Analyzing_Hotel_Booking_Cancellations.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Analyzing Hotel Booking Cancellations\n",
        "\n"
      ],
      "metadata": {
        "id": "NDHFF-V21ihu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Business Context\n",
        "\n",
        "As a Data Analyst of a company owning a city hotel in a metropolitan area and a resort hotel located near a coastline both within the United States, you've been assigned by your manager to provide insights on the recent rise of hotel booking cancellations in both hotels for the past few months. These events causes the management to suffer financial losses and they would like to know some leads on reasons why customers cancel their room reservations."
      ],
      "metadata": {
        "id": "4AOYM2Qp3nmF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset\n",
        "\n",
        "You've been given a dataset containing a list of bookings recorded in the booking system. Each row consist a booking made for a particular date and it also contains details containing the following:\n",
        "\n",
        "| Column Name                    | Description                                                                 |\n",
        "|--------------------------------|-----------------------------------------------------------------------------|\n",
        "| hotel                          | The type of hotel the booking was made                                      |\n",
        "| lead_time                      | The time (in hours) between the booking date and the expected arrival date  |\n",
        "| arrival_date_year              | The year of guest's expected arrival                                        |\n",
        "| arrival_date_month             | The month of guest's expected arrival                                       |\n",
        "| arrival_date_day_of_month      | The day of the month of guests's expected arrival                           |\n",
        "| adults                         | Describes how many guests in the booking are adults                         |\n",
        "| children                       | Describes how many guests in the booking are children                       |\n",
        "| babies                         | Describes how many guests in the booking are babies                         |\n",
        "| meal                           | A category that describes what type of meal ordered by the guest |\n",
        "| country                        | Country of Origin (in ISO 3166 Alpha-3 code format)                         |\n",
        "| is_repeated_guest              | Describes whether or not the guest have booked before                       |\n",
        "| previous_cancellations         | Describes the number of previous cancellations by the guest                 |\n",
        "| previous_bookings_not_canceled | Describes the number of previous non-canceled bookings by the guest         |\n",
        "| reserved_room_type             | A category that describes what room type the guest reserved                 |\n",
        "| assigned_room_type             | A category that describes what room type the guest received                 |\n",
        "| booking_changes                | The number of changes the customer did on the booking                       |\n",
        "| deposit_type                   | The type of payment deposit used by the guest                               |\n",
        "| days_in_waiting_list           | Describes the number of days the guest was in the waiting list              |\n",
        "| customer_type                  | Describes the type of guest in the booking                                  |\n",
        "| adr                            | The potential \"Average Daily Revenue\" of the booking                        |\n",
        "| car_parking_spaces             | The number of car parking spaces requested by the guest                     |\n",
        "| special_requests               | The number of special requests given by the guest                           |\n",
        "| status                         | The updated status of the booking                                           |\n",
        "| status_last_update             | The date of the status registered to the booking system                     |"
      ],
      "metadata": {
        "id": "uJzBxe7r4tjh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Python Libraries\n",
        "\n",
        "For me to analyze the given data, we first have to import the necessary Python libraries."
      ],
      "metadata": {
        "id": "45PJd-Tb26Uv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Task 1: Import the following python libraries to be used for this task\n",
        "\n"
      ],
      "metadata": {
        "id": "FBxWNX4nGZEJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n"
      ],
      "metadata": {
        "id": "RuYFhW8K3m6T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Task 2: Load the `.csv` file into a pandas dataframe and store it in a variable named `data`.\n",
        "\n"
      ],
      "metadata": {
        "id": "Sfzm_FbxIf06"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "5dTZ2IYfIQ73",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59078bfc-7048-4d01-c959-4cdf7f8eceeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "After mounting the drive and uploading the dataset on it, I can import the given dataset."
      ],
      "metadata": {
        "id": "nyejReARJ08e"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wWPm56ydvjYh"
      },
      "outputs": [],
      "source": [
        "\n",
        "df = pd.read_csv('/content/drive/MyDrive/HOME ASSIGNMENT/Home Assignment 5.10/drive-download-20230511T041442Z-001/hotel_bookings_data.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.head()"
      ],
      "metadata": {
        "id": "6olzBC9mLlvk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "b02809a7-68d7-43a6-ef48-5d9a2cf9b5b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          hotel  lead_time  arrival_date_year arrival_date_month  \\\n",
              "0  Resort Hotel        342               2015               July   \n",
              "1  Resort Hotel        737               2015               July   \n",
              "2  Resort Hotel          7               2015               July   \n",
              "3  Resort Hotel         13               2015               July   \n",
              "4  Resort Hotel         14               2015               July   \n",
              "\n",
              "   arrival_date_day_of_month  adults  children  babies meal country  ...  \\\n",
              "0                          1       2       0.0       0   BB     PRT  ...   \n",
              "1                          1       2       0.0       0   BB     PRT  ...   \n",
              "2                          1       1       0.0       0   BB     GBR  ...   \n",
              "3                          1       1       0.0       0   BB     GBR  ...   \n",
              "4                          1       2       0.0       0   BB     GBR  ...   \n",
              "\n",
              "   assigned_room_type  booking_changes  deposit_type days_in_waiting_list  \\\n",
              "0                   C                3    No Deposit                    0   \n",
              "1                   C                4    No Deposit                    0   \n",
              "2                   C                0    No Deposit                    0   \n",
              "3                   A                0    No Deposit                    0   \n",
              "4                   A                0    No Deposit                    0   \n",
              "\n",
              "  customer_type   adr car_parking_spaces  special_requests     status  \\\n",
              "0     Transient   0.0                  0                 0  Check-Out   \n",
              "1     Transient   0.0                  0                 0  Check-Out   \n",
              "2     Transient  75.0                  0                 0  Check-Out   \n",
              "3     Transient  75.0                  0                 0  Check-Out   \n",
              "4     Transient  98.0                  0                 1  Check-Out   \n",
              "\n",
              "   status_last_update  \n",
              "0            7/1/2015  \n",
              "1            7/1/2015  \n",
              "2            7/2/2015  \n",
              "3            7/2/2015  \n",
              "4            7/3/2015  \n",
              "\n",
              "[5 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e4a605a9-2f43-4f22-98cc-af317af7effc\">\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>lead_time</th>\n",
              "      <th>arrival_date_year</th>\n",
              "      <th>arrival_date_month</th>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <th>adults</th>\n",
              "      <th>children</th>\n",
              "      <th>babies</th>\n",
              "      <th>meal</th>\n",
              "      <th>country</th>\n",
              "      <th>...</th>\n",
              "      <th>assigned_room_type</th>\n",
              "      <th>booking_changes</th>\n",
              "      <th>deposit_type</th>\n",
              "      <th>days_in_waiting_list</th>\n",
              "      <th>customer_type</th>\n",
              "      <th>adr</th>\n",
              "      <th>car_parking_spaces</th>\n",
              "      <th>special_requests</th>\n",
              "      <th>status</th>\n",
              "      <th>status_last_update</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>342</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>PRT</td>\n",
              "      <td>...</td>\n",
              "      <td>C</td>\n",
              "      <td>3</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>7/1/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>737</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>PRT</td>\n",
              "      <td>...</td>\n",
              "      <td>C</td>\n",
              "      <td>4</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>7/1/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>7</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>GBR</td>\n",
              "      <td>...</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</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>7/2/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>13</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>GBR</td>\n",
              "      <td>...</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</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>7/2/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>14</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>GBR</td>\n",
              "      <td>...</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</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>7/3/2015</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e4a605a9-2f43-4f22-98cc-af317af7effc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e4a605a9-2f43-4f22-98cc-af317af7effc 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-e4a605a9-2f43-4f22-98cc-af317af7effc');\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": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.info()"
      ],
      "metadata": {
        "id": "-TDk4BsIMaFN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ea71fed6-51ee-4877-e051-d5a39740b7e2"
      },
      "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 24 columns):\n",
            " #   Column                          Non-Null Count   Dtype  \n",
            "---  ------                          --------------   -----  \n",
            " 0   hotel                           119390 non-null  object \n",
            " 1   lead_time                       119390 non-null  int64  \n",
            " 2   arrival_date_year               119390 non-null  int64  \n",
            " 3   arrival_date_month              119390 non-null  object \n",
            " 4   arrival_date_day_of_month       119390 non-null  int64  \n",
            " 5   adults                          119390 non-null  int64  \n",
            " 6   children                        119386 non-null  float64\n",
            " 7   babies                          119390 non-null  int64  \n",
            " 8   meal                            119390 non-null  object \n",
            " 9   country                         118902 non-null  object \n",
            " 10  is_repeated_guest               119390 non-null  int64  \n",
            " 11  previous_cancellations          119390 non-null  int64  \n",
            " 12  previous_bookings_not_canceled  119390 non-null  int64  \n",
            " 13  reserved_room_type              119390 non-null  object \n",
            " 14  assigned_room_type              119390 non-null  object \n",
            " 15  booking_changes                 119390 non-null  int64  \n",
            " 16  deposit_type                    119390 non-null  object \n",
            " 17  days_in_waiting_list            119390 non-null  int64  \n",
            " 18  customer_type                   119390 non-null  object \n",
            " 19  adr                             119390 non-null  float64\n",
            " 20  car_parking_spaces              119390 non-null  int64  \n",
            " 21  special_requests                119390 non-null  int64  \n",
            " 22  status                          119390 non-null  object \n",
            " 23  status_last_update              119390 non-null  object \n",
            "dtypes: float64(2), int64(12), object(10)\n",
            "memory usage: 21.9+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Cleaning\n",
        "\n"
      ],
      "metadata": {
        "id": "AsQHN9Hi2PIT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Task 3: Remove all completely duplicated rows in the dataframe `data`\n",
        "\n"
      ],
      "metadata": {
        "id": "qOwWY8MSNDi3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To start off, i can first check if there are any duplicates found in the dataset.\n"
      ],
      "metadata": {
        "id": "hs_ytDksPNyn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df[df.duplicated()]"
      ],
      "metadata": {
        "id": "XP5xLdy5Ou0G",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "outputId": "9d4a468b-16c4-438f-f8f5-22a3d1cf9018"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               hotel  lead_time  arrival_date_year arrival_date_month  \\\n",
              "5       Resort Hotel         14               2015               July   \n",
              "22      Resort Hotel         72               2015               July   \n",
              "43      Resort Hotel         70               2015               July   \n",
              "138     Resort Hotel          5               2015               July   \n",
              "200     Resort Hotel          0               2015               July   \n",
              "...              ...        ...                ...                ...   \n",
              "119349    City Hotel        186               2017             August   \n",
              "119352    City Hotel         63               2017             August   \n",
              "119353    City Hotel         63               2017             August   \n",
              "119354    City Hotel         63               2017             August   \n",
              "119373    City Hotel        175               2017             August   \n",
              "\n",
              "        arrival_date_day_of_month  adults  children  babies meal country  ...  \\\n",
              "5                               1       2       0.0       0   BB     GBR  ...   \n",
              "22                              1       2       0.0       0   BB     PRT  ...   \n",
              "43                              2       2       0.0       0   HB     ROU  ...   \n",
              "138                             5       2       0.0       0   BB     PRT  ...   \n",
              "200                             7       1       0.0       0   BB     GBR  ...   \n",
              "...                           ...     ...       ...     ...  ...     ...  ...   \n",
              "119349                         31       2       0.0       0   BB     DEU  ...   \n",
              "119352                         31       3       0.0       0   BB     SWE  ...   \n",
              "119353                         31       3       0.0       0   BB     SWE  ...   \n",
              "119354                         31       3       0.0       0   BB     SWE  ...   \n",
              "119373                         31       1       0.0       0   BB     NLD  ...   \n",
              "\n",
              "        assigned_room_type  booking_changes  deposit_type  \\\n",
              "5                        A                0    No Deposit   \n",
              "22                       A                1    No Deposit   \n",
              "43                       E                0    No Deposit   \n",
              "138                      D                0    No Deposit   \n",
              "200                      A                0    No Deposit   \n",
              "...                    ...              ...           ...   \n",
              "119349                   D                0    No Deposit   \n",
              "119352                   D                0    No Deposit   \n",
              "119353                   D                0    No Deposit   \n",
              "119354                   D                0    No Deposit   \n",
              "119373                   A                0    No Deposit   \n",
              "\n",
              "       days_in_waiting_list    customer_type     adr car_parking_spaces  \\\n",
              "5                         0        Transient   98.00                  0   \n",
              "22                        0        Transient   84.67                  0   \n",
              "43                        0        Transient  137.00                  0   \n",
              "138                       0        Transient   97.00                  0   \n",
              "200                       0        Transient  109.80                  0   \n",
              "...                     ...              ...     ...                ...   \n",
              "119349                    0        Transient  126.00                  0   \n",
              "119352                    0  Transient-Party  195.33                  0   \n",
              "119353                    0  Transient-Party  195.33                  0   \n",
              "119354                    0  Transient-Party  195.33                  0   \n",
              "119373                    0        Transient   82.35                  0   \n",
              "\n",
              "        special_requests     status  status_last_update  \n",
              "5                      1  Check-Out            7/3/2015  \n",
              "22                     1  Check-Out            7/7/2015  \n",
              "43                     1  Check-Out            7/7/2015  \n",
              "138                    0   Canceled            7/1/2015  \n",
              "200                    3  Check-Out            7/8/2015  \n",
              "...                  ...        ...                 ...  \n",
              "119349                 2  Check-Out            9/3/2017  \n",
              "119352                 2  Check-Out            9/3/2017  \n",
              "119353                 2  Check-Out            9/3/2017  \n",
              "119354                 2  Check-Out            9/3/2017  \n",
              "119373                 1  Check-Out            9/4/2017  \n",
              "\n",
              "[32090 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cb038ea9-ac6e-4e02-b5f1-f907b1c635c6\">\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>lead_time</th>\n",
              "      <th>arrival_date_year</th>\n",
              "      <th>arrival_date_month</th>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <th>adults</th>\n",
              "      <th>children</th>\n",
              "      <th>babies</th>\n",
              "      <th>meal</th>\n",
              "      <th>country</th>\n",
              "      <th>...</th>\n",
              "      <th>assigned_room_type</th>\n",
              "      <th>booking_changes</th>\n",
              "      <th>deposit_type</th>\n",
              "      <th>days_in_waiting_list</th>\n",
              "      <th>customer_type</th>\n",
              "      <th>adr</th>\n",
              "      <th>car_parking_spaces</th>\n",
              "      <th>special_requests</th>\n",
              "      <th>status</th>\n",
              "      <th>status_last_update</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>14</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>GBR</td>\n",
              "      <td>...</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>98.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>7/3/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>72</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>PRT</td>\n",
              "      <td>...</td>\n",
              "      <td>A</td>\n",
              "      <td>1</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>84.67</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>7/7/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>70</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>HB</td>\n",
              "      <td>ROU</td>\n",
              "      <td>...</td>\n",
              "      <td>E</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>137.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>7/7/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>138</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>5</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>PRT</td>\n",
              "      <td>...</td>\n",
              "      <td>D</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>97.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>7/1/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>200</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>GBR</td>\n",
              "      <td>...</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>109.80</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>7/8/2015</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>119349</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>186</td>\n",
              "      <td>2017</td>\n",
              "      <td>August</td>\n",
              "      <td>31</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>DEU</td>\n",
              "      <td>...</td>\n",
              "      <td>D</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>126.00</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>9/3/2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119352</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>63</td>\n",
              "      <td>2017</td>\n",
              "      <td>August</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>SWE</td>\n",
              "      <td>...</td>\n",
              "      <td>D</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient-Party</td>\n",
              "      <td>195.33</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>9/3/2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119353</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>63</td>\n",
              "      <td>2017</td>\n",
              "      <td>August</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>SWE</td>\n",
              "      <td>...</td>\n",
              "      <td>D</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient-Party</td>\n",
              "      <td>195.33</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>9/3/2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119354</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>63</td>\n",
              "      <td>2017</td>\n",
              "      <td>August</td>\n",
              "      <td>31</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>SWE</td>\n",
              "      <td>...</td>\n",
              "      <td>D</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient-Party</td>\n",
              "      <td>195.33</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>9/3/2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119373</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>175</td>\n",
              "      <td>2017</td>\n",
              "      <td>August</td>\n",
              "      <td>31</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>NLD</td>\n",
              "      <td>...</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>82.35</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>9/4/2017</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>32090 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cb038ea9-ac6e-4e02-b5f1-f907b1c635c6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cb038ea9-ac6e-4e02-b5f1-f907b1c635c6 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-cb038ea9-ac6e-4e02-b5f1-f907b1c635c6');\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": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I found that there are around 32 thousand rows that are considered as complete duplicates.\n",
        "\n"
      ],
      "metadata": {
        "id": "i7UBgEODPQb5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df = df.drop_duplicates()"
      ],
      "metadata": {
        "id": "5p5Xp7Ax2Z56"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To check, i can verify that there would only be 87300 rows left in our dataframe `data`."
      ],
      "metadata": {
        "id": "48uXGsf-Rw2R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(df.shape)"
      ],
      "metadata": {
        "id": "KbuQBmFnR8JQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e649258e-71a3-4a8d-be7f-da245b83e330"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(87300, 24)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "assert(df.shape == (87300, 24))"
      ],
      "metadata": {
        "id": "rDfiVLpGSBiu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Task 4: Check the Columns containing Missing Values"
      ],
      "metadata": {
        "id": "fzoaiYEGS0_t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "num_missing_cols = df.isna().sum().sort_values(ascending=False)"
      ],
      "metadata": {
        "id": "V-AsJMCDS5bk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(num_missing_cols)"
      ],
      "metadata": {
        "id": "Q3Vf5gtnup8o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d347ac46-5b96-4771-9a51-298695b1a07c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "country                           451\n",
            "children                            4\n",
            "hotel                               0\n",
            "reserved_room_type                  0\n",
            "status                              0\n",
            "special_requests                    0\n",
            "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",
            "previous_bookings_not_canceled      0\n",
            "lead_time                           0\n",
            "previous_cancellations              0\n",
            "is_repeated_guest                   0\n",
            "meal                                0\n",
            "babies                              0\n",
            "adults                              0\n",
            "arrival_date_day_of_month           0\n",
            "arrival_date_month                  0\n",
            "arrival_date_year                   0\n",
            "status_last_update                  0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "assert(type(num_missing_cols) == pd.Series)\n",
        "assert(num_missing_cols.shape == (24,))\n",
        "assert(all(num_missing_cols[:3] == [451,4,0]))"
      ],
      "metadata": {
        "id": "NtJr5Wr5ukXb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "i can see in our counts that both columns `country` and `children` have missing values."
      ],
      "metadata": {
        "id": "flUqiDiV9hqf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Task 5: Clean the `country` and `children` column by providing a default value\n",
        "\n"
      ],
      "metadata": {
        "id": "h5ZeEA4gws0C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df[df['country'].isna()].head()"
      ],
      "metadata": {
        "id": "kF3uFaIfyOsj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "de8cae99-a2c4-4af4-815a-91ba5f8b008c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             hotel  lead_time  arrival_date_year arrival_date_month  \\\n",
              "30    Resort Hotel        118               2015               July   \n",
              "4127  Resort Hotel          0               2016           February   \n",
              "7092  Resort Hotel          8               2016               July   \n",
              "7860  Resort Hotel         39               2016             August   \n",
              "8779  Resort Hotel          0               2016            October   \n",
              "\n",
              "      arrival_date_day_of_month  adults  children  babies meal country  ...  \\\n",
              "30                            1       1       0.0       0   BB     NaN  ...   \n",
              "4127                         15       0       0.0       0   SC     NaN  ...   \n",
              "7092                         21       1       0.0       0   BB     NaN  ...   \n",
              "7860                         30       2       0.0       0   HB     NaN  ...   \n",
              "8779                         13       1       0.0       0   BB     NaN  ...   \n",
              "\n",
              "      assigned_room_type  booking_changes  deposit_type days_in_waiting_list  \\\n",
              "30                     A                2    No Deposit                    0   \n",
              "4127                   P                0    No Deposit                    0   \n",
              "7092                   A                0    No Deposit                    0   \n",
              "7860                   A                0    No Deposit                    0   \n",
              "8779                   A                0    No Deposit                    0   \n",
              "\n",
              "     customer_type    adr car_parking_spaces  special_requests     status  \\\n",
              "30       Transient   62.0                  0                 2  Check-Out   \n",
              "4127     Transient    0.0                  0                 0   Canceled   \n",
              "7092     Transient   73.0                  0                 2   Canceled   \n",
              "7860     Transient  159.0                  0                 5   Canceled   \n",
              "8779     Transient   50.0                  0                 0   Canceled   \n",
              "\n",
              "      status_last_update  \n",
              "30             7/15/2015  \n",
              "4127           2/15/2016  \n",
              "7092           7/20/2016  \n",
              "7860           7/22/2016  \n",
              "8779          10/13/2016  \n",
              "\n",
              "[5 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8da2390e-b0b0-465a-8f8d-65fb7824e1db\">\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>lead_time</th>\n",
              "      <th>arrival_date_year</th>\n",
              "      <th>arrival_date_month</th>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <th>adults</th>\n",
              "      <th>children</th>\n",
              "      <th>babies</th>\n",
              "      <th>meal</th>\n",
              "      <th>country</th>\n",
              "      <th>...</th>\n",
              "      <th>assigned_room_type</th>\n",
              "      <th>booking_changes</th>\n",
              "      <th>deposit_type</th>\n",
              "      <th>days_in_waiting_list</th>\n",
              "      <th>customer_type</th>\n",
              "      <th>adr</th>\n",
              "      <th>car_parking_spaces</th>\n",
              "      <th>special_requests</th>\n",
              "      <th>status</th>\n",
              "      <th>status_last_update</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>118</td>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Check-Out</td>\n",
              "      <td>7/15/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4127</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>February</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>SC</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>P</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>2/15/2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7092</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>8</td>\n",
              "      <td>2016</td>\n",
              "      <td>July</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>73.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>7/20/2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7860</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>39</td>\n",
              "      <td>2016</td>\n",
              "      <td>August</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>HB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>159.0</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>7/22/2016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8779</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>2016</td>\n",
              "      <td>October</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>A</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient</td>\n",
              "      <td>50.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>10/13/2016</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8da2390e-b0b0-465a-8f8d-65fb7824e1db')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8da2390e-b0b0-465a-8f8d-65fb7824e1db 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-8da2390e-b0b0-465a-8f8d-65fb7824e1db');\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": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df[df['children'].isna()].head()"
      ],
      "metadata": {
        "id": "siuflgcY89LS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "outputId": "b692cebc-1173-4482-ad15-d3d9c96d471a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            hotel  lead_time  arrival_date_year arrival_date_month  \\\n",
              "40600  City Hotel          2               2015             August   \n",
              "40667  City Hotel          1               2015             August   \n",
              "40679  City Hotel          1               2015             August   \n",
              "41160  City Hotel          8               2015             August   \n",
              "\n",
              "       arrival_date_day_of_month  adults  children  babies meal country  ...  \\\n",
              "40600                          3       2       NaN       0   BB     PRT  ...   \n",
              "40667                          5       2       NaN       0   BB     PRT  ...   \n",
              "40679                          5       3       NaN       0   BB     PRT  ...   \n",
              "41160                         13       2       NaN       0   BB     PRT  ...   \n",
              "\n",
              "       assigned_room_type  booking_changes  deposit_type days_in_waiting_list  \\\n",
              "40600                   B                0    No Deposit                    0   \n",
              "40667                   B                0    No Deposit                    0   \n",
              "40679                   B                0    No Deposit                    0   \n",
              "41160                   B                0    No Deposit                    0   \n",
              "\n",
              "         customer_type   adr car_parking_spaces  special_requests    status  \\\n",
              "40600  Transient-Party  12.0                  0                 1  Canceled   \n",
              "40667  Transient-Party  12.0                  0                 1  Canceled   \n",
              "40679  Transient-Party  18.0                  0                 2  Canceled   \n",
              "41160  Transient-Party  76.5                  0                 1  Canceled   \n",
              "\n",
              "       status_last_update  \n",
              "40600            8/1/2015  \n",
              "40667            8/4/2015  \n",
              "40679            8/4/2015  \n",
              "41160            8/9/2015  \n",
              "\n",
              "[4 rows x 24 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c4890c07-44e1-439f-ae37-3f0c064ac18f\">\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>lead_time</th>\n",
              "      <th>arrival_date_year</th>\n",
              "      <th>arrival_date_month</th>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <th>adults</th>\n",
              "      <th>children</th>\n",
              "      <th>babies</th>\n",
              "      <th>meal</th>\n",
              "      <th>country</th>\n",
              "      <th>...</th>\n",
              "      <th>assigned_room_type</th>\n",
              "      <th>booking_changes</th>\n",
              "      <th>deposit_type</th>\n",
              "      <th>days_in_waiting_list</th>\n",
              "      <th>customer_type</th>\n",
              "      <th>adr</th>\n",
              "      <th>car_parking_spaces</th>\n",
              "      <th>special_requests</th>\n",
              "      <th>status</th>\n",
              "      <th>status_last_update</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>40600</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>2015</td>\n",
              "      <td>August</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>PRT</td>\n",
              "      <td>...</td>\n",
              "      <td>B</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient-Party</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>8/1/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40667</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>2015</td>\n",
              "      <td>August</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>PRT</td>\n",
              "      <td>...</td>\n",
              "      <td>B</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient-Party</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>8/4/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40679</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>2015</td>\n",
              "      <td>August</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>PRT</td>\n",
              "      <td>...</td>\n",
              "      <td>B</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient-Party</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>8/4/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41160</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>8</td>\n",
              "      <td>2015</td>\n",
              "      <td>August</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>BB</td>\n",
              "      <td>PRT</td>\n",
              "      <td>...</td>\n",
              "      <td>B</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>Transient-Party</td>\n",
              "      <td>76.5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Canceled</td>\n",
              "      <td>8/9/2015</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4890c07-44e1-439f-ae37-3f0c064ac18f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c4890c07-44e1-439f-ae37-3f0c064ac18f 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-c4890c07-44e1-439f-ae37-3f0c064ac18f');\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": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "i can see that there are some rows have missing values indicated as `NaN`.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "jTOIcYS49T3b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df['country'].fillna('Other', inplace=True)"
      ],
      "metadata": {
        "id": "pHnC-03Ex4WY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "On the other hand, there are missing values in the `children` column."
      ],
      "metadata": {
        "id": "1rOtE4TU-5VH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df['children'].fillna(0, inplace=True)"
      ],
      "metadata": {
        "id": "2A4XYS91-4_q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "to verify the progress, i'll going to initially check them using `assert` statement."
      ],
      "metadata": {
        "id": "eOUOmleeAOd4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "assert(all(df[['country','children']].isna().sum() == 0))"
      ],
      "metadata": {
        "id": "VqesR0rtyzgV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Task 6: Clean the date-related columns by creating a column with datetime data type."
      ],
      "metadata": {
        "id": "k9J7rKfZxFFb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "cols_to_use = ['arrival_date_year','arrival_date_month','arrival_date_day_of_month', 'status_last_update']\n",
        "\n",
        "df[cols_to_use].head()"
      ],
      "metadata": {
        "id": "t0JEi72WB3Va",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9c2edc12-dfce-4372-c8ea-53c7a5b4ddf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   arrival_date_year arrival_date_month  arrival_date_day_of_month  \\\n",
              "0               2015               July                          1   \n",
              "1               2015               July                          1   \n",
              "2               2015               July                          1   \n",
              "3               2015               July                          1   \n",
              "4               2015               July                          1   \n",
              "\n",
              "  status_last_update  \n",
              "0           7/1/2015  \n",
              "1           7/1/2015  \n",
              "2           7/2/2015  \n",
              "3           7/2/2015  \n",
              "4           7/3/2015  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-353d488c-b576-4769-b14e-288759d46dc9\">\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>arrival_date_year</th>\n",
              "      <th>arrival_date_month</th>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <th>status_last_update</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>1</td>\n",
              "      <td>7/1/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>1</td>\n",
              "      <td>7/1/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>1</td>\n",
              "      <td>7/2/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>1</td>\n",
              "      <td>7/2/2015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015</td>\n",
              "      <td>July</td>\n",
              "      <td>1</td>\n",
              "      <td>7/3/2015</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-353d488c-b576-4769-b14e-288759d46dc9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-353d488c-b576-4769-b14e-288759d46dc9 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-353d488c-b576-4769-b14e-288759d46dc9');\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": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df[cols_to_use].info()"
      ],
      "metadata": {
        "id": "wwB8DHzYCF1j",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d998c4d-be03-4dc4-9002-51249ddd4503"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 87300 entries, 0 to 119389\n",
            "Data columns (total 4 columns):\n",
            " #   Column                     Non-Null Count  Dtype \n",
            "---  ------                     --------------  ----- \n",
            " 0   arrival_date_year          87300 non-null  int64 \n",
            " 1   arrival_date_month         87300 non-null  object\n",
            " 2   arrival_date_day_of_month  87300 non-null  int64 \n",
            " 3   status_last_update         87300 non-null  object\n",
            "dtypes: int64(2), object(2)\n",
            "memory usage: 3.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "i can observe that are three columns that defines the arrival date of a particular guest.\n",
        "\n",
        "My objective here is to first convert these columns arrival date columns to a string, and concatenate them. Afterwards, convert this concatenated column along with `status_last_update` to a `datetime` data type."
      ],
      "metadata": {
        "id": "nZHKSoABCI8y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**First, convert the year and the day columns indicated as `arrival_date_year` and `arrival_date_day_of_month` respectively to a string column `str`.**"
      ],
      "metadata": {
        "id": "rxsgII_L143H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df[['arrival_date_year','arrival_date_day_of_month']] = df[['arrival_date_year','arrival_date_day_of_month']].astype('str')"
      ],
      "metadata": {
        "id": "x8VDc_OO1z2m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**After converting them to `str`, we merge the three columns into a single column named `arrival_date` following the format `YYYY-Month-DD`**\n",
        "\n",
        "Examples: `2015-July-1`, `2017-August-30`"
      ],
      "metadata": {
        "id": "WNNKgR8c2eqV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "col_arrival_dates = ['arrival_date_year', 'arrival_date_month', 'arrival_date_day_of_month']\n",
        "df['arrival_date'] = df[col_arrival_dates].apply( lambda x: '/'.join(x),axis=1)\n",
        "\n"
      ],
      "metadata": {
        "id": "uHgdA4jpzUWV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(df['arrival_date'])"
      ],
      "metadata": {
        "id": "uaXJPZszIceu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fefd9b7c-1155-41a2-9f4c-cae9c9e201de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0            2015/July/1\n",
            "1            2015/July/1\n",
            "2            2015/July/1\n",
            "3            2015/July/1\n",
            "4            2015/July/1\n",
            "               ...      \n",
            "119385    2017/August/30\n",
            "119386    2017/August/31\n",
            "119387    2017/August/31\n",
            "119388    2017/August/31\n",
            "119389    2017/August/29\n",
            "Name: arrival_date, Length: 87300, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "assert(pd.api.types.is_string_dtype(df['arrival_date']))\n",
        "assert(all(df['arrival_date'].apply(lambda x: x.count('/')) == 2))\n",
        "assert(df['arrival_date'].isna().sum() == 0)\n",
        "assert(df['arrival_date'].shape == (87300,))"
      ],
      "metadata": {
        "id": "A9hHmshbGUx7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Next, convert the `arrival_date` and `status_last_update` column into a datetime data type using `pd.to_datetime`**"
      ],
      "metadata": {
        "id": "klRKSrxz391W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df['arrival_date'] = pd.to_datetime(df['arrival_date'])\n",
        "df['status_last_update'] = pd.to_datetime(df['status_last_update'])"
      ],
      "metadata": {
        "id": "1hBDVWjI2h0D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Finally, remove the columns listed in `col_arrival_dates` in the dataframe `data`.**"
      ],
      "metadata": {
        "id": "Myvx_V8W_OEf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "col_arrival_dates = ['arrival_date_year', 'arrival_date_month', 'arrival_date_day_of_month']\n",
        "df.drop(col_arrival_dates, axis=1, inplace=True)\n"
      ],
      "metadata": {
        "id": "lCola6fv-6h_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "assert(df.shape == (87300, 22))\n",
        "assert(pd.api.types.is_datetime64_dtype(df['arrival_date']))\n",
        "assert(pd.api.types.is_datetime64_dtype(df['status_last_update']))"
      ],
      "metadata": {
        "id": "EelmUjkuU4Iy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Task 7: Checking for Outliers\n",
        "\n"
      ],
      "metadata": {
        "id": "il0fS8EGiDAa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Show the summary statistics of the column `adr` in the dataframe `data`, and store the results into a variable `adr_summary`.**"
      ],
      "metadata": {
        "id": "Ftcjb7Pbnbg9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "adr_summary = df['adr'].describe()"
      ],
      "metadata": {
        "id": "hpWtFhsZnYcR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(adr_summary)"
      ],
      "metadata": {
        "id": "uVvRSZ_bogqu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96c676a6-1ed5-44f0-d03d-318ff7da97d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "count    87300.000000\n",
            "mean       106.366698\n",
            "std         55.023163\n",
            "min         -6.380000\n",
            "25%         72.000000\n",
            "50%         98.100000\n",
            "75%        134.000000\n",
            "max       5400.000000\n",
            "Name: adr, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "check if we got it correctly using `assert`"
      ],
      "metadata": {
        "id": "cWgszUAbp2M0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "assert(all(adr_summary.index == pd.Index(['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'])))\n",
        "assert(adr_summary.shape == (8,))"
      ],
      "metadata": {
        "id": "G2rs1dhqpHo5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking at the numbers, this wouldn't depict the outliers properly. What I can do here is to show a plot instead."
      ],
      "metadata": {
        "id": "r3Rv_1CFobP4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,6))\n",
        "ax = df['adr'].plot(kind='hist', bins=30)\n",
        "\n",
        "ax.set_title('Histogram of Hotel Guest ADR')\n",
        "ax.set_yscale('log')"
      ],
      "metadata": {
        "id": "28uoBNoWh2Rl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "outputId": "385afcd6-1fb6-444d-bbbe-818c7bb544c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking into the results, i can see that there's a lone data point in the 5000 range, and it wouldn't make sense to have an `adr` that high, so we check on the raw data by sorting the values in `adr`\n",
        "\n"
      ],
      "metadata": {
        "id": "_W8GkUFl0Q94"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df['adr'].sort_values(ascending=False)"
      ],
      "metadata": {
        "id": "ISLoNqHA0mzc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "294d15c0-35ec-4b17-e8b5-e6cd4bf6f1f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "48515     5400.00\n",
              "111403     510.00\n",
              "15083      508.00\n",
              "103912     451.50\n",
              "13142      450.00\n",
              "           ...   \n",
              "74720        0.00\n",
              "74721        0.00\n",
              "1230         0.00\n",
              "0            0.00\n",
              "14969       -6.38\n",
              "Name: adr, Length: 87300, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "i can observe that there's one data that is very high compared to the rest. Another thing is that  lowest value is a negative number, so i remove it as well in our dataset.\n",
        "\n",
        "Remove the rows that contains an `adr` of `5400` and `-6.38`"
      ],
      "metadata": {
        "id": "qVMKlI0I0tYU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df = df[(df['adr'] != 5400) & (df['adr'] != -6.38)]\n"
      ],
      "metadata": {
        "id": "uXXXgE_f0sD1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df['adr'].sort_values(ascending=False)"
      ],
      "metadata": {
        "id": "GtLg33MO15gW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "06cd7bc0-19e9-4333-aa5c-4b8ebf54dd41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "111403    510.0\n",
              "15083     508.0\n",
              "103912    451.5\n",
              "13142     450.0\n",
              "13391     437.0\n",
              "          ...  \n",
              "32633       0.0\n",
              "97751       0.0\n",
              "83900       0.0\n",
              "97752       0.0\n",
              "0           0.0\n",
              "Name: adr, Length: 87298, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To verify the progress, i would be running some `assert` statements."
      ],
      "metadata": {
        "id": "uexCxU8R3n5r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "assert(df[df['adr'].isin([5400, -6.38])].shape == (0,22))\n",
        "assert(df['adr'].min() == 0)\n",
        "assert(df['adr'].max() == 510)"
      ],
      "metadata": {
        "id": "4vjzdQBa2cLn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Analysis and Insights"
      ],
      "metadata": {
        "id": "-u37L-y_2QQS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Task 8: Show the amount lost from cancelled bookings\n",
        "\n"
      ],
      "metadata": {
        "id": "_Pdg1aV6sx50"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "amount_plotdata = df[df['status'] != 'No-Show']"
      ],
      "metadata": {
        "id": "PJYOyieCvO9p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, create a seaborn barplot that uses the data `amount_plotdata`, `x` with `'status'`, `y` with `'adr'`, and `hue` with `'deposit_type'`.\n",
        "\n",
        "Label the chart title `Average Daily Rate of Hotel Bookings`, the x label with `Booking Status`, y label with `Average Daily Rate`, and the legend title with `Deposit Type`."
      ],
      "metadata": {
        "id": "kGcsE2xf3QAl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,5))\n",
        "sns.barplot(data=amount_plotdata, x='status', y='adr',\n",
        "             hue='deposit_type',ax=ax)\n",
        "\n",
        "ax.set_title('Average Daily Rate of Hotel Bookings')\n",
        "ax.set_xlabel('Booking Status')\n",
        "ax.set_ylabel('Average Daily Rate')\n",
        "ax.legend(title='Deposit Type')"
      ],
      "metadata": {
        "id": "MLBz28GEvz6M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "outputId": "278d670c-04a1-417d-e64b-4b462ddf94c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fddd65db970>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lastly, I'll show the summary statistics using the column `adr` grouped by `status` and `deposit_type`."
      ],
      "metadata": {
        "id": "qH3JrTIJ3xb9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "amount_plotdata.groupby(['status','deposit_type'])['adr'].describe()"
      ],
      "metadata": {
        "id": "5fVCPA84wuIh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "fb6b2869-4656-4a24-91a6-8b88d381aa0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          count        mean        std   min   25%     50%  \\\n",
              "status    deposit_type                                                       \n",
              "Canceled  No Deposit    21970.0  119.770062  52.301414   0.0  85.0  112.50   \n",
              "          Non Refund      973.0   87.634111  33.255428   6.0  62.0   85.00   \n",
              "          Refundable       25.0  115.838000  60.326292   8.0  76.0  108.36   \n",
              "Check-Out No Deposit    63183.0  102.088206  51.415440   0.0  68.0   94.80   \n",
              "          Non Refund       55.0   80.687818  38.075381   6.0  54.5   75.00   \n",
              "          Refundable       81.0   69.239259  12.794604  34.0  66.0   66.00   \n",
              "\n",
              "                           75%    max  \n",
              "status    deposit_type                 \n",
              "Canceled  No Deposit    147.00  450.0  \n",
              "          Non Refund    106.00  330.0  \n",
              "          Refundable    158.33  230.0  \n",
              "Check-Out No Deposit    129.49  510.0  \n",
              "          Non Refund    106.00  245.0  \n",
              "          Refundable     72.00  128.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1a499c8e-b240-40f0-bd42-56e3bd84bbf7\">\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></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>status</th>\n",
              "      <th>deposit_type</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Canceled</th>\n",
              "      <th>No Deposit</th>\n",
              "      <td>21970.0</td>\n",
              "      <td>119.770062</td>\n",
              "      <td>52.301414</td>\n",
              "      <td>0.0</td>\n",
              "      <td>85.0</td>\n",
              "      <td>112.50</td>\n",
              "      <td>147.00</td>\n",
              "      <td>450.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Non Refund</th>\n",
              "      <td>973.0</td>\n",
              "      <td>87.634111</td>\n",
              "      <td>33.255428</td>\n",
              "      <td>6.0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>85.00</td>\n",
              "      <td>106.00</td>\n",
              "      <td>330.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Refundable</th>\n",
              "      <td>25.0</td>\n",
              "      <td>115.838000</td>\n",
              "      <td>60.326292</td>\n",
              "      <td>8.0</td>\n",
              "      <td>76.0</td>\n",
              "      <td>108.36</td>\n",
              "      <td>158.33</td>\n",
              "      <td>230.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">Check-Out</th>\n",
              "      <th>No Deposit</th>\n",
              "      <td>63183.0</td>\n",
              "      <td>102.088206</td>\n",
              "      <td>51.415440</td>\n",
              "      <td>0.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>94.80</td>\n",
              "      <td>129.49</td>\n",
              "      <td>510.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Non Refund</th>\n",
              "      <td>55.0</td>\n",
              "      <td>80.687818</td>\n",
              "      <td>38.075381</td>\n",
              "      <td>6.0</td>\n",
              "      <td>54.5</td>\n",
              "      <td>75.00</td>\n",
              "      <td>106.00</td>\n",
              "      <td>245.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Refundable</th>\n",
              "      <td>81.0</td>\n",
              "      <td>69.239259</td>\n",
              "      <td>12.794604</td>\n",
              "      <td>34.0</td>\n",
              "      <td>66.0</td>\n",
              "      <td>66.00</td>\n",
              "      <td>72.00</td>\n",
              "      <td>128.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1a499c8e-b240-40f0-bd42-56e3bd84bbf7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1a499c8e-b240-40f0-bd42-56e3bd84bbf7 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-1a499c8e-b240-40f0-bd42-56e3bd84bbf7');\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": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Task 9: Use `.groupby()` method to tally the cancellations and compute the cancellation rates per day"
      ],
      "metadata": {
        "id": "DUneDojFKZ0q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Create three new columns named `arrival_day`, `arrival_month`, and `arrival_year` that uses `arrival_date` which gets the day, month, and year respectively.**"
      ],
      "metadata": {
        "id": "02dLFUlJ4uYs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df['arrival_year'] = df['arrival_date'].dt.year\n",
        "df['arrival_month'] = df['arrival_date'].dt.month\n",
        "df['arrival_day'] = df['arrival_date'].dt.day"
      ],
      "metadata": {
        "id": "4FXBnGenM7-3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Next, i create a new column named `is_canceled` that contains boolean values whether the column `status` has the value `Canceled` or not.**"
      ],
      "metadata": {
        "id": "ffC4pib35UDg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['status'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UATh3iOKmrDC",
        "outputId": "6e03b7cb-fe54-4ae4-bc67-520b2a0e7571"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Check-Out', 'Canceled', 'No-Show'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df['is_canceled'] = df['status'] == 'Canceled'"
      ],
      "metadata": {
        "id": "ZvN1oI7ONc6A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**i then use `.groupby()` method to group the given arrival columns and find the `sum` and `count` of cancelled bookings and store the results to a variable named `canceled_data`**"
      ],
      "metadata": {
        "id": "EdPysaRx5h-9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "grouping_cols = ['arrival_year','arrival_month','arrival_day']\n",
        "canceled_data = df.groupby(grouping_cols)['is_canceled'].agg(['sum','count']).reset_index()\n",
        "\n"
      ],
      "metadata": {
        "id": "yws0mu5MM1Ni"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "i then create a new column named `cancel_rate` that is computed by dividing the columns `sum` and `count` from the dataframe `canceled_data`\n",
        "\n",
        "```\n",
        "cancel_rate = column_of_sum / column_of_count\n",
        "```"
      ],
      "metadata": {
        "id": "iQGyE1nk5upM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df['cancel_rate'] = canceled_data['sum'] / canceled_data['count']"
      ],
      "metadata": {
        "id": "TRikh0_ONav4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "canceled_data.head()"
      ],
      "metadata": {
        "id": "2WJEMe-LOE49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "7e1215d4-b4b8-46ed-8076-52f58c9d3c61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   arrival_year  arrival_month  arrival_day  sum  count\n",
              "0          2015              7            1   11     53\n",
              "1          2015              7            2   18     54\n",
              "2          2015              7            3   19     51\n",
              "3          2015              7            4   18     60\n",
              "4          2015              7            5   14     51"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b07dd676-789b-4a26-b4a7-506abad494bb\">\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>arrival_year</th>\n",
              "      <th>arrival_month</th>\n",
              "      <th>arrival_day</th>\n",
              "      <th>sum</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015</td>\n",
              "      <td>7</td>\n",
              "      <td>1</td>\n",
              "      <td>11</td>\n",
              "      <td>53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2015</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>19</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>18</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b07dd676-789b-4a26-b4a7-506abad494bb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b07dd676-789b-4a26-b4a7-506abad494bb 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-b07dd676-789b-4a26-b4a7-506abad494bb');\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": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Task 10: Show a line plot of hotel booking cancellation rate"
      ],
      "metadata": {
        "id": "Ik0k8j-Jl_yJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10,5))\n",
        "sns.lineplot(data=canceled_data, x='arrival_month', y=df['cancel_rate'],\n",
        "             hue='arrival_year', style='arrival_year',\n",
        "             markersize=10, palette='Dark2', ci=None,\n",
        "             markers=True, dashes=False,\n",
        "             ax=ax)\n",
        "\n",
        "ax.set_xticks(range(1,13))\n",
        "\n",
        "ax.set_title('Line Plot of Hotel Cancellation Rate Per Year')\n",
        "ax.set_xlabel('Month Number')\n",
        "ax.set_ylabel('Cancellation Rate')\n",
        "\n",
        "ax.legend(title='Year')"
      ],
      "metadata": {
        "id": "l5cOetKfahRL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "outputId": "ed6289ce-dde2-47e9-bd01-c331149d4995"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-44-c9547a4e696e>:2: FutureWarning: \n",
            "\n",
            "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
            "\n",
            "  sns.lineplot(data=canceled_data, x='arrival_month', y=df['cancel_rate'],\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fddd661b190>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Task 11: Use `.groupby()` method to tally the cancellations and compute the cancellation rates per deposit type and returning guest"
      ],
      "metadata": {
        "id": "_rNeHLjzm8fd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**i then use `.groupby()` method to group the given columns and find the `sum` and `count` of cancelled bookings and store the results to a variable named `pointplot_data`**"
      ],
      "metadata": {
        "id": "3xk_-cw58jf0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "grouping_cols = ['deposit_type','is_repeated_guest','arrival_date']\n",
        "pointplot_data = df.groupby(grouping_cols)['is_canceled'].agg(['sum','count']).reset_index()"
      ],
      "metadata": {
        "id": "0aPYBW3wV5vP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "i then create a new column named `cancel_rate` that is computed by dividing the columns `sum` and `count` from the dataframe `pointplot_data`\n",
        "\n",
        "```\n",
        "cancel_rate = column_of_sum / column_of_count\n",
        "```"
      ],
      "metadata": {
        "id": "GlMntyio8r_b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "pointplot_data['cancel_rate'] = pointplot_data['sum'] / pointplot_data['count']\n"
      ],
      "metadata": {
        "id": "WlQMbWBiZpHf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then, i convert the column of `is_repeated_guest` to a data type `bool`."
      ],
      "metadata": {
        "id": "B30tNqo28ziR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df['is_repeated_guest'] = df['is_repeated_guest'].astype(bool)"
      ],
      "metadata": {
        "id": "xZs4w7e_qf7c"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "pointplot_data.head()"
      ],
      "metadata": {
        "id": "9EeNwMFy9bJk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "199c6ed4-8e12-495b-f98e-6194b2a78024"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  deposit_type  is_repeated_guest arrival_date  sum  count  cancel_rate\n",
              "0   No Deposit                  0   2015-07-01   11     52     0.211538\n",
              "1   No Deposit                  0   2015-07-02   15     51     0.294118\n",
              "2   No Deposit                  0   2015-07-03   19     51     0.372549\n",
              "3   No Deposit                  0   2015-07-04   17     59     0.288136\n",
              "4   No Deposit                  0   2015-07-05   13     50     0.260000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-033a9e46-682c-49ed-82ee-2a1ddaf5d8bf\">\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>deposit_type</th>\n",
              "      <th>is_repeated_guest</th>\n",
              "      <th>arrival_date</th>\n",
              "      <th>sum</th>\n",
              "      <th>count</th>\n",
              "      <th>cancel_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>2015-07-01</td>\n",
              "      <td>11</td>\n",
              "      <td>52</td>\n",
              "      <td>0.211538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>2015-07-02</td>\n",
              "      <td>15</td>\n",
              "      <td>51</td>\n",
              "      <td>0.294118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>2015-07-03</td>\n",
              "      <td>19</td>\n",
              "      <td>51</td>\n",
              "      <td>0.372549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>2015-07-04</td>\n",
              "      <td>17</td>\n",
              "      <td>59</td>\n",
              "      <td>0.288136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>No Deposit</td>\n",
              "      <td>0</td>\n",
              "      <td>2015-07-05</td>\n",
              "      <td>13</td>\n",
              "      <td>50</td>\n",
              "      <td>0.260000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-033a9e46-682c-49ed-82ee-2a1ddaf5d8bf')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-033a9e46-682c-49ed-82ee-2a1ddaf5d8bf 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-033a9e46-682c-49ed-82ee-2a1ddaf5d8bf');\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": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Task 12: Show a Pointplot of Cancellation Rates by Deposit Type"
      ],
      "metadata": {
        "id": "bVI55Dw1X8Rf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fig, ax = plt.subplots(figsize=(7,8))\n",
        "sns.pointplot(data=pointplot_data, x='deposit_type', y='cancel_rate',\n",
        "               hue='is_repeated_guest', join=False,\n",
        "               capsize=0.1, ax=ax)\n",
        "\n",
        "ax.set_xlabel('Deposit Type')\n",
        "ax.set_ylabel('Cancellation Rate')\n",
        "ax.set_title('Cancellation Rates by Deposity type and Repeating Guest ')\n",
        "ax.legend(title='Returning Guest')"
      ],
      "metadata": {
        "id": "dI0tnO9rgLG4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 735
        },
        "outputId": "1f3ad898-faf5-4e73-ccaa-412a535ac558"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fddd661ab90>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from matplotlib.widgets import Button\n",
        "\n",
        "# Create the subplots for the dashboard\n",
        "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(20, 18))\n",
        "\n",
        "# Plot 1: Histogram of Hotel Guest ADR\n",
        "ax1 = axes[0, 0]\n",
        "ax1.hist(df['adr'], bins=30)\n",
        "ax1.set_title('Histogram of Hotel Guest ADR')\n",
        "ax1.set_yscale('log')\n",
        "ax1.set_facecolor('#F5EFE7')\n",
        "\n",
        "# Plot 2: Average Daily Rate of Hotel Bookings\n",
        "ax2 = axes[0, 1]\n",
        "sns.barplot(data=amount_plotdata, x='status', y='adr', hue='deposit_type', ax=ax2)\n",
        "ax2.set_title('Average Daily Rate of Hotel Bookings')\n",
        "ax2.set_xlabel('Booking Status')\n",
        "ax2.set_ylabel('Average Daily Rate')\n",
        "ax2.legend(title='Deposit Type')\n",
        "ax2.set_facecolor('#F5EFE7')\n",
        "\n",
        "# Plot 3: Line Plot of Hotel Cancellation Rate Per Year\n",
        "ax3 = axes[1, 0]\n",
        "sns.lineplot(data=canceled_data, x='arrival_month', y=df['cancel_rate'],\n",
        "             hue='arrival_year', style='arrival_year',\n",
        "             markersize=10, palette='Dark2', ci=None,\n",
        "             markers=True, dashes=False,\n",
        "             ax=ax3)\n",
        "ax3.set_xticks(range(1, 13))\n",
        "ax3.set_title('Line Plot of Hotel Cancellation Rate Per Year')\n",
        "ax3.set_xlabel('Month Number')\n",
        "ax3.set_ylabel('Cancellation Rate')\n",
        "ax3.legend(title='Year')\n",
        "ax3.set_facecolor('#F5EFE7')\n",
        "\n",
        "# Plot 4: Cancellation Rates by Deposit Type and Repeating Guest\n",
        "ax4 = axes[1, 1]\n",
        "sns.pointplot(data=pointplot_data, x='deposit_type', y='cancel_rate',\n",
        "              hue='is_repeated_guest', join=False,\n",
        "              capsize=0.1, ax=ax4)\n",
        "ax4.set_xlabel('Deposit Type')\n",
        "ax4.set_ylabel('Cancellation Rate')\n",
        "ax4.set_title('Cancellation Rates by Deposit Type and Repeating Guest')\n",
        "ax4.legend(title='Returning Guest')\n",
        "ax4.set_facecolor('#F5EFE7')\n",
        "\n",
        "# Create the title at the top\n",
        "figtext_kwargs = {'x': 0.5, 'y': 0.95, 's': 'ANALYZING HOTEL BOOKING CANCELLATION', 'fontsize': 25, 'fontweight': 'bold', 'ha': 'center',\n",
        "                  'bbox': {'boxstyle': 'round', 'facecolor': 'white', 'edgecolor': 'black', 'pad': 0.1}}\n",
        "fig.text(**figtext_kwargs)\n",
        "\n",
        "# Add a background color to the entire dashboard\n",
        "fig.patch.set_facecolor('#C2DEDC')\n",
        "\n",
        "# Adjust the layout and spacing of the subplots\n",
        "plt.tight_layout(rect=[0, 0.1, 1, 0.95])\n",
        "\n",
        "# Save the dashboard as a PNG image\n",
        "plt.savefig('dashboard.png')\n",
        "\n",
        "# Display the interactive dashboard\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Ipn92P3bQT_w",
        "outputId": "b7f788f6-187e-403f-b558-5f0ca465157a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-50-148a64bc437a>:26: FutureWarning: \n",
            "\n",
            "The `ci` parameter is deprecated. Use `errorbar=None` for the same effect.\n",
            "\n",
            "  sns.lineplot(data=canceled_data, x='arrival_month', y=df['cancel_rate'],\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1800 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conclusions and Recommendations"
      ],
      "metadata": {
        "id": "FhXng66greVE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can conclude that being a repeated guest does not have a significant impact on the likelihood of a booking being canceled.\n",
        "\n",
        "As for the recommendation:\n",
        "\n",
        "The hotel should continue to treat all guests equally regardless of whether they are repeated or non-repeated guests.\n",
        "\n",
        "The hotel should also focus on improving the overall booking experience for all guests, including the ease of cancellation, to reduce the likelihood of cancellations.\n",
        "\n",
        "Additionally, the hotel can consider implementing policies or incentives to encourage guests to rebook with them in the future.\n",
        "\n"
      ],
      "metadata": {
        "id": "vF5WnzyOzAg9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "**END OF FILE**"
      ],
      "metadata": {
        "id": "9A7GXLBfzFmf"
      }
    }
  ]
}