 10])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#checking value of babies\n",
        "hb['babies'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4K4KOeUDWXKW",
        "outputId": "9e04b71e-8279-4058-ead3-34cb5112f06a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0,  1,  2, 10,  9])"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**If hotel is booked than there should be any entries in children,adults or babies columns,it should not zero at a time**\n",
        "\n",
        "So to check this we will filter the data"
      ],
      "metadata": {
        "id": "hZ_qbEdVd0Px"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter data where value of adults, children and babies = 0\n",
        "\n",
        "checked_values = (hb['children']==0) & (hb['adults']==0) & (hb['babies']==0)\n",
        "\n",
        "# reading data frame where adult,children and babies are 0\n",
        "hb[checked_values]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "VTEZJKkfWydk",
        "outputId": "5e349563-a2de-4fc6-f2ca-e7dc57cadf6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               hotel  is_canceled  lead_time  arrival_date_year  \\\n",
              "2224    Resort Hotel            0          1               2015   \n",
              "2409    Resort Hotel            0          0               2015   \n",
              "3181    Resort Hotel            0         36               2015   \n",
              "3684    Resort Hotel            0        165               2015   \n",
              "3708    Resort Hotel            0        165               2015   \n",
              "...              ...          ...        ...                ...   \n",
              "115029    City Hotel            0        107               2017   \n",
              "115091    City Hotel            0          1               2017   \n",
              "116251    City Hotel            0         44               2017   \n",
              "116534    City Hotel            0          2               2017   \n",
              "117087    City Hotel            0        170               2017   \n",
              "\n",
              "       arrival_date_month  arrival_date_week_number  \\\n",
              "2224              October                        41   \n",
              "2409              October                        42   \n",
              "3181             November                        47   \n",
              "3684             December                        53   \n",
              "3708             December                        53   \n",
              "...                   ...                       ...   \n",
              "115029               June                        26   \n",
              "115091               June                        26   \n",
              "116251               July                        28   \n",
              "116534               July                        28   \n",
              "117087               July                        30   \n",
              "\n",
              "        arrival_date_day_of_month  stays_in_weekend_nights  \\\n",
              "2224                            6                        0   \n",
              "2409                           12                        0   \n",
              "3181                           20                        1   \n",
              "3684                           30                        1   \n",
              "3708                           30                        2   \n",
              "...                           ...                      ...   \n",
              "115029                         27                        0   \n",
              "115091                         30                        0   \n",
              "116251                         15                        1   \n",
              "116534                         15                        2   \n",
              "117087                         27                        0   \n",
              "\n",
              "        stays_in_week_nights  adults  ...  deposit_type  agent company  \\\n",
              "2224                       3       0  ...    No Deposit    NaN   174.0   \n",
              "2409                       0       0  ...    No Deposit    NaN   174.0   \n",
              "3181                       2       0  ...    No Deposit   38.0     NaN   \n",
              "3684                       4       0  ...    No Deposit  308.0     NaN   \n",
              "3708                       4       0  ...    No Deposit  308.0     NaN   \n",
              "...                      ...     ...  ...           ...    ...     ...   \n",
              "115029                     3       0  ...    No Deposit    7.0     NaN   \n",
              "115091                     1       0  ...    No Deposit    NaN     NaN   \n",
              "116251                     1       0  ...    No Deposit  425.0     NaN   \n",
              "116534                     5       0  ...    No Deposit    9.0     NaN   \n",
              "117087                     2       0  ...    No Deposit   52.0     NaN   \n",
              "\n",
              "       days_in_waiting_list    customer_type     adr  \\\n",
              "2224                      0  Transient-Party    0.00   \n",
              "2409                      0        Transient    0.00   \n",
              "3181                      0  Transient-Party    0.00   \n",
              "3684                    122  Transient-Party    0.00   \n",
              "3708                    122  Transient-Party    0.00   \n",
              "...                     ...              ...     ...   \n",
              "115029                    0        Transient  100.80   \n",
              "115091                    0        Transient    0.00   \n",
              "116251                    0        Transient   73.80   \n",
              "116534                    0  Transient-Party   22.86   \n",
              "117087                    0        Transient    0.00   \n",
              "\n",
              "        required_car_parking_spaces  total_of_special_requests  \\\n",
              "2224                              0                          0   \n",
              "2409                              0                          0   \n",
              "3181                              0                          0   \n",
              "3684                              0                          0   \n",
              "3708                              0                          0   \n",
              "...                             ...                        ...   \n",
              "115029                            0                          0   \n",
              "115091                            1                          1   \n",
              "116251                            0                          0   \n",
              "116534                            0                          1   \n",
              "117087                            0                          0   \n",
              "\n",
              "        reservation_status reservation_status_date  \n",
              "2224             Check-Out              2015-10-06  \n",
              "2409             Check-Out              2015-10-12  \n",
              "3181             Check-Out              2015-11-23  \n",
              "3684             Check-Out              2016-01-04  \n",
              "3708             Check-Out              2016-01-05  \n",
              "...                    ...                     ...  \n",
              "115029           Check-Out              2017-06-30  \n",
              "115091           Check-Out              2017-07-01  \n",
              "116251           Check-Out              2017-07-17  \n",
              "116534           Check-Out              2017-07-22  \n",
              "117087           Check-Out              2017-07-29  \n",
              "\n",
              "[180 rows x 32 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-43cef58b-1dce-4330-9db8-9bfdf0bb60d9\">\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>hotel</th>\n",
              "      <th>is_canceled</th>\n",
              "      <th>lead_time</th>\n",
              "      <th>arrival_date_year</th>\n",
              "      <th>arrival_date_month</th>\n",
              "      <th>arrival_date_week_number</th>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <th>stays_in_weekend_nights</th>\n",
              "      <th>stays_in_week_nights</th>\n",
              "      <th>adults</th>\n",
              "      <th>...</th>\n",
              "      <th>deposit_type</th>\n",
              "      <th>agent</th>\n",
              "      <th>company</th>\n",
              "      <th>days_in_waiting_list</th>\n",
              "      <th>customer_type</th>\n",
              "      <th>adr</th>\n",
              "      <th>required_car_parking_spaces</th>\n",
              "      <th>total_of_special_requests</th>\n",
              "      <th>reservation_status</th>\n",
              "      <th>reservation_status_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2224</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2015</td>\n",
              "      <td>October</td>\n",
              "      <td>41</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>174.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient-Party</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>2015-10-06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2409</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>October</td>\n",
              "      <td>42</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>174.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>2015-10-12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3181</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>2015</td>\n",
              "      <td>November</td>\n",
              "      <td>47</td>\n",
              "      <td>20</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>38.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient-Party</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>2015-11-23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3684</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>165</td>\n",
              "      <td>2015</td>\n",
              "      <td>December</td>\n",
              "      <td>53</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>308.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>122</td>\n",
              "      <td>Transient-Party</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>2016-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3708</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>165</td>\n",
              "      <td>2015</td>\n",
              "      <td>December</td>\n",
              "      <td>53</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>308.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>122</td>\n",
              "      <td>Transient-Party</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>2016-01-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115029</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>107</td>\n",
              "      <td>2017</td>\n",
              "      <td>June</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>7.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>100.80</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>2017-06-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115091</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2017</td>\n",
              "      <td>June</td>\n",
              "      <td>26</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>2017-07-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116251</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>44</td>\n",
              "      <td>2017</td>\n",
              "      <td>July</td>\n",
              "      <td>28</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>425.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>73.80</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>2017-07-17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116534</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2017</td>\n",
              "      <td>July</td>\n",
              "      <td>28</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient-Party</td>\n",
              "      <td>22.86</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>2017-07-22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117087</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>170</td>\n",
              "      <td>2017</td>\n",
              "      <td>July</td>\n",
              "      <td>30</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>52.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>2017-07-29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>180 rows × 32 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-43cef58b-1dce-4330-9db8-9bfdf0bb60d9')\"\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-43cef58b-1dce-4330-9db8-9bfdf0bb60d9 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-43cef58b-1dce-4330-9db8-9bfdf0bb60d9');\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": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**There are 180 rows which having wrong values so we will remove this values**"
      ],
      "metadata": {
        "id": "4MffQL6xcnWj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we use compliment operator tilde(~) , It takes one bit operand and returns its complement. If the operand is 1, it returns 0, and if it is 0, it returns 1\n",
        "\n",
        "or \n",
        "Here we can also use dropna() method to drop that rows."
      ],
      "metadata": {
        "id": "dq-3aL9TgRUM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#storing data to variable where adults,children and babies are having not 0 \n",
        "correct_hb = hb[~checked_values]\n",
        "correct_hb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "IVwyhCdWf2JY",
        "outputId": "ba9351a5-cfa0-45a8-90f9-ac00e859c2bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               hotel  is_canceled  lead_time  arrival_date_year  \\\n",
              "0       Resort Hotel            0        342               2015   \n",
              "1       Resort Hotel            0        737               2015   \n",
              "2       Resort Hotel            0          7               2015   \n",
              "3       Resort Hotel            0         13               2015   \n",
              "4       Resort Hotel            0         14               2015   \n",
              "...              ...          ...        ...                ...   \n",
              "119385    City Hotel            0         23               2017   \n",
              "119386    City Hotel            0        102               2017   \n",
              "119387    City Hotel            0         34               2017   \n",
              "119388    City Hotel            0        109               2017   \n",
              "119389    City Hotel            0        205               2017   \n",
              "\n",
              "       arrival_date_month  arrival_date_week_number  \\\n",
              "0                    July                        27   \n",
              "1                    July                        27   \n",
              "2                    July                        27   \n",
              "3                    July                        27   \n",
              "4                    July                        27   \n",
              "...                   ...                       ...   \n",
              "119385             August                        35   \n",
              "119386             August                        35   \n",
              "119387             August                        35   \n",
              "119388             August                        35   \n",
              "119389             August                        35   \n",
              "\n",
              "        arrival_date_day_of_month  stays_in_weekend_nights  \\\n",
              "0                               1                        0   \n",
              "1                               1                        0   \n",
              "2                               1                        0   \n",
              "3                               1                        0   \n",
              "4                               1                        0   \n",
              "...                           ...                      ...   \n",
              "119385                         30                        2   \n",
              "119386                         31                        2   \n",
              "119387                         31                        2   \n",
              "119388                         31                        2   \n",
              "119389                         29                        2   \n",
              "\n",
              "        stays_in_week_nights  adults  ...  deposit_type  agent company  \\\n",
              "0                          0       2  ...    No Deposit    NaN     NaN   \n",
              "1                          0       2  ...    No Deposit    NaN     NaN   \n",
              "2                          1       1  ...    No Deposit    NaN     NaN   \n",
              "3                          1       1  ...    No Deposit  304.0     NaN   \n",
              "4                          2       2  ...    No Deposit  240.0     NaN   \n",
              "...                      ...     ...  ...           ...    ...     ...   \n",
              "119385                     5       2  ...    No Deposit  394.0     NaN   \n",
              "119386                     5       3  ...    No Deposit    9.0     NaN   \n",
              "119387                     5       2  ...    No Deposit    9.0     NaN   \n",
              "119388                     5       2  ...    No Deposit   89.0     NaN   \n",
              "119389                     7       2  ...    No Deposit    9.0     NaN   \n",
              "\n",
              "       days_in_waiting_list customer_type     adr  \\\n",
              "0                         0     Transient    0.00   \n",
              "1                         0     Transient    0.00   \n",
              "2                         0     Transient   75.00   \n",
              "3                         0     Transient   75.00   \n",
              "4                         0     Transient   98.00   \n",
              "...                     ...           ...     ...   \n",
              "119385                    0     Transient   96.14   \n",
              "119386                    0     Transient  225.43   \n",
              "119387                    0     Transient  157.71   \n",
              "119388                    0     Transient  104.40   \n",
              "119389                    0     Transient  151.20   \n",
              "\n",
              "        required_car_parking_spaces  total_of_special_requests  \\\n",
              "0                                 0                          0   \n",
              "1                                 0                          0   \n",
              "2                                 0                          0   \n",
              "3                                 0                          0   \n",
              "4                                 0                          1   \n",
              "...                             ...                        ...   \n",
              "119385                            0                          0   \n",
              "119386                            0                          2   \n",
              "119387                            0                          4   \n",
              "119388                            0                          0   \n",
              "119389                            0                          2   \n",
              "\n",
              "        reservation_status reservation_status_date  \n",
              "0                Check-Out              2015-07-01  \n",
              "1                Check-Out              2015-07-01  \n",
              "2                Check-Out              2015-07-02  \n",
              "3                Check-Out              2015-07-02  \n",
              "4                Check-Out              2015-07-03  \n",
              "...                    ...                     ...  \n",
              "119385           Check-Out              2017-09-06  \n",
              "119386           Check-Out              2017-09-07  \n",
              "119387           Check-Out              2017-09-07  \n",
              "119388           Check-Out              2017-09-07  \n",
              "119389           Check-Out              2017-09-07  \n",
              "\n",
              "[119210 rows x 32 columns]"
            ],
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              "      <th>hotel</th>\n",
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              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>75.00</td>\n",
              "      <td>0</td>\n",
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              "      <td>2015-07-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>Resort Hotel</td>\n",
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              "      <td>Check-Out</td>\n",
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              "      <td>City Hotel</td>\n",
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              "      <td>August</td>\n",
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              "      <td>City Hotel</td>\n",
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              "      <td>102</td>\n",
              "      <td>2017</td>\n",
              "      <td>August</td>\n",
              "      <td>35</td>\n",
              "      <td>31</td>\n",
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              "      <td>89.0</td>\n",
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              "      <td>No Deposit</td>\n",
              "      <td>9.0</td>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Now we will do Data Analysis and Data Visualisation**"
      ],
      "metadata": {
        "id": "8SukGgrQcHnQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Now there is a situation, someone new in hotel market he/she wants some informational data analysis to get enter in this market,so he/she raises some questions regarding hotel market in which i have to analysis the data and answer them.**\n",
        "\n",
        "\n",
        "\n",
        "**Questions are given below**"
      ],
      "metadata": {
        "id": "gNhuT0txheST"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1. What types of hotel in market and how many numbers of hotels in each type?**\n",
        "\n",
        "For this comparision we will use pie chart ploting."
      ],
      "metadata": {
        "id": "n9T3yawBjnHe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hotel_count = correct_hb['hotel'].value_counts()\n",
        "hotel_count\n"
      ],
      "metadata": {
        "id": "VGk_wXuMgOWh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "002141cc-524a-4994-dbf4-fd4751a06bb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "City Hotel      79163\n",
              "Resort Hotel    40047\n",
              "Name: hotel, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " # Enlarging the pie chart\n",
        "plt.rcParams['figure.figsize'] = 8,8\n",
        "\n",
        "# Indexing labels. tolist() will convert the index to list for easy manipulation\n",
        "labels = correct_hb['hotel'].value_counts().index.tolist()\n",
        "\n",
        "sizes = correct_hb['hotel'].value_counts().tolist()\n",
        "\n",
        "# As the name suggest, explode will determine how much each section is separated from each other \n",
        "explode = (0, 0.1)\n",
        "\n",
        "# Determine colour of pie chart\n",
        "colors = ['gold', 'aqua']\n",
        "\n",
        "plt.pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%',startangle=90, textprops={'fontsize': 14})\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "b0VrAftCf97G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "40f6ea57-eb3e-4d6e-f1b7-19b3a616f0b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###According to this analysis number of City hotels comparatively more than Resort hotels.\n",
        " \n",
        "###Resort Hotel tend to be on the expensive side and most people will just stick with city hotel."
      ],
      "metadata": {
        "id": "tIzoXzSJptdG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " **2. How Many Booking Were Cancelled for diffrent types of hotels ?**\n",
        "\n"
      ],
      "metadata": {
        "id": "EAXazum4tTYd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Total number of canceled hotel booking\n",
        "total_canceled_hotels_count = correct_hb.is_canceled.value_counts()\n",
        "print (total_canceled_hotels_count)\n",
        "\n",
        "#Total number of canceled City hotel booking\n",
        "canceled_city_hotels_count = correct_hb[correct_hb.hotel == 'City Hotel'].is_canceled.value_counts()\n",
        "print (canceled_city_hotels_count)\n",
        "\n",
        "#Total number of canceled Resort hotel booking\n",
        "canceled_resort_hotels_count = correct_hb[correct_hb.hotel == 'Resort Hotel'].is_canceled.value_counts()\n",
        "print (canceled_resort_hotels_count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujyfelZZ2WgL",
        "outputId": "83143b49-9f34-4a46-a30f-1fce1b6b0f71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    75011\n",
            "1    44199\n",
            "Name: is_canceled, dtype: int64\n",
            "0    46084\n",
            "1    33079\n",
            "Name: is_canceled, dtype: int64\n",
            "0    28927\n",
            "1    11120\n",
            "Name: is_canceled, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('City Hotel:')\n",
        "print('===========')\n",
        "total_city_hotels_booking = hotel_count['City Hotel'] \n",
        "print('Booking:', total_city_hotels_booking)\n",
        "\n",
        "# Value indicating if the booking was canceled (1) or not canceled (0)\n",
        "\n",
        "canceled_city_hotels_booking = correct_hb[correct_hb.hotel == 'City Hotel'].is_canceled.sum()\n",
        "print('Canceled:',canceled_city_hotels_booking)\n",
        "\n",
        "not_canceled_city_hotels_booking = total_city_hotels_booking - canceled_city_hotels_booking\n",
        "print('Not canceled:', not_canceled_city_hotels_booking)\n",
        "\n",
        "print('')\n",
        "print('Resort Hotel')\n",
        "print('============')\n",
        "total_resort_hotels_booking = hotel_count['Resort Hotel'] \n",
        "print('Booking:', total_resort_hotels_booking)\n",
        "\n",
        "# Value indicating if the booking was canceled (1) or not canceled(0)\n",
        "\n",
        "canceled_resort_hotels_booking = correct_hb[correct_hb.hotel == 'Resort Hotel'].is_canceled.sum()\n",
        "print('Canceled:',canceled_resort_hotels_booking)\n",
        "not_canceled_resort_hotels_booking = total_resort_hotels_booking - canceled_resort_hotels_booking\n",
        "print('Not canceled:', not_canceled_resort_hotels_booking)"
      ],
      "metadata": {
        "id": "jU-7qDIe3RHz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb6a7076-4c65-4cd4-9b65-05d0c41fa826"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "City Hotel:\n",
            "===========\n",
            "Booking: 79163\n",
            "Canceled: 33079\n",
            "Not canceled: 46084\n",
            "\n",
            "Resort Hotel\n",
            "============\n",
            "Booking: 40047\n",
            "Canceled: 11120\n",
            "Not canceled: 28927\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Draw the graph\n",
        "xs = ['City hotel canceled', 'City hotel not canceled', 'Resort hotel canceled', 'Resort hotel not canceled'] \n",
        "ys = [canceled_city_hotels_booking, not_canceled_city_hotels_booking, canceled_resort_hotels_booking, not_canceled_resort_hotels_booking] \n",
        "\n",
        "angle = 90\n",
        "plt.bar(xs, ys,color='red')\n",
        "plt.xticks(rotation=angle)\n",
        "\n",
        "plt.title('Cancelation of each type of hotels',fontsize=20)\n",
        "plt.ylabel('Number of canceleod hotel booking',fontsize=15)\n",
        "plt.xlabel('Types of hotels',fontsize=15)\n",
        "\n",
        "# Set the display figure size using rcParams method \n",
        "plt.rcParams['figure.figsize'] = (10,5)"
      ],
      "metadata": {
        "id": "q6MR_Ni-f9wR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 477
        },
        "outputId": "9e663149-89af-48c0-e8ef-e551422a80c5"
      },
      "execution_count": null,
      "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"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###From above analysis we can say that city hotels bookings are more canceled than resort hotels."
      ],
      "metadata": {
        "id": "FvXqnYrV0_d4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Hotels booking which are not canceled\n",
        "hotels_not_canceled = correct_hb[correct_hb['is_canceled'] == 0]\n"
      ],
      "metadata": {
        "id": "AAZx5rQM3s5H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Year wise comparision of booking of hotels.**\n",
        "\n"
      ],
      "metadata": {
        "id": "WALIu6zf0J4H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.subplots(figsize=(7,5))\n",
        "\n",
        "sns.countplot(correct_hb.arrival_date_year, hue=correct_hb['hotel'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "qQBt2kt7zl05",
        "outputId": "367e2640-6d57-44aa-d71f-d487a8208548"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f84cf1b9f50>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 504x360 with 1 Axes>"
            ],
            "image/png": 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+BJX7k+vW3wSOLtnWpcClHdRnAbM2u7OSJHXAN+5IklTCkJQkqYQhKUlSCUNSkqQShqQkSSUMSUmSShiSkiSVMCQlSSphSEqSVMKQlCSphCEpSVIJQ1KSpBL1HAVEkrSF+/PEYY3uQk3tdsEDNd2eZ5KSJJUwJCVJKmFISpJUwpCUJKmEISlJUglDUpKkEoakJEklDElJkkoYkpIklTAkJUkqYUhKklTCkJQkqUTdQjIido2IOyPioYhYFBF/V9QviohlEdFWTIdXrfPtiFgSEY9ExKFV9TFFbUlETKiqD4qIuUV9ekRsU6/jkST1PPU8k1wN/K/M3AsYBZwZEXsVy67MzJZimgVQLDsOGAKMAf4lIpoiogm4GjgM2As4vmo73y+29SngReDUOh6PJKmHqVtIZubyzLyvmH8VWAzssp5VxgLTMvOtzPwTsATYp5iWZOYTmfk2MA0YGxEBHATMKNafAhxRn6ORJPVEXXJPMiIGAnsDc4vSWRGxMCImRUS/orYL8HTVakuLWll9R+ClzFy9Tl2SpJqoe0hGxIeBW4BzMvMV4Bpgd6AFWA5c0QV9GB8R8yNi/ooVK+q9O0lSN1HXkIyI3lQCcmpm/jtAZj6XmWsy813gX6lcTgVYBuxatfqAolZWXwn0jYhe69TfJzOvzczWzGxtbm6uzcFJkrq9ej7dGsB1wOLM/EFVvX9VsyOBB4v5mcBxEfHBiBgEDAbuBeYBg4snWbeh8nDPzMxM4E7gqGL9ccBt9ToeSVLP02vDTTbZF4C/BR6IiLai9h0qT6e2AAk8CXwDIDMXRcRNwENUnow9MzPXAETEWcBsoAmYlJmLiu2dB0yLiEuAP1IJZUmSaqJuIZmZvwWig0Wz1rPOpcClHdRndbReZj7BXy7XSpJUU75xR5KkEoakJEklDElJkkoYkpIklTAkJUkqYUhKklTCkJQkqYQhKUlSCUNSkqQShqQkSSUMSUmSShiSkiSVMCQlSSphSEqSVMKQlCSphCEpSVIJQ1KSpBKdCsmImNOZmiRJ3Umv9S2MiD7AdsBOEdEPiGLRR4Bd6tw3SZIaar0hCXwDOAf4OLCAv4TkK8CP6tgvSZIabr0hmZn/DPxzRJydmT/soj5JkrRF2NCZJACZ+cOI+DwwsHqdzLyhTv2SJKnhOhWSEfFTYHegDVhTlBMwJCVJ3VanQhJoBfbKzKxnZyRJ2pJ09neSDwIfq2dHJEna0nQ2JHcCHoqI2RExs31a3woRsWtE3BkRD0XEooj4u6K+Q0TcHhGPFX/7FfWIiKsiYklELIyIz1Rta1zR/rGIGFdVHxkRDxTrXBUR8f6eSJK0aTp7ufWiTdj2auB/ZeZ9EbE9sCAibgdOBuZk5mURMQGYAJwHHAYMLqZ9gWuAfSNiB+BCKpd8s9jOzMx8sWhzGjAXmAWMAX65CX2VJOl9Ovt06683dsOZuRxYXsy/GhGLqbyAYCxwQNFsCnAXlZAcC9xQ3Pf8Q0T0jYj+RdvbM3MVQBG0YyLiLuAjmfmHon4DcASGpCSpRjr7dOurVM7iALYBegP/nZkf6eT6A4G9qZzx7VwEKMCzwM7F/C7A01WrLS1q66sv7aAuSVJNdPZMcvv2+eK+31hgVGfWjYgPA7cA52TmK9W3DTMzI6LuT8xGxHhgPMBuu+1W791JkrqJjR4FJCt+Dhy6obYR0ZtKQE7NzH8vys8Vl1Ep/j5f1JcBu1atPqCora8+oIN6R32+NjNbM7O1ubl5Q92WJAno/CggX62ajoqIy4A3N7BOANcBizPzB1WLZgLtT6iOA26rqp9UPOU6Cni5uCw7GxgdEf2KJ2FHA7OLZa9ExKhiXydVbUuSpM3W2adbv1w1vxp4ksol1/X5AvC3wAMR0VbUvgNcBtwUEacCTwHHFMtmAYcDS4DXgVMAMnNVRHwXmFe0m9j+EA9wBjAZ2JbKAzs+tCNJqpnO3pM8ZWM3nJm/5S+jhqzr4A7aJ3BmybYmAZM6qM8Hhm5s3yRJ6ozOXm4dEBG3RsTzxXRLRAzY8JqSJG29OvvgzvVU7hl+vJj+X1GTJKnb6mxINmfm9Zm5upgmAz4mKknq1jobkisj4sSIaCqmE4GV9eyYJEmN1tmQ/BqVp1CfpfKquaOovINVkqRuq7M/AZkIjCteKk7x0vHLqYSn1COMPLd7jTF+6/YbbiP1dJ09kxzeHpBQ+e0ilXexSpLUbXU2JD/QPu4jrD2T7OxZqCRJW6XOBt0VwD0RcXPx+Wjg0vp0SZKkLUNn37hzQ0TMBw4qSl/NzIfq1y1Jkhqv05dMi1A0GCVJPcZGD5UlSVJPYUhKklTCkJQkqYQhKUlSCUNSkqQShqQkSSUMSUmSShiSkiSVMCQlSSphSEqSVMKQlCSphCEpSVIJQ1KSpBKGpCRJJeoWkhExKSKej4gHq2oXRcSyiGgrpsOrln07IpZExCMRcWhVfUxRWxIRE6rqgyJiblGfHhHb1OtYJEk9Uz3PJCcDYzqoX5mZLcU0CyAi9gKOA4YU6/xLRDRFRBNwNXAYsBdwfNEW4PvFtj4FvAicWsdjkST1QHULycz8DbCqk83HAtMy863M/BOwBNinmJZk5hOZ+TYwDRgbEQEcBMwo1p8CHFHTA5Ak9XiNuCd5VkQsLC7H9itquwBPV7VZWtTK6jsCL2Xm6nXqkiTVTFeH5DXA7kALsBy4oit2GhHjI2J+RMxfsWJFV+xSktQNdGlIZuZzmbkmM98F/pXK5VSAZcCuVU0HFLWy+kqgb0T0Wqdett9rM7M1M1ubm5trczCSpG6vS0MyIvpXfTwSaH/ydSZwXER8MCIGAYOBe4F5wODiSdZtqDzcMzMzE7gTOKpYfxxwW1ccgySp5+i14SabJiJuBA4AdoqIpcCFwAER0QIk8CTwDYDMXBQRNwEPAauBMzNzTbGds4DZQBMwKTMXFbs4D5gWEZcAfwSuq9exSJJ6prqFZGYe30G5NMgy81Lg0g7qs4BZHdSf4C+XayVJqjnfuCNJUglDUpKkEoakJEklDElJkkrU7cEdbbn+PHFYo7tQU7td8ECjuyCpm/JMUpKkEoakJEklvNwqSRth5Lk3NLoLNXXr9o3uwZbNM0lJkkoYkpIklTAkJUkqYUhKklTCkJQkqYQhKUlSCUNSkqQShqQkSSUMSUmSShiSkiSVMCQlSSphSEqSVMKQlCSphCEpSVIJQ1KSpBKGpCRJJQxJSZJK1C0kI2JSRDwfEQ9W1XaIiNsj4rHib7+iHhFxVUQsiYiFEfGZqnXGFe0fi4hxVfWREfFAsc5VERH1OhZJUs9UzzPJycCYdWoTgDmZORiYU3wGOAwYXEzjgWugEqrAhcC+wD7Ahe3BWrQ5rWq9dfclSdJmqVtIZuZvgFXrlMcCU4r5KcARVfUbsuIPQN+I6A8cCtyemasy80XgdmBMsewjmfmHzEzghqptSZJUE119T3LnzFxezD8L7FzM7wI8XdVuaVFbX31pB3VJkmqmYQ/uFGeA2RX7iojxETE/IuavWLGiK3YpSeoGujoknysulVL8fb6oLwN2rWo3oKitrz6gg3qHMvPazGzNzNbm5ubNPghJUs/Q1SE5E2h/QnUccFtV/aTiKddRwMvFZdnZwOiI6Fc8sDMamF0seyUiRhVPtZ5UtS1JkmqiV702HBE3AgcAO0XEUipPqV4G3BQRpwJPAccUzWcBhwNLgNeBUwAyc1VEfBeYV7SbmJntDwOdQeUJ2m2BXxaTJEk1U7eQzMzjSxYd3EHbBM4s2c4kYFIH9fnA0M3poyRJ6+MbdyRJKmFISpJUwpCUJKmEISlJUglDUpKkEoakJEkl6vYTkO5k5Lk3NLoLNXXr9o3ugSRtHTyTlCSphCEpSVIJQ1KSpBKGpCRJJQxJSZJKGJKSJJUwJCVJKmFISpJUwpCUJKmEISlJUglDUpKkEoakJEklDElJkkoYkpIklTAkJUkqYUhKklTCkJQkqYQhKUlSiYaEZEQ8GREPRERbRMwvajtExO0R8Vjxt19Rj4i4KiKWRMTCiPhM1XbGFe0fi4hxjTgWSVL31cgzyQMzsyUzW4vPE4A5mTkYmFN8BjgMGFxM44FroBKqwIXAvsA+wIXtwSpJUi1sSZdbxwJTivkpwBFV9Ruy4g9A34joDxwK3J6ZqzLzReB2YExXd1qS1H01KiQT+K+IWBAR44vazpm5vJh/Fti5mN8FeLpq3aVFrawuSVJN9GrQfr+Ymcsi4q+A2yPi4eqFmZkRkbXaWRHE4wF22223Wm1WktTNNeRMMjOXFX+fB26lck/xueIyKsXf54vmy4Bdq1YfUNTK6h3t79rMbM3M1ubm5loeiiSpG+vykIyID0XE9u3zwGjgQWAm0P6E6jjgtmJ+JnBS8ZTrKODl4rLsbGB0RPQrHtgZXdQkSaqJRlxu3Rm4NSLa9/+zzPzPiJgH3BQRpwJPAccU7WcBhwNLgNeBUwAyc1VEfBeYV7SbmJmruu4wJEndXZeHZGY+AYzooL4SOLiDegJnlmxrEjCp1n2UJAm2rJ+ASJK0RTEkJUkqYUhKklTCkJQkqYQhKUlSCUNSkqQShqQkSSUMSUmSShiSkiSVMCQlSSphSEqSVMKQlCSphCEpSVIJQ1KSpBKGpCRJJQxJSZJKGJKSJJUwJCVJKmFISpJUwpCUJKmEISlJUglDUpKkEoakJEklDElJkkoYkpIkldjqQzIixkTEIxGxJCImNLo/kqTuY6sOyYhoAq4GDgP2Ao6PiL0a2ytJUnexVYcksA+wJDOfyMy3gWnA2Ab3SZLUTWztIbkL8HTV56VFTZKkzdar0R3oChExHhhffHwtIh5pZH8a7ROwE/BCo/tRMxdGo3uwVfJ7IPB7UPhE2YKtPSSXAbtWfR5Q1N4jM68Fru2qTm3pImJ+ZrY2uh9qLL8HAr8HG7K1X26dBwyOiEERsQ1wHDCzwX2SJHUTW/WZZGaujoizgNlAEzApMxc1uFuSpG5iqw5JgMycBcxqdD+2Ml56Fvg9UIXfg/WIzGx0HyRJ2iJt7fckJUmqG0OyG4iIXSPizoh4KCIWRcTfFfUdIuL2iHis+NuvqO8ZEfdExFsR8a11tvVkRDwQEW0RMb8Rx6NNU+PvQd+ImBERD0fE4oj4XCOOSRuvVt+DiNij+O9A+/RKRJzTqONqFC+3dgMR0R/on5n3RcT2wALgCOBkYFVmXla817ZfZp4XEX9F5XdBRwAvZublVdt6EmjNzO7zu6keosbfgynA3Zn5k+LJ8e0y86WuPiZtvFp+D6q22UTl53X7ZuZTXXUsWwLPJLuBzFyemfcV868Ci6m8eWgsMKVoNoXK/wjIzOczcx7wTgO6qzqp1fcgIj4KfAm4rmj3tgG59ajTfw8OBh7vaQEJhmS3ExEDgb2BucDOmbm8WPQssHMnNpHAf0XEguJNRdoKbeb3YBCwArg+Iv4YET+JiA/Vq6+qnxr896DdccCNNe3cVsKQ7EYi4sPALcA5mflK9bKsXFfvzLX1L2bmZ6iMrHJmRHyp9j1VPdXge9AL+AxwTWbuDfw34DB0W5ka/feA4nL7V4Cba97JrYAh2U1ERG8q/4OYmpn/XpSfK+5PtN+neH5D28nMZcXf54FbqYy0oq1Ejb4HS4GlmTm3+DyDSmhqK1Gr/x4UDgPuy8znat/TLZ8h2Q1ERFC5f7Q4M39QtWgmMK6YHwfctoHtfKi40U9xeW008GDte6x6qNX3IDOfBZ6OiD2K0sHAQzXuruqkVt+DKsfTQy+1gk+3dgsR8UXgbuAB4N2i/B0q9yFuAnYDngKOycxVEfExYD7wkaL9a1QGrd6JytkjVC65/SwzL+2q49DmqdX3IDNfiYgW4CfANsATwCmZ+WJXHo82TY2/Bx8C/gx8MjNf7toj2TIYkpIklfByqyRJJQxJSZJKGJKSJJUwJCVJKmFISpJUwpCUJKmEISltAYr3o+61ies+GRE7dbLtResOi9VBmyM2tS9Sd2NISl2sGHboPZ8z8+uZuaW81eYIKi+X2CJERK9G90E9lyEp1VhE/LwYRWVR+0gqEfFaRFwREfcDn+vg810R0RoRp0fEP1Zt6+SI+FHZdjvZn/Mj4tGI+C2wR1X9tIiYF5zTKeAAAALASURBVBH3R8QtEbFdRHyeysus/7EYaHf3YvrPYt93R8SeJfvZPiL+VLw3lIj4SPvnsm1ExJcjYm4x2sivImLnon5RRPw0In4H/HTj/g1INZSZTk5ONZyAHYq/21J59+2OVEZcOKaqzbqf7wJagWZgSVX9l1RGZulwu8XnJ4GdSvoyksrrybaj8tqxJcC3imU7VrW7BDi7mJ8MHFW1bA4wuJjfF7hjPcd+PXBEMT8euGJ92wD68Zc3f329qv1FVAYL3rbR/z6devbkZQyp9r4ZEUcW87sCg4E1VEZlaLfuZwAyc0VEPBERo4DHgD2B361nuys30Jf9gFsz83WAiJhZtWxoRFwC9AU+DMxed+ViuKXPAzdX3psNwAfXs7+fAP8b+DlwCnDaBrYxAJhejEqxDfCnqm3NzMw3NnB8Ul0ZklINRcQBwP8APpeZr0fEXUAf4M3MXFPVdN3P1aYBxwAPUwm4XM92N8dkKmd990fEycABHbT5APBSZrZ0ZoOZ+buIGFj0tykzH4yIj6xnGz8EfpCZM4t1Lqpa9t+dPRCpXrwnKdXWR4EXiyDbExi1Cdu4FRhLZYiiaZu53d8AR0TEtsUwaF+uWrY9sLy4h3hCVf3VYhlZGaz3TxFxNFSGYYqIERvY5w3Az6hcet3QNj4KLCvmx627IanRDEmptv4T6BURi4HLgD9s7AayMiTVYuATmXnv5mw3M+8DpgP3U7m/Oa9q8f+hMnzS76ictbabBpxbPEyzO5UAPbV4yGgRlQBfn6lU7jVWj0FYto2LqFyGXQC80JljkrqSQ2VJqqmIOAoYm5l/2+i+SJvLe5KSaiYifggcBhze6L5ItWBISt1AROxI5WcW6zo4Mzf0BOym7O984Oh1yjdn5tm13pfUSF5ulSSphA/uSJJUwpCUJKmEISlJUglDUpKkEoakJEkl/j+wBDmVoNyBTAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###From above analysis we can say that in year 2016 most numbers of guests visited the both type of hotels."
      ],
      "metadata": {
        "id": "H6HaP_Zo1ZPB"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**3. Which is the most busy month for hotels ?**\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Uq1loqiOocou"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n",
        "\n",
        "sorted_months = hotels_not_canceled['arrival_date_month'].value_counts().reindex(months)\n",
        "\n",
        "x_label = sorted_months.index\n",
        "y_label = sorted_months/sorted_months.sum()*100\n",
        "\n",
        "## Draw the curve\n",
        "plt.plot(x_label , y_label,'go--',linewidth = 3 , markersize = 7)\n",
        "\n",
        "plt.title('Booking Trend (Monthly)',{'fontsize':15})\n",
        "plt.ylabel('Booking % per months',{'fontsize':15 , 'fontweight':12})\n",
        "plt.xlabel('Month',{'fontsize':15 , 'fontweight':12})\n",
        "\n",
        "plt.xticks(rotation=angle)\n",
        "angle=90\n",
        "plt.rcParams['figure.figsize'] = (10,8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        },
        "id": "KMacdjBjtscK",
        "outputId": "729dbfab-5754-4e68-b5e4-cc7b8e9302a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###From above graph we can say that **August** is the busiest month for hotels,which means august month has most number of bookings.\n"
      ],
      "metadata": {
        "id": "mMkRe4jA8x__"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Now we will check comparatively for both types of hotels which month is busiest.**"
      ],
      "metadata": {
        "id": "AOH9Yugr9TGU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n",
        "\n",
        "## Draw the graph\n",
        "plt.figure(figsize=(20,5))\n",
        "sns.countplot(data = correct_hb, x = 'arrival_date_month', hue = 'hotel', order = months).set_title('Graph showing number of arrival per month',fontsize=20)\n",
        "\n"
      ],
      "metadata": {
        "id": "91qXM-Rpf9nI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "outputId": "cdcd9253-0bfb-418b-ad25-2f9da0f7cd7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Graph showing number of arrival per month')"
            ]
          },
          "metadata": {},
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###From above we can say that **August** is the busiest month for both type of hotels."
      ],
      "metadata": {
        "id": "p1Utpl9p-YAy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**4. Which was the most booked accommodation type (Single, Couple, Family)?**"
      ],
      "metadata": {
        "id": "WLS0NMDzuLn4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select single, couple, multiple adults and family\n",
        "single   = hotels_not_canceled[(hotels_not_canceled.adults==1) & (hotels_not_canceled.children==0) & (hotels_not_canceled.babies==0)]\n",
        "couple   = hotels_not_canceled[(hotels_not_canceled.adults==2) & (hotels_not_canceled.children==0) & (hotels_not_canceled.babies==0)]\n",
        "family   = hotels_not_canceled[hotels_not_canceled.adults + hotels_not_canceled.children + hotels_not_canceled.babies > 2]\n",
        "\n",
        "\n",
        "# Make the list of Category names, and their total percentage\n",
        "x_label = ['Single', 'Couple (No Children)', 'Family / Friends']\n",
        "count = [single.shape[0],couple.shape[0], family.shape[0]]\n",
        "y_label = [x/hotels_not_canceled.shape[0]*100 for x in count]\n",
        "\n",
        "\n",
        "# Draw the curve\n",
        "plt.bar(x_label, y_label)\n",
        "plt.title('Accommodation type',{'fontsize':15})\n",
        "plt.ylabel('Booking % ',{'fontsize':15 , 'fontweight':12})"
      ],
      "metadata": {
        "id": "oujyTz-bf9dV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "outputId": "7af1ef48-5e34-45c5-e25a-017aa9cb07cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Booking % ')"
            ]
          },
          "metadata": {},
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###From above plot bar we can say that **Couples(No Children)**  has most number of booking."
      ],
      "metadata": {
        "id": "la2tNL03yPBr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**5. Which type of meal booked ?** \n",
        "\n",
        "**Categories are presented in standard hospitality meal packages:**\n",
        "\n",
        "* Undefined/SC — no meal package\n",
        "* BB — Bed & Breakfast\n",
        "* HB — Half board (breakfast and one other meal — usually dinner)\n",
        "* FB — Full board (breakfast, lunch and dinner)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "2kipJEG5y2sn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Print diffrent types of meals consumed by guests\n",
        "correct_hb.meal.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j0HsH-Cz4xZY",
        "outputId": "4aced4de-8a1e-43e4-9a8b-43feba3b0d49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BB           92236\n",
              "HB           14458\n",
              "SC           10549\n",
              "Undefined     1169\n",
              "FB             798\n",
              "Name: meal, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Enlarging the pie chart\n",
        "plt.rcParams['figure.figsize'] = 8,8\n",
        "\n",
        "# Indexing labels. tolist() will convert the index to list for easy manipulation\n",
        "labels = correct_hb['meal'].value_counts().index.tolist()\n",
        "\n",
        "sizes = correct_hb['meal'].value_counts().tolist()\n",
        "\n",
        "# As the name suggest, explode will determine how much each section is separated from each other \n",
        "explode = (0.1, 0,0,0,0)\n",
        "\n",
        "# Determine colour of pie chart\n",
        "colors = ['gold', 'aqua','red','yellowgreen']\n",
        "\n",
        "plt.pie(sizes, explode= explode, labels=labels, colors=colors, autopct='%1.1f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "pgSz3jbhf9J5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "83b52023-123e-4c29-f0ff-7aeff90586c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###As we can see from above graph **BB i.e. Bed & Breakfast** is most preferable meal type of the guests which is 77.4%"
      ],
      "metadata": {
        "id": "W6U-lzE05gC3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**6. From which country most guests comes?**"
      ],
      "metadata": {
        "id": "gpzepGYH7MH2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Taking top 10 country according to number of guest, to analyze the largest number quest comes from\n",
        "\n",
        "top_10_countries = correct_hb[correct_hb['is_canceled']==0]['country'].value_counts()[:10]\n",
        "top_10_countries"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s-uJ3l6B18b9",
        "outputId": "67ea77bd-4f18-4184-da8a-37957642a69c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PRT    20977\n",
              "GBR     9668\n",
              "FRA     8468\n",
              "ESP     6383\n",
              "DEU     6067\n",
              "IRL     2542\n",
              "ITA     2428\n",
              "BEL     1868\n",
              "NLD     1716\n",
              "USA     1592\n",
              "Name: country, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Renaming the index\n",
        "\n",
        "top_10_countries = top_10_countries.reset_index().rename(columns = {'index':'country','country':'number_of_bookings'})\n",
        "\n",
        "#Adding percentage column\n",
        "top_10_countries['percentage'] = (top_10_countries['number_of_bookings']/top_10_countries['number_of_bookings'].sum())*100"
      ],
      "metadata": {
        "id": "9ZaYyNOv66Jx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "top_10_countries"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tx1mr8rF-6l6",
        "outputId": "9059922d-17ae-4ef4-bee7-b81fa86723d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PRT    20977\n",
              "GBR     9668\n",
              "FRA     8468\n",
              "ESP     6383\n",
              "DEU     6067\n",
              "IRL     2542\n",
              "ITA     2428\n",
              "BEL     1868\n",
              "NLD     1716\n",
              "USA     1592\n",
              "Name: country, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Draw the graph\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.barplot(x=\"country\", y=\"percentage\", data=top_10_countries).set(title='Percentage of guest per country')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 621
        },
        "id": "sKzEi-qlBCOG",
        "outputId": "e5e6fcb4-0795-44cb-946c-f7ea5b96546a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###From above graph we can say that most of the guests come from **PRT i.e. Portugal**.\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "3KIDfvsPBrH9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**7. Which type of market segment is used for bookings hotels ?**\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "VQxRv4PbCTDZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Print market segment used for booking hotels\n",
        "correct_hb.market_segment.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t2eMlyMfEVC2",
        "outputId": "a59c2ccd-c9b8-47c7-e687-401dd3c425a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Online TA        56408\n",
              "Offline TA/TO    24182\n",
              "Groups           19791\n",
              "Direct           12582\n",
              "Corporate         5282\n",
              "Complementary      728\n",
              "Aviation           235\n",
              "Undefined            2\n",
              "Name: market_segment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Darw the graph\n",
        "plt.title(\"Segments wise booking\")\n",
        "sns.countplot(x = \"market_segment\", data = correct_hb)\n",
        "plt.xticks(rotation = 90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "id": "XvAPYBnfCShW",
        "outputId": "a39a3927-23fe-4cd9-9388-e8756f0dc160"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###From above graph we can say that **online TA** market segment is mostly used to book a hotel."
      ],
      "metadata": {
        "id": "-MD55OnTFRkn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**8. How much guests pay per night for each types of rooms ?**\n",
        "\n"
      ],
      "metadata": {
        "id": "MkliPgHMFsvq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Not canceled booking of hotels\n",
        "hotels_not_canceled = correct_hb[correct_hb['is_canceled'] == 0]"
      ],
      "metadata": {
        "id": "aH0G6y-SH3Be"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#ploting box plot for analysis\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "sns.boxplot(x='reserved_room_type', y ='adr',data = hotels_not_canceled,hue='hotel')\n",
        "plt.title('Price per night for each types of rooms',fontsize=20)\n",
        "plt.xlabel('Room Type',fontsize=15)\n",
        "plt.ylabel('Price',fontsize=15)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "1o7gilqMHNub",
        "outputId": "db37768f-1111-4e16-c6a8-7034766761a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ],
            "image/png": 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pP81svby+oMfKu0nqDEn/Lu+u+Wxsl9dP9nH/K/K+8oYtGyHph865h+IbOm/M4Mvl/VN+wszq5P1z7CsvsZwsaYe8MW6Lgn8j42fkDWG4wsx+LS+RHCuvVeo+eS2qqW5Wy8Xf5fUt/oSZHZA3yoWTdIdz7rluxr7bP9/3SHrQzO6R1895gqRz5XXP+Owhxh2v64CZfVTeuMl/8G/+3CQvoThe3nV1orzrYq9/XhfKGwd7i5n9Tt4IEFPltbA+oLdaG+N1PGneJCk/lvQ3f5+n5fVRPkNed56EffJotbz3ZqW81s8D8m4s7nx9OzP7saTv+IsWd3HMOknf9j+4Paa3xsHeJ+nypBsg/0den/Qr5I0Xv0be9XKMvN/n98vrA/6kv/1vJO02sz/JG0nF5P1+nSHvBuKGLF7zrfKu7YskPWZm98q7gfASv95vOecOuvk3n/xzGpXXR/9u/z1/St7NjP8mb7SQmqRzdbW81/pF/2bqB/TWONhD5A2NmO3ft1xNk/eB5BF5f9tekXcT5EXy/kb8b4HqRW8T9jAmPHiE/VCacbDTbHujUgz51mn9SKUYB9tfN0jeP9jN8pKaXfL+2X5PSWPhyktkvihv9I/4uLDPy0siviQpkmW8Tf7jCHmTUDT7x9oqr6XI0uw3Rt5X1c/5278q6XF5X6efnbTt2mzPX5pzvzbNuiVKGtpOGYbKkzeRR71/XnfJS1DeJ69V3ilp7PBc6/aXn+G/B2/I+2ec9lrI5nhJx/2NvA8vb/rv9Y8kHZvrsbKI5RhJt/jv5155o6Q8LW9oyk8qcTzlcklf9q/TFnkJ/x3yhj9LG4d/3n8tL3l5U95ID3WSLs7md6U715X/uu6U92G0LcN1cpS/vkVpfo86X2f+a2mQ9+Fgl7yJes5Is5/JG5lktbzfmTfl/c6tl3SNpOM7bXuF/54/678Pr8rrbvF15TAWs7zx8a/x388WP8b1kv49H+c122tOXkJ9h7wP9Af8519Iemea7Y+U9E3/2tsvr4tNvaRzM70fKdZ1dR0l/J7La6T4jrwRSXb4dTfJu/7/X7rXx4NHrg9zLqz7kgAUmvlTuDvnRoYbSXjM7I/yku8jXNfDJKLEmTe1/QOSfuGc+88utpnrnLsxsOAAlAz6YAPo8cxsYKobpsxsurybHFeRXMP3df/5+6FGAaCk0QcbQCk4QV6f33p5N5+Vy5uoYpK8r56/EmJsCJmZjZH0YXl928+T9Hvn3J/DjQpAKSPBBlAKXpY3MsFZ8m6i6yevv/DPJd3snHsmxNgQvgmS5svrR32PkkYGAYB8ow82AAAAkEf0wQYAAADyqOS6iBx99NFu5MiRYYcBAACAErdx48adzrlhyctLLsEeOXKkNmzYEHYYAAAAKHFmlnKiMbqIAAAAAHlEgg0AAADkEQk2AAAAkEcl1wcbAACglBw4cEDbtm3Tvn37wg6l1+rfv7+OO+449e3bN6vtSbABAACK2LZt2zRkyBCNHDlSZhZ2OL2Oc06xWEzbtm3TqFGjstqHLiIAAABFbN++fYpEIiTXITEzRSKRnL5BIMEGAAAociTX4cr1/JNgAwAAICdNTU067bTTst5+yZIlevHFF7vcbvr06Vq+fPmhhFYUSLABAABQUNkm2KWCBBsAAAA5a2tr03/9139p9OjROvfcc9XS0qJNmzbpzDPP1NixY/WRj3xEr732mpYvX64NGzbosssu0/jx49XS0qKNGzfqrLPO0oQJE/TBD35Q27dvD/vl5BUJNgAAAHL29NNP6wtf+IKeeOIJHXnkkfr1r3+tmpoaffOb39TmzZs1ZswYzZ07VxdffLEmTpyoZcuWadOmTSovL9fMmTO1fPlybdy4UZdffrmuvfbasF9OXjFMHwAAAHI2atQojR8/XpI0YcIEPfPMM3r99dd11llnSZKi0aguueSSg/b7+9//rscff1zTpk2T5LWEjxgxIrjAA0CCDQAAgJz169ev4+eysjK9/vrrWe3nnNPo0aP1yCOPFCq00NFFBAAAAIfsiCOO0FFHHaV169ZJku64446O1uwhQ4Zo165dkqR3vvOd2rFjR0eCfeDAAT3xxBPhBF0gJNgoCbFYTLNmzVIsFgs7FAAAeq3a2lp97Wtf09ixY7Vp0yZdf/31krzh96644gqNHz9ebW1tWr58ua666iqNGzdO48eP18MPPxxy5PllzrmwY8iriRMnug0bNoQdRq8Vi8U0d+5c3XDDDYpEIoHV+53vfEcrV67UhRdeqNmzZwdWLwAAhbZ161adeuqpYYfR66V6H8xso3NuYvK2tGAjr2pra7VlyxYtXbo0sDpjsZjq6urknFNdXR2t2AAAIFQk2MibsBLd2tpatbe3S/LuRA4yuQcAAEhGgo28CSvRbWhoUGtrqySptbVV9fX1gdQLAACQCgk28iasRLeqqkrl5d6Ik+Xl5R3jagIAAISBBBt5E1aiG41G1aePdymXlZWppqYmkHoBAABSIcFG3oSV6EYiEVVXV8vMVF1dHejoJQAAAMkCT7DNrMnMtpjZJjPb4C8bamb1Zva0/3yUv9zMbKGZNZrZZjM7Peh4kb0wE91oNKoxY8bQeg0AQAGUlZVp/PjxOu2003TBBRdkPWtjdzQ1NenOO+9Mu+60005LWHbjjTfq1ltvzXjMJUuW6MUXX+yy7unTp2v58uXZB5tGWFOlT3XO7exUvlrSaufcLWZ2tV++StJ5kk72H++V9CP/GUUqGo2qqakp8EQ3Eolo4cKFgdYJAEAYvvjlr+mVna/m7XjHHD1U3//O/2bcZsCAAdq0aZMk73/9D37wA1177bV5iyGutbW1I8H+j//4j7wdd8mSJTrttNN07LHH5u2YmYSVYCe7SNIU/+daSWvlJdgXSVrqvNlw/mRmR5rZCOfc9lCiRJdIdAEAKKxXdr6qZ4aflb8DvvxgTpu/733v0+bNmyVJzzzzjL7whS9ox44dGjhwoH7yk5/olFNO0T333KO5c+eqrKxMRxxxhB566CHt27dPn/vc57RhwwaVl5frO9/5jqZOnaolS5bo//7v/7R79261tbVp//792rp1q8aPH69oNJrTBHKbNm3SFVdcob179+qkk07S7bffrtWrV2vDhg267LLLNGDAAD3yyCN68skn9eUvf1m7d+/W0UcfrSVLlmjEiBE5nYdMwkiwnaRVZuYk3eacWyxpeKek+SVJw/2fKyS90Gnfbf6yhATbzGZImiFJJ5xwQgFDBwAA6L3a2tq0evVqffrTn5YkzZgxQz/+8Y918skn689//rM+//nPa82aNbrpppt0//33q6KioqM7yQ9+8AOZmbZs2aKnnnpK5557rv7xj39Ikh599FFt3rxZQ4cO1dq1a3Xrrbfq97//fcoYnnnmGY0fP76j/NJLL+mrX/2qJKmmpkaLFi3SWWedpeuvv15z587V9773PX3/+9/XrbfeqokTJ+rAgQOaOXOmfve732nYsGG6++67de211+r222/P23kKI8Ge5JxrNrNjJNWb2VOdVzrnnJ98Z81P0hdL3lTp+QsVAAAALS0tGj9+vJqbm3Xqqadq2rRp2r17tx5++GFdcsklHdvt379fkvT+979f06dP16WXXqqPfvSjkqT169dr5syZkqRTTjlFb3/72zsS7GnTpmno0KFZxXLSSSd1dFeRvD7YkvTGG2/o9ddf11lnea370Wg0Iba4v//973r88cc7Rjtra2vLa+u1FEKC7Zxr9p9fMbPfSHqPpJfjXT/MbISkV/zNmyUd32n34/xlAAAACEi8D/bevXv1wQ9+UD/4wQ80ffp0HXnkkQnJbtyPf/xj/fnPf9Yf/vAHTZgwQRs3bsx4/EGDBhUq9IM45zR69Gg98sgjBasj0FFEzGyQmQ2J/yzpXEmPS1ohKepvFpX0O//nFZJq/NFEzpT0Bv2vAQAAwjFw4EAtXLhQ3/72tzVw4ECNGjVK99xzjyQvcX3ssccked043vve9+qmm27SsGHD9MILL2jy5MlatmyZJOkf//iHnn/+eb3zne88qI4hQ4Zo165dOcd2xBFH6KijjtK6deskSXfccUdHa3bnY77zne/Ujh07OhLsAwcO6Iknnsi5vkyCbsEeLuk3Zhav+07nXJ2Z/VXSr8zs05Kek3Spv/29ks6X1Chpr6RPBRwvAAAAOnn3u9+tsWPH6pe//KWWLVumz33uc5o3b54OHDigT3ziExo3bpy+9rWv6emnn5ZzTuecc47GjRunU045RZ/73Oc0ZswYlZeXa8mSJerXr99Bxx87dqzKyso0btw4TZ8+PaebHGtraztucjzxxBP185//XJI3/N4VV1zRcZPj8uXLNWvWLL3xxhtqbW3Vl770JY0ePTpv58i8ATpKx8SJE92GDRvCDgMAACAvtm7dqlNPPbWjHMYwfTj4fZAkM9vonJuYvG2xDNMHAACALJAMFz+mSgcAAADyiAQbAAAAyCMSbAAAACCPSLABAACAPCLBBgAAAPKIBBsAAAAZvfTSS/rEJz6hk046SRMmTND555+vf/zjH3rxxRd18cUXS5I2bdqke++9N6fjLlmyRF/84hcTlk2ZMkVdDbk8f/78rI4/cuRI7dy5M6eY8oFh+gAAAHqQa77yRb2x8+W8He+Io4dr/re/n3a9c04f+chHFI1Gddddd0mSHnvsMb388st6xzveoeXLl0vyEuwNGzbo/PPPz1ts6cyfP1/XXHNNwevpLhJsAACAHuSNnS/rqpOeytvxvvlM5vUPPPCA+vbtqyuuuKJj2bhx4yRJTU1N+vCHP6xHH31U119/vVpaWrR+/XrNmTNH1113nR5++GENGzZM7e3tesc73qFHHnlEw4YNyzq2X/7yl5o/f76cc/rQhz6kb37zm7r66qvV0tKi8ePHa/To0Vq2bJl+8YtfaOHChXrzzTf13ve+Vz/84Q9VVlbWrfORD3QRAQAAQFqPP/64JkyYkHGbww47TDfddJM+/vGPa9OmTfr4xz+uT37yk1q2bJkkqaGhQePGjUuZXN99990aP358xyPePeTFF1/UVVddpTVr1mjTpk3661//qt/+9re65ZZbNGDAAG3atEnLli3T1q1bdffdd+uPf/yjNm3apLKyso56w0KCDQAAgLy7/PLLtXTpUknS7bffrk996lMpt4sn5fHHxInezON//etfNWXKFA0bNkzl5eW67LLL9NBDDx20/+rVq7Vx40adccYZGj9+vFavXq1nn322cC8sC3QRAQAAQFqjR4/u6Gedi+OPP17Dhw/XmjVr9Je//KVgrcrOOUWjUX3jG98oyPG7gxZsAAAApHX22Wdr//79Wrx4cceyzZs3a926dQnbDRkyRLt27UpY9pnPfEaf/OQndckll+TcJ/o973mPHnzwQe3cuVNtbW365S9/qbPOOkuS1LdvXx04cECSdM4552j58uV65ZVXJEmvvvqqnnvuuZxfZz6RYAMAACAtM9NvfvMbNTQ06KSTTtLo0aM1Z84cve1tb0vYburUqXryySc1fvx43X333ZKkCy+8ULt3707bPSSTESNG6JZbbtHUqVM1btw4TZgwQRdddJEkacaMGRo7dqwuu+wyvetd79K8efN07rnnauzYsZo2bZq2b99+6C/8EJhzLtQA8m3ixImuq7ETAQAAeoqtW7fq1FNP7SgHPUzfodiwYYNmz559UGt3T5T8PkiSmW10zk1M3pY+2AAAAD1IoZLhfLvlllv0ox/9KPQRPcJAFxEAAADk3dVXX63nnntOkyZNCjuUwJFgAwAAAHlEgg0AAFDkSu2euZ4m1/NPgg0AAFDE+vfvr1gsRpIdEuecYrGY+vfvn/U+3OQIAABQxI477jht27ZNO3bsCDuUXqt///467rjjst6eBBsAAKCI9e3bV6NGjQo7DOSALiIAAABAHpFgAwAAAHlEgg0AAADkEQk2AAAAkEck2AAAAEAekWADALotFotp1qxZisViYYcCAEWDBBsA0G21tbXasmWLli5dGnYoAFA0SLABAN0Si8VUV1cn55zq6upoxQYAHwk2AKBbamtr1d7eLklqa2ujFRsAfCTYAIBuaWhoUGtrqySptbVV9fX1IUcEAMWBBBsA0C1VVVUqL+2Eg4AAACAASURBVC+XJJWXl2vatGkhRwQAxYEEGwDQLdFoVH36eP9GysrKVFNTE3JEAFAcSLABAN0SiURUXV0tM1N1dbUikUjYIQFAUSgPOwAAQM8VjUbV1NRE6zUAdEKCDQDotkgkooULF4YdBgAUFbqIAAAAAHlEgg0AAADkEQk2AAAAkEck2AAAAEAekWADAAAAeUSCDQAAAOQRCTYAAACQRyTYAAAAQB6RYAMAAAB5RIINAAAA5BEJNoCiFovFNGvWLMVisbBDAQAgKyTYAIpabW2ttmzZoqVLl4YdCgAAWSHBBlC0YrGY6urq5JxTXV0drdgAgB6BBBtA0aqtrVV7e7skqa2tjVZsAECPQIINoGg1NDSotbVVktTa2qr6+vqQIwIAoGsk2ACKVlVVlcrLyyVJ5eXlmjZtWsgRAQDQNRJsAEUrGo2qTx/vz1RZWZlqampCjggAgK6RYAMoWpFIRNXV1TIzVVdXKxKJhB0SAABdKg87AADIJBqNqqmpidZrAECPQYINoKhFIhEtXLgw7DAAAMgaXUQAAACAPCLBBgAAAPKIBBsAAADIIxJsAAAAII9IsAEAAIA8IsEGAAAA8ogEGwAAAMgjEmwAAAAgj0iwAQDAQWKxmGbNmqVYLBZ2KECPQ4INAAAOUltbqy1btmjp0qVhhwL0OKEk2GZWZmZ/M7Pf++VRZvZnM2s0s7vN7DB/eT+/3OivHxlGvAAA9CaxWEx1dXVyzqmuro5WbCBHYbVgXylpa6fyNyV91zlXKek1SZ/2l39a0mv+8u/62wEAgAKqra1Ve3u7JKmtrY1WbCBHgSfYZnacpA9J+qlfNklnS1rub1Ir6d/8ny/yy/LXn+NvDwAACqShoUGtra2SpNbWVtXX14ccEdCzhNGC/T1JX5fU7pcjkl53zrX65W2SKvyfKyS9IEn++jf87QEAQIFUVVWpvLxcklReXq5p06aFHBHQswSaYJvZhyW94pzbmOfjzjCzDWa2YceOHfk8NAAAvU40GlWfPl6KUFZWppqampAjAnqW8oDre7+kC83sfEn9JR0uaYGkI82s3G+lPk5Ss799s6TjJW0zs3JJR0g66E4L59xiSYslaeLEia7grwIAgBKwaNEiNTY2plwX75E5ePBg3XTTTQetr6ys1MyZMwsaH9BTBdqC7Zyb45w7zjk3UtInJK1xzl0m6QFJF/ubRSX9zv95hV+Wv36Nc44EGuhFGIsXCEefPn3Up08fDR8+POxQgB4n6BbsdK6SdJeZzZP0N0k/85f/TNIdZtYo6VV5STmAXqTzWLyzZ88OOxygpGRqgb7yyislSQsWLAgqHKBkhJZgO+fWSlrr//yspPek2GafpEsCDQxA0Ugei7empkaRCPc5AwCKGzM5AihajMULAOiJSLABFC3G4gUA9EQk2ACKVlVVVcdIBmbGWLwAgB6BBBtA0brwwgsVHzjIOacLLrgg5IgAAOgaCTaAorVixYqEFuyVK1eGHBEAAF0jwQZQtBoaGhJasOmDDQDoCUiwARStqqoqlZd7o4mWl5fTBxsA0COQYAMoWtFoVH36eH+mysrKVFNTE3JEAAB0jQQbQNGKRCKaOnWqJGnKlClMMgMA6BFIsAEUtXgfbAAAegoSbKAXicVimjVrlmKxWNihZCUWi2nt2rWSpLVr1/aYuAEAvRsJNtCL1NbWasuWLT1mynGmSgcA9EQk2EAvEYvFVFdXJ+ec6urqekRrcFhTpfe0ln4AQHEhwQZ6iZ7YGhzWVOk9raUfAFBcSLCBXiKs1uBDEcZU6T2xpR8AUFxIsIFeoidO2hLGVOk9saUf2aP7D4AgkGADvURPnLQljKnSe2JLP7K3ePFibd68WYsXLw47FAAljAQb6CUikYiqq6tlZqquru4Rk7aE0ereE1v6kZ1YLNbxgam+vp5WbAAFQ4KNksDXvtmJRqMaM2ZMj2i9lsJpde+JLf3IzuLFizu6/7S3t9OKDaBgysMOoFgsWrRIjY2NKdc1NzdLkioqKtLuX1lZqZkzZxYkNnSt86gPs2fPDjucohWJRLRw4cKww8havNV95cqVgbW6h1EngrF69eqDynPmzAkpGgCljBbsLLS0tKilpSXsMJAGoz6UtjBa3XtaSz+yE+/Pn64MAPlCC7YvU+vzlVdeKUlasGBBUOEgB6lGfaAVu3SE0ere01r6kZ1zzjlHq1at6ihXVVWFGA2AUkYLNno8Rn0obWvWrNGUKVP0wAMPhB0KerjPfvazHf3r+/TpoxkzZoQcEYBSRYKNHo9RH0rb/PnzJUk333xzyJGgp4tEIh2t1tOmTaN/PYCCIcFGj8eoD6VrzZo1Cd9O0IqNQ/XZz35WY8eOpfUaQEGRYKPH64njOyM78dbrOFqxcaji/ev5OwGgkLjJESUhGo2qqamJ1usSE2+9TlcGAKAYkWCjJDDqQ2kys4Sh1MwsxGgAAMgOXUQAFK2+fftmLAMAUIxowQZCEIvFNHfuXN1www29vi9opllUhwwZkjBx0JAhQzrGpY9jFlUP11R2wjpPvD9A70ILNhCCzlO7I73hw4dnLOMtXFPZWbx4sTZv3qzFixcHWi/vD9C70IINBCx5aveamppe3aLVVevzxz72McViMV100UXM0JkG11R2YrFYx0RU9fX1mjFjRiDnifcH6H1owQYClmpqd6Q3fPhwDRo0iBFiMuCays7ixYs7zlN7e3tgrdi8P0DvQ4INBIyp3XPTt29fVVZW0uKXAddUdlavXp2xXCi8P0DvQ4INBIyp3ZFvXFPZ6TzkY6pyofD+AL0PCTYQMKZ2R75xTWXnnHPOSShXVVUFUi/vD9D7cJMjELBIJKKpU6fq/vvv15QpU+j6EJJMwwM2NzdLkioqKtLuX0zDA0YiEVVXV2vlypWqrq7mmkrj0ksv1apVqzrKl1xySSD18v4AvQ8t2EAIgvpqGt3T0tKilpaWsMPIyZAhQ+Sc0xFHHBF2KEVrxYoVCeWVK1cGVnc0GtWYMWNovQZ6CVqwgYDFYjGtXbtWkrR27drAhgpDokytz/HJbBYsWBBUOIds2bJlkqSlS5fq8ssvDzma4tTQ0JBQrq+vD2zox0gkooULFwZSF4Dw0YKNvIrFYpo1a1bC7HtIxJBdyLd4ch131113hRRJceNmQwBBIcFGXjFbWdcYsgv59pOf/CSh/OMf/zikSIpbmDcb0vgA9C4k2Mib5NnK+EeSGq1oQDjiNxhLCvwGYxofgN6FBBt5Q9eH7DBkFxCeMG4wpvEB6H1IsJE3dH3ITnzILjNjyC7kxWWXXZZQ5kNbask3GAeV6NL4APQ+JNjIG7o+ZI8hu5BPu3btSii/8cYbIUVS3Gpra9XW1ibJawQIKtGl8QHofUiwkTd0fchefMguWq+RD50nT5Gk+++/P6RIiltDQ0NHgt3W1hZYojt58uSMZQClhwQbeUPXByAcZpaxDM+kSZMSykElukwsBfQ+JNjIK7o+AMFLnnWyp81CGZSwPnisX78+obxu3bpQ4gAQHBJs5BVdH7KzZs0aTZkyRQ888EDYoQC9xkMPPZSxXChVVVUqKyuT5HWf4/4UoPSRYAMhmD9/viTp5ptvDjkSlIIRI0YklI899tiQIiluyR/8g2oIiEajHQl2eXk53/ABvQAJNhCwNWvWJIwoQCs2DtVXvvKVjGV4tm/fnrFcKNyfAvQ+JNhAwOKt13G0YuNQhdX1oacJ82ZQ7k8BehcSbCBg8dbrdGUgVw0NDQllxllO7ZxzzslYLiTuTwF6FxJsIGDxyXjSlYFcMclTdmbMmNExVn+fPn00Y8aMwOqOxWKaNWsW06QDvQQJNhCwa665JqF87bXXhhQJSgWTPGUnEolo6NChkqShQ4cG2ppcW1urLVu2ME060EuQYAMBO/vssxNaG6dOnRpyROjpIpGIpkyZIkmaMmUK3RDSiMVi2rlzpyRp586dgbUmx2Ix3XfffXLO6b777qMVG+gFSLCBEMycOVOSdOWVV4YcCUrFm2++KUnav39/yJEUr1tvvTVjuVBqa2s77rU4cOAArdhAL0CCDYTgySeflCQ9/vjjgdZLP9DSFIvFOkYOeeihh3h/03jkkUcylgulvr6+Y7p055xWrVoVSL0AwkOCDQQsFot1jPJQX18faDJEP9DSdNttt6m9vV2S1N7ersWLF4ccETobPnx4xjKA0kOCDQRs8eLFoSRDsVhMdXV1cs6prq6OVs4Ssnr16oRy8rB98AwaNChjuVBeeumljGUApYcEGwhYcjKUXC6U2trajsS+ra2NVuwSEuYEKj3J3LlzE8o33XRTIPW+7W1vy1gGUHpIsIGAxftipisXSkNDQ8IU7UxGUjrCnEClJ5k4cWJHq/WgQYM0YcKEQOp9+eWXM5YBlB5muAACNm7cOD366KMd5fHjxwdSb1VVle699161trYyGUmJmTFjhurr69Xe3h74BCrFaNGiRWpsbEy5rqysTJLXDzrdKD6VlZUdI/3kw7Rp07RixYqO8rnnnpu3YwMoTrRgAwF76qmnEspbt24NpF4mIyldkUhEAwYMkCQNGDCAcbAzaGtr06BBgzRkyJDA6rzwwgsTyhdccEFgdQMIBy3YyKvp06erqalJlZWV+ulPfxp2OEVp7969GcuFEolEVF1drZUrV6q6upokrIQ0NjZqz549kqQ9e/aosbFRlZWVIUcVnkytz/FW6wULFgQVjn71q18llO+55x7NmTMnsPoBBI8WbORVU1OTJKX9ehbhikajGjNmDK3XJWbevHkZywgXo7wAvQ8JNvJm+vTpCeXPfOYz4QSCtCKRiBYuXEjrdYmJf7BNVwYABIsEG3mT/E89yFbsNWvWaMqUKXrggQcCqxMoFscdd1zGMsI1YsSIhPKxxx4bUiQAghJogm1m/c3sL2b2mJk9YWZz/eWjzOzPZtZoZneb2WH+8n5+udFfPzLIeNFzzJ8/X5J08803hxxJ1+I3GqYrA7lK7m/dm/tfF6PkSZ127twZUiQAghL0f/b9ks52zo2TNF5StZmdKembkr7rnKuU9JqkT/vbf1rSa/7y7/rbAQnWrFmTML5zsbdiJ0+TzKQTOFR/+ctfMpYRruQhMRmmDyh9gSbYzrPbL/b1H07S2ZKW+8trJf2b//NFfln++nOMKcqK1siRIxPKQbWixVuv44q9FfuVV15JKDPpBA7V5MmTM5YRrmg0qr59+0qSDjvsMG4yBnqBwL+bNrMyM9sk6RVJ9ZKekfS6c67V32SbpAr/5wpJL0iSv/4NSQfdnWVmM8xsg5lt2LFjR6FfAtJYsmRJQjmoYfrirdfpysUmPl15ujKQq6BmA0X3RCIRnXfeeTIznXfeedxkDPQCgSfYzrk259x4ScdJeo+kU/JwzMXOuYnOuYnDhg075BjRPck3NQZ1k2N5eXnGMt4Si8U0a9asg/qEomd78MEHE8pr164NJxCkxRCZQO8SWibinHvdzB6Q9D5JR5pZud9KfZykZn+zZknHS9pmZuWSjpBEZlCkrr/++oPKd955Z8Hrveaaa3TTTTd1lK+99tqC13kohg0bltBN5Jhjjgms7traWm3ZskVLly7V7NmzA6sXhdXTvsUpVZmmaG9u9v6tdf5b1Vm+p2cHEK6gRxEZZmZH+j8PkDRN0lZJD0i62N8sKul3/s8r/LL89Wsc34UWrRdffDFjuVDOPvvsjlbr8vJyTZ06NZB6uyusPtixWEx1dXVyzqmuro5W7BLS1taWsYzwtbS0qKWlJewwAAQk6BbsEZJqzaxMXnL/K+fc783sSUl3mdk8SX+T9DN/+59JusPMGiW9KukTAceLHiLeil3srddhqq2t7ejv3dbWRis2kGfFNkU7gPAEmmA75zZLeneK5c/K64+dvHyfpEsCCA15MGLECG3fvr2jHORkCmeffbbOPvvswOrriRoaGhKGM6yvryfBLhFDhw7Vq6++mlAGAISHGS6QN//zP/+TUE7X1xDhqKqqSuhKkzw2L3qu+BBw6coAgGCRYCNvjjrqqIxleJKnsT7++OMDqTcajXZ0EWlvb2c0gxKS3I+fsdUBIFwk2MibxYsXZywXUmNjoz70oQ8FNjTgobjxxhsTyjfccEM4gaBkJM+/xXxcABAuEmzkzerVqzOWC2nevHnas2eP5s2bF1id3RVWS39tbW1H4mVmWrp0aSD1ovBOP/30jGUAQLBIsJE3ySMoBjWiYmNjo5qamiRJTU1NRd+KXVtbm1AOKtFtaGjoGL6tra1N9fX1gdSLwnvhhRcSytu2bQspEgCARIKNPJo0aVJCefLkyYHUm9xqXeyt2KtWrUoo33///YHUy02OpSussdUBAKmRYCNv+vfvn1Du169fIPXGW6/TlYtNWVlZxnKhRKNR9enTp6NObnIEAKAwSLCRN+vWrctYLpSRI0dmLBebPXv2ZCwXSiQSUXV1tcxM1dXVikQigdQLAEBvQ4KNvKmqqupojS0rKwusC8J1112XsVxswhzxIRqNasyYMbReAwBQQCTYyJtoNJpwE11QSVxlZWVHq/XIkSNVWVkZSL3ddeaZZ2YsF1IkEtHChQtpvQYAoIBIsFESrrvuOg0aNKjoW68laciQIQnlww8/PKRIAABAIZBgI2/mz5+fUP7GN74RWN2VlZX6wx/+UPSt15K0fv36hHJQfdUBAEAwSLCRNxs3bkwob9iwIaRIiltYwxkCAIBglIcdANDbvPTSSxnLQDFZtGhRxsmbmpubJUkVFRUp11dWVmrmzJkFiQ0AihUJNhCwzZs3J5Qfe+yxkCIBDl1LS0vYIQBA0SHBBgCk1VXr85VXXilJWrBgQRDhAECPQB9soBeJxWKaNWuWYrFY2KEAAFCyck6wzWysmd1tZs+Y2X4zO91ffrOZnZf/EAHkS21trbZs2aKlS5eGHQoAACUrpwTbT6A3SnqbpKWS+nZavV8Sd7IARSoWi6murk7OOdXV1dGKDQAoeWF9c5trC/Y3JC1xzp0l6eakdZskjc9LVADyrra2Vu3t7ZK8mTZpxQYAlLqwvrnNNcE+RdLd/s8uad2/JA095IgAFERDQ4NaW1slSa2traqvrw85IgAACifMb25zTbBfkXRimnWjJT1/aOGglJhZ2CGgk6qqKpWXewMHlZeXa9q0aSFHBABA4YT5zW2uCfZdkm4ys85T0Tkze4ekqyQty1tk6PGcS/6So3AYHaNr0Wi04w9Ne3u7ampqQo4I+RL/4JSuDAC9UZjf3OaaYP+3pA2SHtRbrdW/k/S4pM2S5ucvNCB7jI6B3iz+DyRdGQB6ozC/uc0pwXbO7XfOfVjSuZJqJf1U0p2SPuSc+7Bz7kABYgQyYnSM7NTW1nZ02zEzPowAAEpaNBpVnz5eqltWVhboN7fdmmjGObfaOXeNc26Gc+5q5xx3SyE0jI6RnYaGBrW1tUnyzhM3OQIASlkkElF1dbXMTNXV1YpEIoHVnes42J8ws6+lWfdVM7s0P2EB2WN0jOxUVVUltGBzkyMAoNRFo1GNGTMm8PuOcm3BvlrSvjTr9kqac2jhALkjcczOhRde2HHjqXNOF1xwQcgRIV8GDBiQsQwAvVUkEtHChQsDbb2Wck+wT5Z3Q2MqW/31QKBIHLNzzz33ZCyj52ppaclYBgAEK9exnPZKOi7NuuPlTZcOBGrFihUJ5ZUrV2r27NkhRVO8Vq9efVB5zpzS/tJp0aJFamxszHm/+D5XXnllt+qtrKzUzJkzu7UvAKDnyzXBbpD032Z2v3PulfhCMxsm6VpJq/IZHJCN+++/P6FcV1dHgp1C8rjkQY5THpbGxkY9/cTfdMLgtpz2O+yA9+Xe/uc25Fzn87vLct4HAFAYsVhMc+fO1Q033BBoN5FcE+yrJP1J0jNmVidpu6QRkj4o6XVJX89veEDXGAM4O5MmTdKDDz7YUZ48eXKI0QTnhMFtuub0fwVW3/xHDw+sLgBAZp3nyQiy8S3XcbCflzRO0vfldQk5z39eJOl059wLeY8Q6EJ86Ll0ZXiYuh4A0JuEOU9GzuNgO+d2OOfmOOfOdM6d7D9f65zbWYgAAeTHQw89lLEMAEApCXOejG5NNAMUk2OOOSZjGZ74H5l0ZQAASkmY82R0mWCb2V/M7F3+z3/1y2kfhQ8ZSDR//vyMZQAA0PtUVVWpvNy73bC8vDzQeTKyucnxCUktnX4u/aEH0KNUVlaqf//+2rdvn/r376/KysqwQwIAACGLRqOqq6uTJJWVlQU6m2OXCbZz7lOdfp5e0GiAbojFYh03Nra1tSkWiwU+YxMAACgukUhE1dXVWrlypaqrq4tzmD4z6y/pDUkfd879tnAhAbmpra1NmMkx6KF4gKDkMnFO8iQ5TH4DoDeKRqNqamoKtPVayuEmR+fcPkmvSGKQYRSVMG9iAAAASJbrRDO3SZrlz+R4oBABAbmaPHlywmyOvWUCFfQ+6VqgGxsb9ZnPfKaj/NOf/pR7EQBA4U00k2uCfaSk0yQ1mdlqSS8r8aZH55y7Kl/BAdnoDVN+A5l0TqYHDBhAcg0AOniimZqamsD6Yec6DvbHJO2X9KakyZIulnRJ0gMI1Pr16xPK69atCykSIDwnn3yy+vTpo0WLFoUdCgAUhaKfaMbMBpjZx+RNkX6dpDOdc6NSPE4saLRACpMmTUoo00UktZEjRyaUR40aFU4gKIiBAwdqzJgxtF4DgK/YJ5o5Ud741/dI+l9Jd0h6yszOLXBsQFbefPPNhPL+/ftDiqS4NTU1JZT/+c9/hhMIAAABCHOimWxasL8lqV1el5CBkkZL2iTvhkcgdMldQugiAgAAotGo+vTxUt2im2hG0vskfcU590e/vNXMPus/j3DObS9ceEDXzCxjOSyMWQwAQHiKfaKZEZKeTVr2jCST9DZJJNgI1aRJk7R27dqEcjErKyvrmHkyXgaAQsjlg36y+H7JDQDZoJEAxSKsiWayHaaPcdBQtIp1mL5sxyy+7bbbuDENQEE0NjZq0+Nb1TZwaM779nnT+9u68dmXc9qvbO+rOdcFFEokEtHChQsDrzfbBPt+M0s1g+Pq5OXOuWMOPSwgez1tmL7KysqOVuyhQ4eSXAMoqLaBQ9VyyvmB1TfgqXsDqwsoVtkk2HMLHgVwCDp3t0hVLkYnnniinnnmGX3rW98KOxQAAJBnXSbYzjkSbCDPGLMYAIDCi8Vimjt3rm644YZAb3LMdSZHAAAAoEeora3Vli1bAp3FUcq+DzaAHoLhAQEA8Fqv6+rq5JxTXV2dampqAmvFpgUbPV6xjoMNAADCU1tbq/b2dkne/VlBtmLTgo0eLxKJaOfOnQnl3ixdC/SGDRv01a9+taP87W9/WxMmTAgqrFA0Nzdrz64yzX/08MDqfG5XmQY1NwdWHwAgtYaGBrW2eoPdtba2qr6+XrNnzw6kbhJs9Hidk+tUZXgmTpzY8XP//v1LPrkGCqG7E7ccyqQtEt23gO6oqqrSypUr5ZyTmWnatGmB1U2CDfQio0aN0j//+U/dfPPNYYcSiIqKCu1v3a5rTv9XYHXOf/Rw9auoCKw+BKuxsVFPP/E3nTA4t+FADzvg9cjc/9yGnOt8fjezvQLdceGFF2rFihWSvEnpLrjggsDqJsEGepHDDz9c48aNo/UaOAQnDG4L/EMbgNzFk+u4lStXBtZFhJscAQAAUHLq6+sTyqtWrQqsbhJsAAAAlJzhw4dnLBcSCTYAAABKzssvv5yxXEi9qg82d38DAAD0DtOmTUsYReTcc88NrO5elWA3NjZq0+Nb1TZwaE779XnTSZI2Ppv7J5+yva/mvA8AAAAOTTQa1X333acDBw6ob9++qqmpCazuXpVgS1LbwKFqOeX8wOob8NS9gdUFAAAATyQS0XnnnaeVK1fqvPPOC3Qiul6XYAMAAKB3iEajampqCrT1WuImRwAAACCvAm3BNrPjJS2VNFySk7TYObfAzIZKulvSSElNki51zr1mZiZpgaTzJe2VNN0592iQMQM4dN29wVg6tJuMm5ubdXS3agUAlILa2lpt2bJFS5cuDWySGSn4LiKtkr7inHvUzIZI2mhm9ZKmS1rtnLvFzK6WdLWkqySdJ+lk//FeST/ynwH0IN29wVjq/k3GZXtf1eD+faW+OVcJACgBsVhMdXV1cs7pvvvuU01NTWD9sANNsJ1z2yVt93/eZWZbJVVIukjSFH+zWklr5SXYF0la6pxzkv5kZkea2Qj/OAB6kFBuMG7fFVh9AIDiUltbqwMHDkiSDhw4EGgrdmg3OZrZSEnvlvRnScM7Jc0vyetCInnJ9wuddtvmLyPBBgAAgcjUza25uVmSVFFRkXI9c2GEp76+Xl4breSc06pVqwJLsEO5ydHMBkv6taQvOef+1Xmd31rtcjzeDDPbYGYbduzYkcdIAQAA0mtpaVFLS0vYYSCFMKdKD7wF28z6ykuulznn/s9f/HK864eZjZD0ir+8WdLxnXY/zl+WwDm3WNJiSZo4cWJOyTkAAEAmmVqg4zdgL1iwIKhwkKUwp0oPtAXbHxXkZ5K2Oue+02nVCklR/+eopN91Wl5jnjMlvUH/awAAAHTlAx/4QMZyIQXdgv1+Sf8paYuZbfKXXSPpFkm/MrNPS3pO0qX+unvlDdHXKG+Yvk8FGy4AAAB6onj/6zAEPYrIekmWZvU5KbZ3kr5Q0KAAAABQctavX59QXrdunebMmRNI3czkCAAAgJJTVVWlsrIySVJZWZmmTZsWWN0k2AAAACg50Wi0I8EuLy9XTU1NYHWTYAMAAKDkRCIRVVdXy8xUXV0d2CyOUogTzQAA0NM0Nzdrz64yzX/08MDqfG5XmQY1HzRCLYAsRKNRNTU1Bdp6LZFgA0Cvl2mWuq7E94uPBZwLZrgDUGiRSEQLFy4MvF4SbADo5RobG/X0E3/TCYPbct73sANeT8P9z23Iab/nd5flXFcxo01M/gAAHs9JREFUqKio0P7W7brm9H91vXGezH/0cPVLMw03gOJEgg0A0AmD2wJPGgGgVJFgAyhpz+/Ovb/sy3u9VtnhA9u7Vd/JOe8FACiExsZGXXnllVqwYIEqKysDq5cEG0DJGjBggCq68Qf1Tb9fcb+3577vyVKgf8QBAOnNmzdPe/bs0bx587RkyZLA6iXBBlCyKioqtGDBgpz3i9+w1519AQDFobGxUU1NTZKkpqYmNTY2BtYAQoINAAB6ve6OpnMoI+lIjKZTSPPmzTuoHFQrNgk2AADo9RobG7Xp8a1qGzg0p/36vOkkSRuffTnnOsv2vprzPshevPU6XbmQSLABAAAktQ0cqpZTzg+svgFP3RtYXb3RyJEjE5LqkSNHBlY3U6UDAACg5Fx33XUZy4VECzYAACWqublZZXvfCLSltGxvTM3NrYHVB6RTWVmpwYMHa/fu3Ro8eDDD9AHJcr35JPlmE24iAUpLWDekNTc36+hu7QkgaLFYTC0tLZKkffv2KRaLKRKJBFI3CTYAoMcJ64a0wf37Sn1z3jU0FRUVeml/eeD9iisqhgdWH5BObW2t2traJEmtra1aunSpZs+eHUjdJNjoETK1Pk+ZMuWgZYxfDJS+UG5Ia98VWH0ADk19fX1CedWqVYEl2NzkiB4v+ZflK1/5SkiRAACAYpHcHeToo4Pr4EWCjR7voosuSihfcMEFIUUCAACKxfbt2xPKL774YmB1k2CjJBx77LGSaL0GAADhI8FGSRg2bJjGjRtH6zUAAJAkTZ48OWO5kEiwAQAAUHIOO+ywhHK/fv0Cq5tRRAAAyMHzu8s0/9HDc9rn5b1ee9bwge3dqu/knPcCsH79+oTyunXrNGfOnEDqJsFGznKZ4IEJXwCUkgEDBqiiG7PBven/zez39tz3PVkKdAa63opZL0vPGWecoQcffLCj/J73vCewukmwAQDIUkVFRbfG2Y83NjBGPxCcZ599NqH8zDPPBFZ3r0qw+XSaH+laoKdOnSrnXEfZzPhnAvQAzc3N2rMr924Ph+K5XWUa1NwcWH1AV5j1svS88MILGcuFxE2OyJuf/OQnGcsAAABBOeaYYxLKw4cH92GmV7Vg8+m0sDr3ETQz+gwCPURFRYX2t27XNaf/K7A65z96uPpVVARWH4De51//Svyb9sYbbwRWNy3YyKuTTz5Zffr0ofUaAACEat++fRnLhUSCjbwaOHCgxowZQ+s1AADotXpVFxGgFOQyTGKy+H7Jwydmi2EWAQA9xdixY7V58+aO8rhx4wKrmwQb6GEaGxv19BN/0wmD23Le97AD3pdW+5/bkPO+z+8uy3kfAADC8uqrryaUY7FYYHWTYAM90AmD2wK9IU1SoEO4AQBwqLZt25axXEj0wQYAAEDJiUQiGcuFRAs2gIJjkicAQNBee+21jOVCogUbAAAAJae9vT1juZBowQZQcEzyBADoTWjBBgAAQMnp379/xnIhkWADAACg5Hz9619PKF911VWB1U2CDQAAgJKzadOmjOVCog82ABSJsGbpbGxs1PF9u1UtAIQu3d/OLVu2JJRXrlyppqamg7YrxCzFJNgAUCQaGxu16fGtahs4NOd9+7zpJEkbn305533L9uyVjsx5NwAoakcddVTC7I1HHXVUYHWTYANAEWkbODTQ0VYkafCjd0h6M9A6ASBf0rU+x2IxXXzxxXLOqV+/flq8eHFgk83QBxsAAAAlJxKJaOhQ7xvB6upqZnIEAAAIWtneV3OecbbPvn9Jktr7H96t+qTSGa+/q/tImpubJXlzI6RSiL7Qw4cP1759+1RTU5PX43aFBBsAAPR6lZWV3dqvsXGXt/+J3UmUh3e73p6opaUl8Dr79u2rysrKQFuvJRJsAACAbrecxkfuWbBgQT7D6ZG6Ooe96VyRYIcs09cpYXyVgtx0d1i1QxlS7YknnlBfV6b5j+b+deSheG5XmQb51yQA4P+3d/9RcpX1Hcc/3+xukk0jrSy42pUkDYlFaqCF2GqbVGgjhlQBxXrE9mRp60nbIz+a2qpHOYZYfnh6Ts8pxFYPFXFjq2B/J21ckpBGsScBAwQxB5UJJiGDBB1SINkl++vbP+6dZWayOztzZ+beOzPv1zl7dp67c/f57pOZzHeeee73AaZHgp1iSXyUgupELatWU0m1sTF1dVR9GgAAiAkJdsLKzUC300cpzSzusmrzH/2KFr5mSJ+86KXY+pSk2x49Q3Om+TQFAAC8igQbAKAjJ6ItOzo2FFR77Z03UXV/S6vuDQCaAwk2ALQ5n9Ulmz1bcxZWX81gJLyeoNpzlyp61QYASDsSbABocxNzz9CSxb2RlqOxlA0ATkeCDQAAgIpErZ4l1VZBq9kqp5FgAwAAoCJRq2dJ0StoBTteNhcSbAAAAFQs7upZ1W5fnwazkg4AAAAAaCXMYAMA0MI6hl6INAM465Wg1v7E3OrKNwYf5/dW3R/QSkiwY5DNZiMt6K/lYgCp+S4I4MIJAKivWkohZjIvB79jcbXJci8lGNH2SLBjMDw8rKcOPKYF88erOm/2aLCC59ThfVX3eeRE8+2lnclkIo2TFH2smnGcAKBStUweUIIRiI4EOyYL5o/HurV1lB3Z0oBxAgAAzY4EGwDQdLLZrDqGXoy1ukDHUE7Z7Fhs/QFoXiTYAAAAqAhvbitDgg0AaDp9fX167lRn7LV4+/qojgFgZiTYAAAAqAhvbivDRjMAAABAHTGDDTShIyc6IlVAOTYUvKfunTcRqc+lVZ8FAED7iTXBNrMvSXq3pOfd/S3hsTMl3SdpkaRDkj7g7sfNzCTdIWmNpCFJ17r7o3HGC6SRz+qSzZ6tOQur38hhJNyQJ8q5S1XbphUAALSLuGewvyzpc5I2Fxz7hKQH3P2zZvaJsP1xSZcreE1fKunXJH0+/A60tYm5Z2jJ4t5Imz8kuXEE2zUDANpFrAm2u3/LzBaVHL5S0iXh7QFJuxUk2FdK2uzuLmmvmf2cmb3B3X8cT7QA6oXtmgEAtchms5OTRNXIhJ/cRjlXCl6/ouyImoY12L0FSfNzenXKqU/SMwX3OxoeI8EGmgzbNQMAajE8PKynDjymBfPHqzpv9mhw7dGpw/uq7vPIiY6qz8lLQ4I9yd3dzLza88xsnaR1krRgwYK6xwUAAIBkLZg/rk9e9FJs/UUpJpCXhjJ9x8zsDZIUfn8+PJ6VdE7B/d4YHjuNu9/l7svdffnZZ5/d0GABAACActKQYG+R1B/e7pf0nwXH11rgbZJeZP01AAAA0i7uMn1fU3BB41lmdlTSBkmflfR1M/sjSYclfSC8+zYFJfoyCsr0/UE9YohSySBqFYN8f5rbVfV57Sibzerky9HqO0d1+OUO/Ux2yg9GgNhls1l1DL0YqdpKLTqGcspmx2LtEwBaWdxVRK6Z5ke/PcV9XdJH6tl/1IoC0asYSFKvstmsNPZ/kfoGAABAc0nVRY6NFrWSQa1VDG688UadOszqlpn09fXp1NiPY7+AYU5fX2z9AeX09fXpuVOdGj5vTaz9dn9/m/r6qBkOAPXSVgk2KrNp06bJupHVqqXeZDab1VmRegUAAHFJZOOwJltuS4Idg2ZbW5zJZLT/e09qfN6ZVZ87aySosvjI08eqOq9j6AXNn9slNdfzB02s3BvJSt4oRt18AACaWVIbhzXbclsSbExpfN6ZsX5M3f39bdLEy7H1B5TT3d2ddAgAkEpJbRzWbMttSbBjwNpiIH2YfQaA5tFsqwHSUAcbAAAAaBnMYAMAACDVmm01AAk2AABAGbVcFM0F0e2JBBsAACAiLorGVEiwgRoksbU121oDQLyYgUa1SLABAE0pymYXUTe6yPcnTV+/t9wygieffFIjIyNau3atXvva1572c5YRAK2FBBupcuREtBI8x4aCgji98yaq7m9p1b29KomtrdnWGoi+2UX0jS4kqTdyvyMjI5KkI0eOTJlgA2gtJNhIje7ubvVFffEKZ43mLKzu/KWqbVcqAMmIOttby0YXM5kupl27dunxxx+fbF911VW69NJL694/0OqiTMJFnYDL9xd1Eo4EG6nR19cX+UWvkS+aAFCL2267rah96623kmADVYr86VHECTiptkk4EmwAKJHL5bRx40Zt2LBBPT09SYeDJjc2Nla2DWBmafzUqhx2cgSAEgMDA3riiSe0efPmpENBC+js7CzbBtB6SLABoEAul9Pg4KDcXYODg8rlckmHhCZXOvM23YYkAFoHb6MBoMDAwIDGx8clBR/lb968WevXr084KjSzgwcPFrWnK+UHNLtypSql9tr1kgQbAArs3LlzMsEeHx/Xjh07Yk2wo9R2lhpb3xm12blzZ1E77scUkBbttOslCTYAFFixYoW2b98+2V65cmVsfddSMjKp+s6Y2YUXXqg9e/YUtYFW1Cqzz/VAgg0ABcwssb5reXGiVGV67d+/v2wbQOvhIkcAKPDggw+WbQPVGh4eLtsG0HpIsAGgwKpVqybLqHV2duqd73xnwhEBAJoNCTYAFOjv79esWcF/jR0dHVq7dm3CEaHZzZ07t2wbQOthDTZQoyhVH6j4kF49PT1avXq1tm7dqtWrV7OTIwCgaiTYOE02m1XH0IuRSoVF1TGUUzbbfNsHR628QMWHdOvv79ehQ4eYvUZdXHbZZdqyZctk+13veleC0QCIAwk2UIOoVR+o+JBuPT09uvPOO5MOAy2iv79f3/jGNzQ6Oqquri7euAFtgAQbp+nr69Nzpzo1fN6a2Prs/v429fWx7AHpkMvltHHjRm3YsIElIqhZT0+PLr/8cm3dulVr1qzhMQW0AS5yBIASAwMDeuKJJ7R58+akQ0GL6O/v17Jly5i9BtoECTYAFMjlchocHJS7a3BwULlcLumQ0ALyy46YvQbaAwk2ABQYGBjQxMSEJGl8fJxZbABA1UiwAaDAzp07NTYWVLQZGxvTjh07Eo4IANBsSLABoAA7OQIAakUVkZgcOdGh2x6tblORY0PB+5/eeROR+lta9VkA+vv7NTg4KImdHAEA0ZBgxyDqpiAjmYwkac7C6s9fWkO/QDtjJ0cAQK1IsGPAZiRAc2EnRwBALUiwAaAEOzkCAGrBRY4AADRYLpfTDTfcQF11oE0wg40pdQy9oO7vb6v6vFmvvCRJmphb3QWdHUMvSGKrdACtqXB30PXr1ycdDtBSNm3apEx43VqpH/zgB3rllVe0bt06dXd3T3mfJUuWRF7OOx0SbJymlosjM5mXg9+xuNpkuZeLMgG0pNLdQdeuXcvFs0BMRkZGJEmHDx/WeeedF1u/JNg4TS3v4rgwEwCKTbU7KLPYQP1Ml7dkMhl9+MMfliSdOnVK119/fWyTeazBBgCggdgdFEjGLbfcUrbdSCTYAAA0ELuDAsk4dOhQ2XYjsUQEaDHlLvbIH88v5SnViAs90NzKPZ4kHlOVYHdQIBmLFi0qSqoXLVoUW9/MYANtpLu7e9qrqIEoeEzNrKenR5deeqkk6ZJLLuECRyAmN910U9l2IzGDDbSYcrOFuVxOGzdu1Kc//Wle5FGRdp99rhd3TzoEADFiBhtoI4W1eAHEI5fLaffu3ZKk3bt3s9kMEBMucgTQcKW1eHmRB+IxVZk+AI2X5EWOJNhAmxgYGND4+LikoFQYL/JAPCjTBySj9KJGLnIEUHc7d+6cTLDHx8d5kQdiQpk+IBlJXuRIgg20iRUrVhS1V65cmVAkQHvp7+/XrFnByy1l+oD4LFmyRL29vZKk3t7e2HZxlEiwgbZhZkmHALSlnp4erV69Wmam1atXU8EHiNFLL71U9D0uJNhAm3jwwQfLtgE0Tn9/v5YtW8bsNRCjffv2aXh4WJI0PDysRx55JLa+SbDREkZHR5XJZKiMUcaqVauK2qwDRT3kcjndcMMNPPdmcPz4cR08eFDHjx9POhSgbdx8881F7Q0bNsTWNwk2WsKxY8d08uRJKmOUccUVVxS13/Oe9yQUCVoJtdUrc8stt+jkyZOx1uEF2t2JEyfKthuJnRzRFDZt2qRMJjPlz0ZHRydnz7Zs2aKnnnpKXV1dRfdZsmRJ2+9I98UvfrGofffdd+v2229PKBq0gtLa6mvXrmV98RQymcxk/d1Dhw4pk8nEerEV0K7mz59flFTPnz8/tr6ZwUbTO3bs2ORtdy9q41V79+4tau/ZsyehSNAq2EClMknuJge0s9IlIhs3boytb2aw0RTKzT6vWbOmqD08PKw77rij0SEBbW+qDVTWr1+fcFTpk+RuckA7W758uTo7OzU2NqbOzk5dfPHFsfXNDDaa3pvf/Oai9vnnn59QJOk2b968sm2gWmygUplzzjmnbBtAY+RyuclP2dw91ouxSbDR9Pbv31/UfuyxxxKKJN0uuOCCovaFF16YUCRoFWygUpnFixcXtc8999yEIgHay8DAgNxdkjQxMRHrMjYSbDS9/Pbf07UR2LdvX1H7O9/5TkKRoFWwgUplHn744aL2Qw89lFAkQHvZsWPHZILt7tq+fXtsfZNgA20iv1Z2ujYQBRuozCy/VfN0bQCNkeRzj4scAQCR9fT06M4770w6jFQrrWxEpSMgHs8++2zZdiMxgw0AiIydHGdWevHnZZddllAkQHvJX+A4XbuRSLCBNpGv9jBdG4iCnRxn1t/fX9RmOQ0QjySXRpJgA21ixYoVZdtAtUp3cmQWe2rHjx8v2wbQekiwgTZRutX8wYMHE4ok/Vj2UBl2cqwMOzkC7Sf1CbaZrTazH5hZxsw+kXQ8QLM6evRoUfuZZ55JKJL0u/rqq/Xd735XV199ddKhpNpUOznidOzkCLSfVCfYZtYh6e8kXS7pfEnXmBnb9AERLFq0qGwbgdJZa2axp7dq1aqiNjs5To3nHtB+Up1gS/pVSRl3f9rdRyTdK+nKhGMCmhKzaJUpnbVmFnt6W7duLdtGoPTTo2w2m1AkAOKS9jICfZIKP8c+KunXGtHRpk2bTlujmpc/fuONN057/pIlS3T99dfH2m/UPhEP/m0rU26cJMaqUNoeU/kd0vLiLIFVTlL/n0+ntHLB6Oho3X53rdL2mAJaRdoT7IqY2TpJ6yRpwYIFdf/93d3ddf+dae4Xjce/beUYq8owTpVhnCrHWAHRpT3Bzko6p6D9xvBYEXe/S9JdkrR8+XIv/XklknoXzrv/1sW/bWUYp8oxVpVhnCrHWAGNkfY12N+RtNTMfsHMZkv6oKQtCccEAJA0a1bxS0hHR0dCkaTb7Nmzi9pz5sxJKBKgvXR1dRW1S5+LjZTqBNvdxyRdJ+l+SU9K+rq7H0g2KqTN7t27y7YRYJwqwzhVbteuXUXtBx54IKFI0m379u1F7fvvvz+hSID2Ulo6tPS52EhpXyIid98maVvSceBVXBQDIG/WrFmamJhg9noGs2fP1sjICLPXQMy6uro0Ojoa6+y11AQJNppLUhfFMMtYGcapMoxT5UpnsTG1OGfOALwqqQ2wSLBRNWagAQAAppfqNdgAAABAsyHBBgAAAOqIBBsAAACoIxJsAAAAoI5IsAEAAIA6IsEGAAAA6ogEGwAAAKgjEmwAAACgjkiwAQAAgDoiwQYAAADqiAQbAAAAqCMSbAAAAKCOSLABAACAOiLBBgAAAOqIBBsAAACoIxJsAAAAoI5IsAEAAIA6IsEGAAAA6sjcPekY6srMfiLpcNJxlDhL0k+TDqJJMFaVYZwqwzhVjrGqDONUOcaqMoxTZdI6Tgvd/ezSgy2XYKeRme1z9+VJx9EMGKvKME6VYZwqx1hVhnGqHGNVGcapMs02TiwRAQAAAOqIBBsAAACoIxLseNyVdABNhLGqDONUGcapcoxVZRinyjFWlWGcKtNU48QabAAAAKCOmMEGAAAA6ogEu4HM7PVmdq+ZHTSzR8xsm5m9Kem40sjMrjIzN7Pzko4lrcxs3Mz2m9kBM3vczD5qZjyHp2BmvWb2VTN7Onzu7TGz9yYdVxoVPK7yX59IOqY0mmKcFiUdUxqZ2YmS9rVm9rmk4kmz0rHC1Aqee4+b2aNm9utJx1SJzqQDaFVmZpL+XdKAu38wPHahpF5JP0wytpS6RtK3w+8bEo4lrYbd/ZclycxeJ+mrks4Q41UkfO79h4Ln3ofCYwslXZFoYOk1+bhCWYwTkIzC1753Sbpd0juSDWlmzH41zqWSRt39C/kD7v64uz+YYEypZGbzJa2Q9EeSPphwOE3B3Z+XtE7SdWFCiVf9lqSRkufeYXfflGBMAIDanSHpeNJBVIIZ7MZ5i6RHkg6iSVwpadDdf2hmOTO72N0Zuxm4+9Nm1iHpdZKOJR1PivySpEeTDqKJdJvZ/oL27e5+X2LRpFfhOP3I3VlyNLXSx9OZkrYkFQxaQv4xNVfSGxRMoqQeCTbS4BpJd4S37w3bJNioCzP7OwWfkIy4+1uTjieFWPpQGcapMkXjZGbXSmqa3feQSoVLRN4uabOZvcVTXgaPBLtxDkh6f9JBpJ2Znang3egyM3NJHZLczP4y7U+epJnZYknjkp5POpaUOSDp6nzD3T9iZmdJ2pdcSACAWrn7nvD/87OV8tc+1mA3zi5Jc8xsXf6AmV1gZisTjCmN3i/pK+6+0N0Xufs5kn4kiXEqw8zOlvQFSZ/jjchpdkmaa2Z/WnBsXlLBAADqI6w01iEpl3QsMyHBbpAw6XmvpFVhmb4DCq58fS7ZyFLnGgXVVgr9a3gcxbrzZfok7ZS0XdLGhGNKnfC5d5Wkd5jZj8zsYUkDkj6ebGSplX9c5b8+m3RAQJuYZ2ZHC77+POmAUmry/yhJ90nqd/fxpIOaCTs5AgAAAHXEDDYAAABQRyTYAAAAQB2RYAMAAAB1RIINAAAA1BEJNgAAAFBHJNgAEAMzu9nMvODrOTP7LzO7IOnYyjGza0vinurrUNJxAkCasJMjAMTnRUmrw9uLJH1G0g4ze7O7v5BYVOX9t6S3F7TfL+mjJcdOxRoRAKQcCTYAxGfM3feGt/eGM797FCTdX00sqjLc/SeSfpJvm9ny8PjeaU8CgDbHEhEASM7j4fdz8gfMrCNcTnLEzE6Z2QEz+1DpiWb2ATN7IrzPM2Z2q5l1Fvw8v7TjIjPbbWZD4W5oF5nZz5jZPWb2opk9bWaRd041s4fN7MtTHP+ymT0W3r4kjOWycFnMyfDv+5MpzltpZt8M482Z2T+Y2WuixgcASSDBBoDkLAi//6jg2GckfUrSXZKukPS/kv6pMAk2s8sUbBn8qKQrJW2S9BeSPjdFHwOSvibpakkm6V8k3S3pWQXLPR6StNnM3hjxb7hb0vvNbH5BfPPD3/2lKe77XUnvk7RN0ufN7N0F5/2GpJ2SngvP/zNJayTdEzE2AEgEW6UDQAzM7GZJ10l6fXhooaQvSDpL0tvc/ZSZnSnpGUl/7e4bC87dJulcd//FsL1X0rC7X1pwn49Jul3SQnc/ambXKkhMr3X3gfA+axSsqb7H3f8wPPazkn4q6QZ3/3wFf8d1kja5u4XtMyT9WNJ17n5PeOwPJX1e0s+7e87MLpH0P5L+wd3XFfyuHZJe4+5vC9sPKlhGU/h3/ZakByQtc/fvzRQfAKQBM9gAEJ8eSaPhV0bSr0h6n7vnLxJ8i6R5kv655Lz7JL3JzM42sw5JF01zn1kqvvhQCpLTvEz4fVf+gLu/qGCNdV+UP8jdX1IwK35tweFrJW1x91zJ3f+9pP1vki4Ol8XMC2P/upl15r8kfVvBeF0cJT4ASAIJNgDE50VJb5X0Nkl/LGm2pK+aWf7/4jeE34+VnJdvn6lgxrtrhvsU+r+C2yNTHMsfn1tB/NO5W9JKM1tsZudKWqnTl4dI0vNTtDsV/E2vldQh6e/16puQUQUVSrpUsE4dANKOKiIAEJ8xd98X3n7IzIYlbZb0uwpmoH8c/ux1kgpnf3vD7y+EX6PhfTTNfWLl7t8ys6cUzFybgvXd26e4a2nMr5M0pmCJylxJLulmBeuzSz1bp3ABoOGYwQaA5PyjpAOSPh62vydpSEHCXegDkn7o7j9x93FJj0xznwkFZf+S8CVJ/ZLWStocxlnqvVO0H3H3cXc/KWmvpF90931TfJFgA2gazGADQELc3c3sNgVVQn7b3R8ws7+VdJOZjUnap6DixhpJhaX0Nki638zukXSvpGWS/krBRYRH4/0rJg1IukXB68p0VT8uN7NbJX1Twd/1TgVVUPI+JukBM5tQsK77ZQWVVn5H0qfc/YcNih0A6ooEGwCSdZ+CZREfU3BB4qcVLJv4UwXLPjKSft/d782f4O7bzeyDkm6S9HsK1jL/jYLEOxHu/pyZPRTeni4R/rCC0nvrFSxl+Yi7byn4Hd82s9+UtFHSVxSsyT4saVCnrzkHgNSiTB8AoGZhicGsgnJ9d5f87BIFZfootQegLTCDDQCILNxl8XxJNypY0vG1ZCMCgOSRYAMAanGxgtnpw5LWuvtQwvEAQOJYIgIAAADUEWX6AAAAgDoiwQYAAADqiAQbAAAAqCMSbAAAAKCOSLABAACAOiLBBgAAAOro/wGYTVOuZX5luQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###From above graph we can say that **'G'** category room of city hotel are much costlier than other."
      ],
      "metadata": {
        "id": "AmNeZSQANsTF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Assigned room type**"
      ],
      "metadata": {
        "id": "vfhenN8_MgCu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Print assigned room type to guests\n",
        "correct_hb.assigned_room_type.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nxmBiQBQMfkr",
        "outputId": "d3c69a06-fc04-4c6b-c457-cc8970e70007"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "A    74020\n",
              "D    25309\n",
              "E     7798\n",
              "F     3751\n",
              "G     2549\n",
              "C     2370\n",
              "B     2154\n",
              "H      712\n",
              "I      359\n",
              "K      187\n",
              "L        1\n",
              "Name: assigned_room_type, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Draw the graph\n",
        "plt.figure(figsize=(14,7))\n",
        "sns.countplot(correct_hb['assigned_room_type'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "id": "nFPLvd8pMwOI",
        "outputId": "8c12592f-43a2-4475-ef15-2dc881e2497b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###From above we can say that **'A'** type rooms are most assigned room type among all."
      ],
      "metadata": {
        "id": "aeZ6M2WoNQDR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Inferences and Conclusion**:\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "We used the dataset that contains data about hotel bookings.\n",
        "\n",
        "We cleaned and preprocessed the data and then we performed the exploratory data analysis to extract information from the data to answer the following questions.\n",
        "\n",
        "**1. What types of hotel in market and how many numbers of hotels in each type?**\n",
        "\n",
        " **2. How Many booking were cancelled for diffrent types of hotels?**\n",
        "\n",
        "* **Year wise comparision of booking of hotels.**\n",
        "\n",
        "**3. Which is the busiest month for hotels ?**\n",
        "\n",
        "*   **Now we will check comparatively for both types of hotels which month is busiest.**\n",
        "\n",
        "**4. Which was the most booked accommodation type (Single, Couple, Family)?**\n",
        "\n",
        "**5. Which type of meal booked ?** \n",
        "\n",
        "**6. From which country most guests comes?**\n",
        "\n",
        "**7. Which type of market segment is used for bookings hotels ?**\n",
        "\n",
        "**8. How much guests pay per night for each types of rooms ?**\n",
        "\n",
        "*   **Assigned room type**\n",
        "\n",
        "\n",
        "We learned that\n",
        "\n",
        "*  According to this analysis number of City hotels comparatively more than Resort hotels.\n",
        "Resort Hotel tend to be on the expensive side and most people will just stick with city hotel.\n",
        "\n",
        "*  City hotels bookings are more canceled than resort hotels.\n",
        "\n",
        "*   In year 2016 most numbers of guests visited the both type of hotels compare to other years.\n",
        "*   August is the busiest month for hotels,which means august month has most number of bookings.\n",
        "\n",
        "\n",
        "*  August is the busiest month for both type of hotels.\n",
        "\n",
        "*   Couples(No Children) has most number of booking.\n",
        "*   BB i.e. Bed & Breakfast is most preferable meal type of the guests which is 77.4%\n",
        "\n",
        "*   Most of the guests come from PRT i.e. Portugal.\n",
        "*   Guest uses most online TA market segment for hotels booking.\n",
        "\n",
        "*    'G' category room of city hotel are much costlier than other.\n",
        "*    'A' type rooms are most assigned room type among all.\n"
      ],
      "metadata": {
        "id": "KkYYET97f29e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**References**\n",
        "----\n",
        "\n",
        "References:\n",
        "\n",
        "*   Pandas user guide: https://pandas.pydata.org/docs/user_guide/index.html\n",
        "*  Matplotlib user guide: https://matplotlib.org/3.3.1/users/index.html\n",
        "\n",
        "*  Seaborn user guide & tutorial: https://seaborn.pydata.org/tutorial.html\n",
        "\n",
        "*  Tutorials: 1) https://stackoverflow.com ,  2) https://www.w3schools.com\n",
        "            \n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "sJoippoeZoCP"
      }
    }
  ]
}