investigate_hotel_business_using_data_visualization.ipynb

{
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    {
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      "id": "2dc85d41-ab0d-4800-a474-964db9367f58",
      "metadata": {
        "id": "2dc85d41-ab0d-4800-a474-964db9367f58"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9bcbaa52-5fb7-4ca8-aa19-b75061b3fe06",
      "metadata": {
        "id": "9bcbaa52-5fb7-4ca8-aa19-b75061b3fe06"
      },
      "outputs": [],
      "source": [
        "pd.set_option('display.max_columns',None)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9cb3a92e-1598-4fa4-a54c-142c5a09599a",
      "metadata": {
        "id": "9cb3a92e-1598-4fa4-a54c-142c5a09599a"
      },
      "source": [
        "# Load Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b45ea2d2-9045-446f-9032-731c11924880",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              hotel  is_canceled  lead_time  arrival_date_year  \\\n",
              "75055    City Hotel            1        271               2017   \n",
              "91867    City Hotel            0        192               2018   \n",
              "63518    City Hotel            1         12               2019   \n",
              "35714  Resort Hotel            0         94               2019   \n",
              "22063  Resort Hotel            0         47               2018   \n",
              "\n",
              "      arrival_date_month  arrival_date_week_number  arrival_date_day_of_month  \\\n",
              "75055          September                        29                         15   \n",
              "91867             August                        26                         24   \n",
              "63518              April                         7                         13   \n",
              "35714               June                        17                         23   \n",
              "22063                May                        10                          3   \n",
              "\n",
              "       stays_in_weekend_nights  stays_in_weekdays_nights  adults  children  \\\n",
              "75055                        0                         2       2       0.0   \n",
              "91867                        0                         2       1       0.0   \n",
              "63518                        1                         0       2       0.0   \n",
              "35714                        2                         5       2       0.0   \n",
              "22063                        4                        10       2       0.0   \n",
              "\n",
              "       babies       meal              city market_segment  \\\n",
              "75055       0  Breakfast     Kota Denpasar         Groups   \n",
              "91867       0  Breakfast     Kota Denpasar  Offline TA/TO   \n",
              "63518       0     Dinner     Kota Denpasar         Direct   \n",
              "35714       0     Dinner  Kabupaten Bangka  Offline TA/TO   \n",
              "22063       0  Breakfast       Kota Malang  Offline TA/TO   \n",
              "\n",
              "      distribution_channel  is_repeated_guest  previous_cancellations  \\\n",
              "75055                TA/TO                  0                       1   \n",
              "91867                TA/TO                  0                       0   \n",
              "63518               Direct                  0                       0   \n",
              "35714                TA/TO                  0                       0   \n",
              "22063                TA/TO                  0                       0   \n",
              "\n",
              "       previous_bookings_not_canceled  booking_changes deposit_type  agent  \\\n",
              "75055                               0                0   Non Refund    1.0   \n",
              "91867                               0                0   No Deposit   34.0   \n",
              "63518                               0                0   No Deposit   14.0   \n",
              "35714                               0                0   No Deposit   40.0   \n",
              "22063                               0                0   No Deposit    8.0   \n",
              "\n",
              "       company  days_in_waiting_list customer_type     adr  \\\n",
              "75055      NaN                     0        Family   62.00   \n",
              "91867      NaN                     0        Family   95.00   \n",
              "63518      NaN                     0      Personal  143.00   \n",
              "35714      NaN                     0      Contract   70.95   \n",
              "22063      NaN                     0      Personal   26.10   \n",
              "\n",
              "       required_car_parking_spaces  total_of_special_requests  \\\n",
              "75055                            0                          0   \n",
              "91867                            0                          0   \n",
              "63518                            0                          2   \n",
              "35714                            0                          0   \n",
              "22063                            0                          0   \n",
              "\n",
              "      reservation_status  \n",
              "75055           Canceled  \n",
              "91867          Check-Out  \n",
              "63518           Canceled  \n",
              "35714          Check-Out  \n",
              "22063          Check-Out  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>hotel</th>\n",
              "      <th>is_canceled</th>\n",
              "      <th>lead_time</th>\n",
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              "      <th>market_segment</th>\n",
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              "      <th>is_repeated_guest</th>\n",
              "      <th>previous_cancellations</th>\n",
              "      <th>previous_bookings_not_canceled</th>\n",
              "      <th>booking_changes</th>\n",
              "      <th>deposit_type</th>\n",
              "      <th>agent</th>\n",
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              "      <th>customer_type</th>\n",
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              "      <td>City Hotel</td>\n",
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              "      <td>2017</td>\n",
              "      <td>September</td>\n",
              "      <td>29</td>\n",
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              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Breakfast</td>\n",
              "      <td>Kota Denpasar</td>\n",
              "      <td>Groups</td>\n",
              "      <td>TA/TO</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Non Refund</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Family</td>\n",
              "      <td>62.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Canceled</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91867</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>192</td>\n",
              "      <td>2018</td>\n",
              "      <td>August</td>\n",
              "      <td>26</td>\n",
              "      <td>24</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Breakfast</td>\n",
              "      <td>Kota Denpasar</td>\n",
              "      <td>Offline TA/TO</td>\n",
              "      <td>TA/TO</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>34.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Family</td>\n",
              "      <td>95.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Check-Out</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63518</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>2019</td>\n",
              "      <td>April</td>\n",
              "      <td>7</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>Dinner</td>\n",
              "      <td>Kota Denpasar</td>\n",
              "      <td>Direct</td>\n",
              "      <td>Direct</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>14.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>Personal</td>\n",
              "      <td>143.00</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Canceled</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35714</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>94</td>\n",
              "      <td>2019</td>\n",
              "      <td>June</td>\n",
              "      <td>17</td>\n",
              "      <td>23</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>Kabupaten Bangka</td>\n",
              "      <td>Offline TA/TO</td>\n",
              "      <td>TA/TO</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>No Deposit</td>\n",
              "      <td>40.0</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>Contract</td>\n",
              "      <td>70.95</td>\n",
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              "      <td>Resort Hotel</td>\n",
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              "      <td>47</td>\n",
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              "      <td>No Deposit</td>\n",
              "      <td>8.0</td>\n",
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              "      <td>26.10</td>\n",
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              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6669a614-03d9-4789-b678-51195a4f5118 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df = pd.read_csv('hotel_bookings_data.csv')\n",
        "df.sample(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "27334892-b4db-4657-b532-52f97a03c4ca",
      "metadata": {
        "id": "27334892-b4db-4657-b532-52f97a03c4ca"
      },
      "source": [
        "# Data Checking"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8fa70591-1f78-49a5-ba4d-840de6a069aa",
      "metadata": {
        "id": "8fa70591-1f78-49a5-ba4d-840de6a069aa",
        "outputId": "d6016a26-7b2e-42a5-d40c-45f9e582d423",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 119390 entries, 0 to 119389\n",
            "Data columns (total 29 columns):\n",
            " #   Column                          Non-Null Count   Dtype  \n",
            "---  ------                          --------------   -----  \n",
            " 0   hotel                           119390 non-null  object \n",
            " 1   is_canceled                     119390 non-null  int64  \n",
            " 2   lead_time                       119390 non-null  int64  \n",
            " 3   arrival_date_year               119390 non-null  int64  \n",
            " 4   arrival_date_month              119390 non-null  object \n",
            " 5   arrival_date_week_number        119390 non-null  int64  \n",
            " 6   arrival_date_day_of_month       119390 non-null  int64  \n",
            " 7   stays_in_weekend_nights         119390 non-null  int64  \n",
            " 8   stays_in_weekdays_nights        119390 non-null  int64  \n",
            " 9   adults                          119390 non-null  int64  \n",
            " 10  children                        119386 non-null  float64\n",
            " 11  babies                          119390 non-null  int64  \n",
            " 12  meal                            119390 non-null  object \n",
            " 13  city                            118902 non-null  object \n",
            " 14  market_segment                  119390 non-null  object \n",
            " 15  distribution_channel            119390 non-null  object \n",
            " 16  is_repeated_guest               119390 non-null  int64  \n",
            " 17  previous_cancellations          119390 non-null  int64  \n",
            " 18  previous_bookings_not_canceled  119390 non-null  int64  \n",
            " 19  booking_changes                 119390 non-null  int64  \n",
            " 20  deposit_type                    119390 non-null  object \n",
            " 21  agent                           103050 non-null  float64\n",
            " 22  company                         6797 non-null    float64\n",
            " 23  days_in_waiting_list            119390 non-null  int64  \n",
            " 24  customer_type                   119390 non-null  object \n",
            " 25  adr                             119390 non-null  float64\n",
            " 26  required_car_parking_spaces     119390 non-null  int64  \n",
            " 27  total_of_special_requests       119390 non-null  int64  \n",
            " 28  reservation_status              119390 non-null  object \n",
            "dtypes: float64(4), int64(16), object(9)\n",
            "memory usage: 26.4+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2e7ef56c-1b88-4837-ae78-f504e37c6cbb",
      "metadata": {
        "id": "2e7ef56c-1b88-4837-ae78-f504e37c6cbb",
        "outputId": "d1eb7917-e573-4d4c-dbca-55db126e812c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         is_canceled      lead_time  arrival_date_year  \\\n",
              "count  119390.000000  119390.000000      119390.000000   \n",
              "mean        0.370416     104.011416        2018.156554   \n",
              "std         0.482918     106.863097           0.707476   \n",
              "min         0.000000       0.000000        2017.000000   \n",
              "25%         0.000000      18.000000        2018.000000   \n",
              "50%         0.000000      69.000000        2018.000000   \n",
              "75%         1.000000     160.000000        2019.000000   \n",
              "max         1.000000     737.000000        2019.000000   \n",
              "\n",
              "       arrival_date_week_number  arrival_date_day_of_month  \\\n",
              "count             119390.000000              119390.000000   \n",
              "mean                  27.165173                  15.798241   \n",
              "std                   13.605138                   8.780829   \n",
              "min                    1.000000                   1.000000   \n",
              "25%                   16.000000                   8.000000   \n",
              "50%                   28.000000                  16.000000   \n",
              "75%                   38.000000                  23.000000   \n",
              "max                   53.000000                  31.000000   \n",
              "\n",
              "       stays_in_weekend_nights  stays_in_weekdays_nights         adults  \\\n",
              "count            119390.000000             119390.000000  119390.000000   \n",
              "mean                  0.927599                  2.500302       1.856403   \n",
              "std                   0.998613                  1.908286       0.579261   \n",
              "min                   0.000000                  0.000000       0.000000   \n",
              "25%                   0.000000                  1.000000       2.000000   \n",
              "50%                   1.000000                  2.000000       2.000000   \n",
              "75%                   2.000000                  3.000000       2.000000   \n",
              "max                  19.000000                 50.000000      55.000000   \n",
              "\n",
              "            children         babies  is_repeated_guest  \\\n",
              "count  119386.000000  119390.000000      119390.000000   \n",
              "mean        0.103890       0.007949           0.031912   \n",
              "std         0.398561       0.097436           0.175767   \n",
              "min         0.000000       0.000000           0.000000   \n",
              "25%         0.000000       0.000000           0.000000   \n",
              "50%         0.000000       0.000000           0.000000   \n",
              "75%         0.000000       0.000000           0.000000   \n",
              "max        10.000000      10.000000           1.000000   \n",
              "\n",
              "       previous_cancellations  previous_bookings_not_canceled  \\\n",
              "count           119390.000000                   119390.000000   \n",
              "mean                 0.087118                        0.137097   \n",
              "std                  0.844336                        1.497437   \n",
              "min                  0.000000                        0.000000   \n",
              "25%                  0.000000                        0.000000   \n",
              "50%                  0.000000                        0.000000   \n",
              "75%                  0.000000                        0.000000   \n",
              "max                 26.000000                       72.000000   \n",
              "\n",
              "       booking_changes          agent      company  days_in_waiting_list  \\\n",
              "count    119390.000000  103050.000000  6797.000000         119390.000000   \n",
              "mean          0.221124      86.693382   189.266735              2.321149   \n",
              "std           0.652306     110.774548   131.655015             17.594721   \n",
              "min           0.000000       1.000000     6.000000              0.000000   \n",
              "25%           0.000000       9.000000    62.000000              0.000000   \n",
              "50%           0.000000      14.000000   179.000000              0.000000   \n",
              "75%           0.000000     229.000000   270.000000              0.000000   \n",
              "max          21.000000     535.000000   543.000000            391.000000   \n",
              "\n",
              "                 adr  required_car_parking_spaces  total_of_special_requests  \n",
              "count  119390.000000                119390.000000              119390.000000  \n",
              "mean      101.831122                     0.062518                   0.571363  \n",
              "std        50.535790                     0.245291                   0.792798  \n",
              "min        -6.380000                     0.000000                   0.000000  \n",
              "25%        69.290000                     0.000000                   0.000000  \n",
              "50%        94.575000                     0.000000                   0.000000  \n",
              "75%       126.000000                     0.000000                   1.000000  \n",
              "max      5400.000000                     8.000000                   5.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-80c189e4-4de2-4692-8853-4451b082811b\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>is_canceled</th>\n",
              "      <th>lead_time</th>\n",
              "      <th>arrival_date_year</th>\n",
              "      <th>arrival_date_week_number</th>\n",
              "      <th>arrival_date_day_of_month</th>\n",
              "      <th>stays_in_weekend_nights</th>\n",
              "      <th>stays_in_weekdays_nights</th>\n",
              "      <th>adults</th>\n",
              "      <th>children</th>\n",
              "      <th>babies</th>\n",
              "      <th>is_repeated_guest</th>\n",
              "      <th>previous_cancellations</th>\n",
              "      <th>previous_bookings_not_canceled</th>\n",
              "      <th>booking_changes</th>\n",
              "      <th>agent</th>\n",
              "      <th>company</th>\n",
              "      <th>days_in_waiting_list</th>\n",
              "      <th>adr</th>\n",
              "      <th>required_car_parking_spaces</th>\n",
              "      <th>total_of_special_requests</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
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              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
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              "      <td>119390.000000</td>\n",
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              "      <td>119386.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
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              "      <td>6797.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "      <td>119390.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.370416</td>\n",
              "      <td>104.011416</td>\n",
              "      <td>2018.156554</td>\n",
              "      <td>27.165173</td>\n",
              "      <td>15.798241</td>\n",
              "      <td>0.927599</td>\n",
              "      <td>2.500302</td>\n",
              "      <td>1.856403</td>\n",
              "      <td>0.103890</td>\n",
              "      <td>0.007949</td>\n",
              "      <td>0.031912</td>\n",
              "      <td>0.087118</td>\n",
              "      <td>0.137097</td>\n",
              "      <td>0.221124</td>\n",
              "      <td>86.693382</td>\n",
              "      <td>189.266735</td>\n",
              "      <td>2.321149</td>\n",
              "      <td>101.831122</td>\n",
              "      <td>0.062518</td>\n",
              "      <td>0.571363</td>\n",
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              "      <th>std</th>\n",
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              "      <td>106.863097</td>\n",
              "      <td>0.707476</td>\n",
              "      <td>13.605138</td>\n",
              "      <td>8.780829</td>\n",
              "      <td>0.998613</td>\n",
              "      <td>1.908286</td>\n",
              "      <td>0.579261</td>\n",
              "      <td>0.398561</td>\n",
              "      <td>0.097436</td>\n",
              "      <td>0.175767</td>\n",
              "      <td>0.844336</td>\n",
              "      <td>1.497437</td>\n",
              "      <td>0.652306</td>\n",
              "      <td>110.774548</td>\n",
              "      <td>131.655015</td>\n",
              "      <td>17.594721</td>\n",
              "      <td>50.535790</td>\n",
              "      <td>0.245291</td>\n",
              "      <td>0.792798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>2017.000000</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-6.380000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <td>18.000000</td>\n",
              "      <td>2018.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>8.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>9.000000</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>69.290000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>2018.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>16.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>179.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.575000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-af3ee3f3-8f5e-468d-bd6b-128dcc39eb9d')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-af3ee3f3-8f5e-468d-bd6b-128dcc39eb9d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0c37e7d9-fce8-4fe6-a234-0632423cb456",
      "metadata": {
        "id": "0c37e7d9-fce8-4fe6-a234-0632423cb456",
        "outputId": "56602148-fe5d-40a9-b8a7-b69496417ad9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 959
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            column data_type  no._null  percent_null  \\\n",
              "0                            hotel    object         0      0.000000   \n",
              "1                      is_canceled     int64         0      0.000000   \n",
              "2                        lead_time     int64         0      0.000000   \n",
              "3                arrival_date_year     int64         0      0.000000   \n",
              "4               arrival_date_month    object         0      0.000000   \n",
              "5         arrival_date_week_number     int64         0      0.000000   \n",
              "6        arrival_date_day_of_month     int64         0      0.000000   \n",
              "7          stays_in_weekend_nights     int64         0      0.000000   \n",
              "8         stays_in_weekdays_nights     int64         0      0.000000   \n",
              "9                           adults     int64         0      0.000000   \n",
              "10                        children   float64         4      0.003350   \n",
              "11                          babies     int64         0      0.000000   \n",
              "12                            meal    object         0      0.000000   \n",
              "13                            city    object       488      0.408744   \n",
              "14                  market_segment    object         0      0.000000   \n",
              "15            distribution_channel    object         0      0.000000   \n",
              "16               is_repeated_guest     int64         0      0.000000   \n",
              "17          previous_cancellations     int64         0      0.000000   \n",
              "18  previous_bookings_not_canceled     int64         0      0.000000   \n",
              "19                 booking_changes     int64         0      0.000000   \n",
              "20                    deposit_type    object         0      0.000000   \n",
              "21                           agent   float64     16340     13.686238   \n",
              "22                         company   float64    112593     94.306893   \n",
              "23            days_in_waiting_list     int64         0      0.000000   \n",
              "24                   customer_type    object         0      0.000000   \n",
              "25                             adr   float64         0      0.000000   \n",
              "26     required_car_parking_spaces     int64         0      0.000000   \n",
              "27       total_of_special_requests     int64         0      0.000000   \n",
              "28              reservation_status    object         0      0.000000   \n",
              "\n",
              "    no._unique                                      unique_sample  \n",
              "0            2                         [Resort Hotel, City Hotel]  \n",
              "1            2                                             [0, 1]  \n",
              "2          479                              [342, 737, 7, 13, 14]  \n",
              "3            3                                 [2017, 2018, 2019]  \n",
              "4           12  [September, October, November, December, January]  \n",
              "5           53                               [27, 28, 29, 30, 31]  \n",
              "6           31                                    [1, 2, 3, 4, 5]  \n",
              "7           17                                    [0, 1, 2, 4, 3]  \n",
              "8           35                                    [0, 1, 2, 3, 4]  \n",
              "9           14                                   [2, 1, 3, 4, 40]  \n",
              "10           5                         [0.0, 1.0, 2.0, 10.0, 3.0]  \n",
              "11           5                                   [0, 1, 2, 10, 9]  \n",
              "12           5  [Breakfast, Full Board, Dinner, No Meal, Undef...  \n",
              "13         177  [Kota Denpasar, Kabupaten Bangka, Kabupaten Sl...  \n",
              "14           8  [Direct, Corporate, Online TA, Offline TA/TO, ...  \n",
              "15           5         [Direct, Corporate, TA/TO, Undefined, GDS]  \n",
              "16           2                                             [0, 1]  \n",
              "17          15                                   [0, 1, 2, 3, 26]  \n",
              "18          73                                    [0, 1, 2, 3, 4]  \n",
              "19          21                                    [3, 4, 0, 1, 2]  \n",
              "20           3               [No Deposit, Refundable, Non Refund]  \n",
              "21         333                   [nan, 304.0, 240.0, 303.0, 15.0]  \n",
              "22         352                  [nan, 110.0, 113.0, 270.0, 178.0]  \n",
              "23         128                               [0, 50, 47, 65, 122]  \n",
              "24           4            [Personal, Contract, Family, Bussiness]  \n",
              "25        8879                    [0.0, 75.0, 98.0, 107.0, 103.0]  \n",
              "26           5                                    [0, 1, 2, 8, 3]  \n",
              "27           6                                    [0, 1, 3, 2, 4]  \n",
              "28           3                     [Check-Out, Canceled, No-Show]  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8fe9bd58-83b4-459e-bd7a-a8b42935e56d\" 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>column</th>\n",
              "      <th>data_type</th>\n",
              "      <th>no._null</th>\n",
              "      <th>percent_null</th>\n",
              "      <th>no._unique</th>\n",
              "      <th>unique_sample</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>hotel</td>\n",
              "      <td>object</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>[Resort Hotel, City Hotel]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>is_canceled</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>[0, 1]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>lead_time</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>479</td>\n",
              "      <td>[342, 737, 7, 13, 14]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>arrival_date_year</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>[2017, 2018, 2019]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>arrival_date_month</td>\n",
              "      <td>object</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "      <td>[September, October, November, December, January]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>arrival_date_week_number</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>53</td>\n",
              "      <td>[27, 28, 29, 30, 31]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>arrival_date_day_of_month</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31</td>\n",
              "      <td>[1, 2, 3, 4, 5]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>stays_in_weekend_nights</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>17</td>\n",
              "      <td>[0, 1, 2, 4, 3]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>stays_in_weekdays_nights</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>35</td>\n",
              "      <td>[0, 1, 2, 3, 4]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>adults</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14</td>\n",
              "      <td>[2, 1, 3, 4, 40]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>children</td>\n",
              "      <td>float64</td>\n",
              "      <td>4</td>\n",
              "      <td>0.003350</td>\n",
              "      <td>5</td>\n",
              "      <td>[0.0, 1.0, 2.0, 10.0, 3.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>babies</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5</td>\n",
              "      <td>[0, 1, 2, 10, 9]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>meal</td>\n",
              "      <td>object</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5</td>\n",
              "      <td>[Breakfast, Full Board, Dinner, No Meal, Undef...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>city</td>\n",
              "      <td>object</td>\n",
              "      <td>488</td>\n",
              "      <td>0.408744</td>\n",
              "      <td>177</td>\n",
              "      <td>[Kota Denpasar, Kabupaten Bangka, Kabupaten Sl...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>market_segment</td>\n",
              "      <td>object</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8</td>\n",
              "      <td>[Direct, Corporate, Online TA, Offline TA/TO, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>distribution_channel</td>\n",
              "      <td>object</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5</td>\n",
              "      <td>[Direct, Corporate, TA/TO, Undefined, GDS]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>is_repeated_guest</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2</td>\n",
              "      <td>[0, 1]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>previous_cancellations</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15</td>\n",
              "      <td>[0, 1, 2, 3, 26]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>previous_bookings_not_canceled</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>73</td>\n",
              "      <td>[0, 1, 2, 3, 4]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>booking_changes</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>21</td>\n",
              "      <td>[3, 4, 0, 1, 2]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>deposit_type</td>\n",
              "      <td>object</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>[No Deposit, Refundable, Non Refund]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>agent</td>\n",
              "      <td>float64</td>\n",
              "      <td>16340</td>\n",
              "      <td>13.686238</td>\n",
              "      <td>333</td>\n",
              "      <td>[nan, 304.0, 240.0, 303.0, 15.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>company</td>\n",
              "      <td>float64</td>\n",
              "      <td>112593</td>\n",
              "      <td>94.306893</td>\n",
              "      <td>352</td>\n",
              "      <td>[nan, 110.0, 113.0, 270.0, 178.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>days_in_waiting_list</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>128</td>\n",
              "      <td>[0, 50, 47, 65, 122]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>customer_type</td>\n",
              "      <td>object</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4</td>\n",
              "      <td>[Personal, Contract, Family, Bussiness]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>adr</td>\n",
              "      <td>float64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8879</td>\n",
              "      <td>[0.0, 75.0, 98.0, 107.0, 103.0]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>required_car_parking_spaces</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5</td>\n",
              "      <td>[0, 1, 2, 8, 3]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>total_of_special_requests</td>\n",
              "      <td>int64</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6</td>\n",
              "      <td>[0, 1, 3, 2, 4]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>reservation_status</td>\n",
              "      <td>object</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3</td>\n",
              "      <td>[Check-Out, Canceled, No-Show]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8fe9bd58-83b4-459e-bd7a-a8b42935e56d')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-8fe9bd58-83b4-459e-bd7a-a8b42935e56d 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-8fe9bd58-83b4-459e-bd7a-a8b42935e56d');\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",
              "\n",
              "\n",
              "<div id=\"df-5f6e013c-9779-4074-8542-29658265b5eb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5f6e013c-9779-4074-8542-29658265b5eb')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5f6e013c-9779-4074-8542-29658265b5eb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "result = []\n",
        "for col in df.columns:\n",
        "    result.append([col, df[col].dtype, df[col].isna().sum(), 100*df[col].isna().sum()/len(df[col]), df[col].nunique(), df[col].unique()[:5]])\n",
        "\n",
        "output = pd.DataFrame(data=result, columns = 'column data_type no._null percent_null no._unique unique_sample'.split())\n",
        "output"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d338f5ff-d769-47c1-a282-5351712722bb",
      "metadata": {
        "id": "d338f5ff-d769-47c1-a282-5351712722bb"
      },
      "source": [
        "# Data Cleansing"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "35a5de28-1e89-49f0-b76d-16ccc645221c",
      "metadata": {
        "id": "35a5de28-1e89-49f0-b76d-16ccc645221c"
      },
      "source": [
        "## Null Values"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6dd32d00-52e6-4eab-9607-e8b219446474",
      "metadata": {
        "id": "6dd32d00-52e6-4eab-9607-e8b219446474"
      },
      "source": [
        "### Imputing null values in children column with the mode"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "44b8af27-6390-4a9b-9982-7bd71babbad0",
      "metadata": {
        "id": "44b8af27-6390-4a9b-9982-7bd71babbad0",
        "outputId": "77222f9a-263b-4dac-8cc5-71783604c17d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0     110796\n",
              "1.0       4861\n",
              "2.0       3652\n",
              "3.0         76\n",
              "10.0         1\n",
              "Name: children, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df['children'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8e0495a2-786b-45b5-836d-d3547c3abdd0",
      "metadata": {
        "id": "8e0495a2-786b-45b5-836d-d3547c3abdd0"
      },
      "outputs": [],
      "source": [
        "df.loc[df['children'].isna(), 'children'] = 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "71b076a1-f540-4d34-8e35-654ad4e87a1d",
      "metadata": {
        "id": "71b076a1-f540-4d34-8e35-654ad4e87a1d",
        "outputId": "072ce2ae-7d90-4586-94cf-4c4a724fc20c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df['children'].isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4e76f6ce-b93b-4449-95fa-76473d98ec26",
      "metadata": {
        "id": "4e76f6ce-b93b-4449-95fa-76473d98ec26"
      },
      "source": [
        "### Dropping rows with null cities"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0e350b30-845a-4f9e-8cc8-1af336bfbf35",
      "metadata": {
        "id": "0e350b30-845a-4f9e-8cc8-1af336bfbf35"
      },
      "outputs": [],
      "source": [
        "df = df.dropna(subset=['city'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a7500161-c914-49ae-a99e-9f8d8b675c45",
      "metadata": {
        "id": "a7500161-c914-49ae-a99e-9f8d8b675c45",
        "outputId": "ef3f8e38-431b-4cf5-ce3f-3a89c3cb32a5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df['city'].isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e2a57186-42e5-4fe8-98a7-65c6b3f9704a",
      "metadata": {
        "id": "e2a57186-42e5-4fe8-98a7-65c6b3f9704a"
      },
      "source": [
        "### Dropping the 'agent' and 'company' columns because of the high null value percentage"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "758f2aa0-03e0-4a0d-90c3-e438f4c748a6",
      "metadata": {
        "id": "758f2aa0-03e0-4a0d-90c3-e438f4c748a6"
      },
      "outputs": [],
      "source": [
        "df = df.drop(columns = ['agent', 'company'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c12770eb-1df3-4b1d-b028-bcb3f8b3f0af",
      "metadata": {
        "id": "c12770eb-1df3-4b1d-b028-bcb3f8b3f0af",
        "outputId": "a52afc5f-caa6-4cbc-a757-e9daed89a8ea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 118902 entries, 0 to 119389\n",
            "Data columns (total 27 columns):\n",
            " #   Column                          Non-Null Count   Dtype  \n",
            "---  ------                          --------------   -----  \n",
            " 0   hotel                           118902 non-null  object \n",
            " 1   is_canceled                     118902 non-null  int64  \n",
            " 2   lead_time                       118902 non-null  int64  \n",
            " 3   arrival_date_year               118902 non-null  int64  \n",
            " 4   arrival_date_month              118902 non-null  object \n",
            " 5   arrival_date_week_number        118902 non-null  int64  \n",
            " 6   arrival_date_day_of_month       118902 non-null  int64  \n",
            " 7   stays_in_weekend_nights         118902 non-null  int64  \n",
            " 8   stays_in_weekdays_nights        118902 non-null  int64  \n",
            " 9   adults                          118902 non-null  int64  \n",
            " 10  children                        118902 non-null  float64\n",
            " 11  babies                          118902 non-null  int64  \n",
            " 12  meal                            118902 non-null  object \n",
            " 13  city                            118902 non-null  object \n",
            " 14  market_segment                  118902 non-null  object \n",
            " 15  distribution_channel            118902 non-null  object \n",
            " 16  is_repeated_guest               118902 non-null  int64  \n",
            " 17  previous_cancellations          118902 non-null  int64  \n",
            " 18  previous_bookings_not_canceled  118902 non-null  int64  \n",
            " 19  booking_changes                 118902 non-null  int64  \n",
            " 20  deposit_type                    118902 non-null  object \n",
            " 21  days_in_waiting_list            118902 non-null  int64  \n",
            " 22  customer_type                   118902 non-null  object \n",
            " 23  adr                             118902 non-null  float64\n",
            " 24  required_car_parking_spaces     118902 non-null  int64  \n",
            " 25  total_of_special_requests       118902 non-null  int64  \n",
            " 26  reservation_status              118902 non-null  object \n",
            "dtypes: float64(2), int64(16), object(9)\n",
            "memory usage: 25.4+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "abfe5f7b-bb9a-4ea6-9f14-99901781394f",
      "metadata": {
        "id": "abfe5f7b-bb9a-4ea6-9f14-99901781394f"
      },
      "source": [
        "## Handling inappropriate values"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Checking for \"undefined\" values"
      ],
      "metadata": {
        "id": "fvMzMwV_Q1iJ"
      },
      "id": "fvMzMwV_Q1iJ"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "68f6dadf-f4a5-4341-a1f6-5e3b6cf7cc06",
      "metadata": {
        "id": "68f6dadf-f4a5-4341-a1f6-5e3b6cf7cc06",
        "outputId": "85c6bdcc-c1b7-4f66-c63d-61817754f02e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['meal: Undefined',\n",
              " 'market_segment: Undefined',\n",
              " 'distribution_channel: Undefined']"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "res=[]\n",
        "for col in df.columns:\n",
        "    if len(df[df[col] == 'Undefined'])>0:\n",
        "        res.append(f'{col}: Undefined')\n",
        "\n",
        "\n",
        "res"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "ee0ea768-2043-4d0b-a187-d45c288afd92",
      "metadata": {
        "id": "ee0ea768-2043-4d0b-a187-d45c288afd92"
      },
      "source": [
        "### Substituting inappropriate values in meal column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0ada4d49-15d3-433d-9af1-8ece2a22d021",
      "metadata": {
        "id": "0ada4d49-15d3-433d-9af1-8ece2a22d021",
        "outputId": "24ed8a46-de13-4488-d3fb-58ecacb6101c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Breakfast     91867\n",
              "Dinner        14434\n",
              "No Meal       10638\n",
              "Undefined      1165\n",
              "Full Board      798\n",
              "Name: meal, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "df['meal'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8aa5c7db-c104-483a-b271-fed658f335e8",
      "metadata": {
        "id": "8aa5c7db-c104-483a-b271-fed658f335e8"
      },
      "outputs": [],
      "source": [
        "df.loc[df['meal'] == 'Undefined', 'meal'] = 'Breakfast'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "66c45313-de0b-49c9-9655-d996a47b2dc3",
      "metadata": {
        "id": "66c45313-de0b-49c9-9655-d996a47b2dc3",
        "outputId": "2679b014-28c1-49c6-a087-2ae5d9715891",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Breakfast     93032\n",
              "Dinner        14434\n",
              "No Meal       10638\n",
              "Full Board      798\n",
              "Name: meal, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "df['meal'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3610b822-1620-4598-b264-9b1a8561eb66",
      "metadata": {
        "id": "3610b822-1620-4598-b264-9b1a8561eb66"
      },
      "source": [
        "### Substituting inappropriate values in distribution_channel column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9abfe5d2-3897-4473-b7a4-11bd7c87b125",
      "metadata": {
        "id": "9abfe5d2-3897-4473-b7a4-11bd7c87b125",
        "outputId": "9bc2ddf0-bad7-4145-c630-a4bb56e3f84c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TA/TO        97730\n",
              "Direct       14483\n",
              "Corporate     6491\n",
              "GDS            193\n",
              "Undefined        5\n",
              "Name: distribution_channel, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "df['distribution_channel'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "03370408-89ff-489e-9089-2bfc8686cf74",
      "metadata": {
        "id": "03370408-89ff-489e-9089-2bfc8686cf74"
      },
      "outputs": [],
      "source": [
        "df.loc[df['distribution_channel'] == 'Undefined', 'distribution_channel'] = 'TA/TO'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4f17b8dd-0007-436c-a4d1-57535b04adac",
      "metadata": {
        "id": "4f17b8dd-0007-436c-a4d1-57535b04adac",
        "outputId": "d1930130-efd1-439f-e7de-e0cec7c286e2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TA/TO        97735\n",
              "Direct       14483\n",
              "Corporate     6491\n",
              "GDS            193\n",
              "Name: distribution_channel, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df['distribution_channel'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8247083f-6022-4a41-b8cf-6c371d6b87f4",
      "metadata": {
        "id": "8247083f-6022-4a41-b8cf-6c371d6b87f4"
      },
      "source": [
        "### Dropping inappropriate values in market_segment column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "58c8bf35-9d28-427d-bd2a-2dd767306c7b",
      "metadata": {
        "id": "58c8bf35-9d28-427d-bd2a-2dd767306c7b",
        "outputId": "716814aa-4c1a-4814-8585-fe98ce309696",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Online TA        56403\n",
              "Offline TA/TO    24160\n",
              "Groups           19806\n",
              "Direct           12449\n",
              "Corporate         5111\n",
              "Complementary      734\n",
              "Aviation           237\n",
              "Undefined            2\n",
              "Name: market_segment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "df['market_segment'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "27d830cf-18e2-462a-a5ef-0f13cd588894",
      "metadata": {
        "id": "27d830cf-18e2-462a-a5ef-0f13cd588894"
      },
      "outputs": [],
      "source": [
        "df = df[(df['market_segment'] != 'Undefined')]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a74b7066-d7a0-492c-871a-6457012817fd",
      "metadata": {
        "id": "a74b7066-d7a0-492c-871a-6457012817fd",
        "outputId": "511a1f50-8ba8-470b-87b5-d3cf7075a0cb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Online TA        56403\n",
              "Offline TA/TO    24160\n",
              "Groups           19806\n",
              "Direct           12449\n",
              "Corporate         5111\n",
              "Complementary      734\n",
              "Aviation           237\n",
              "Name: market_segment, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "df['market_segment'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a010e5f0-ed86-4c2b-9b12-7f4ae2aef02e",
      "metadata": {
        "id": "a010e5f0-ed86-4c2b-9b12-7f4ae2aef02e"
      },
      "source": [
        "## Discarding unnecessary data"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6ff4c4ac-9eae-43a5-a64d-2b0955734148",
      "metadata": {
        "id": "6ff4c4ac-9eae-43a5-a64d-2b0955734148"
      },
      "source": [
        "### Aggregating the adults, children, and babies columns into a single total_guest column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6e8214e7-f5ea-46f1-be1b-c963fef64bb8",
      "metadata": {
        "id": "6e8214e7-f5ea-46f1-be1b-c963fef64bb8",
        "outputId": "e1a90fc9-3e74-4331-a8be-b5b86736699c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 118900 entries, 0 to 119389\n",
            "Data columns (total 25 columns):\n",
            " #   Column                          Non-Null Count   Dtype  \n",
            "---  ------                          --------------   -----  \n",
            " 0   hotel                           118900 non-null  object \n",
            " 1   is_canceled                     118900 non-null  int64  \n",
            " 2   lead_time                       118900 non-null  int64  \n",
            " 3   arrival_date_year               118900 non-null  int64  \n",
            " 4   arrival_date_month              118900 non-null  object \n",
            " 5   arrival_date_week_number        118900 non-null  int64  \n",
            " 6   arrival_date_day_of_month       118900 non-null  int64  \n",
            " 7   stays_in_weekend_nights         118900 non-null  int64  \n",
            " 8   stays_in_weekdays_nights        118900 non-null  int64  \n",
            " 9   meal                            118900 non-null  object \n",
            " 10  city                            118900 non-null  object \n",
            " 11  market_segment                  118900 non-null  object \n",
            " 12  distribution_channel            118900 non-null  object \n",
            " 13  is_repeated_guest               118900 non-null  int64  \n",
            " 14  previous_cancellations          118900 non-null  int64  \n",
            " 15  previous_bookings_not_canceled  118900 non-null  int64  \n",
            " 16  booking_changes                 118900 non-null  int64  \n",
            " 17  deposit_type                    118900 non-null  object \n",
            " 18  days_in_waiting_list            118900 non-null  int64  \n",
            " 19  customer_type                   118900 non-null  object \n",
            " 20  adr                             118900 non-null  float64\n",
            " 21  required_car_parking_spaces     118900 non-null  int64  \n",
            " 22  total_of_special_requests       118900 non-null  int64  \n",
            " 23  reservation_status              118900 non-null  object \n",
            " 24  total_guest                     118900 non-null  float64\n",
            "dtypes: float64(2), int64(14), object(9)\n",
            "memory usage: 23.6+ MB\n"
          ]
        }
      ],
      "source": [
        "df = df.copy()\n",
        "df['total_guest'] = df['adults'] + df['children'] + df['babies']\n",
        "df = df.drop(columns=['adults', 'children', 'babies'])\n",
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Monthly Hotel Booking Analysis Based on Hotel Type**"
      ],
      "metadata": {
        "id": "qHrUvwPOhV11"
      },
      "id": "qHrUvwPOhV11"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking the presence of the 12 months throughout the years (2017-2019)"
      ],
      "metadata": {
        "id": "BoGW-MZV07PG"
      },
      "id": "BoGW-MZV07PG"
    },
    {
      "cell_type": "code",
      "source": [
        "dfg = df.groupby(['arrival_date_month', 'hotel'])['arrival_date_year'].agg(['nunique', 'count']).reset_index()\n",
        "dfg.columns = ['month', 'hotel_type', 'unique_years', 'no._bookings']\n",
        "dfg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "pPLwNaTum0X-",
        "outputId": "b4328582-def0-49e4-aa16-0a4b72b8f102"
      },
      "id": "pPLwNaTum0X-",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        month    hotel_type  unique_years  no._bookings\n",
              "0       April    City Hotel             2          4965\n",
              "1       April  Resort Hotel             2          3047\n",
              "2      August    City Hotel             2          7894\n",
              "3      August  Resort Hotel             2          3033\n",
              "4    December    City Hotel             2          7591\n",
              "5    December  Resort Hotel             2          3504\n",
              "6    February    City Hotel             2          4129\n",
              "7    February  Resort Hotel             2          2599\n",
              "8     January    City Hotel             2          4354\n",
              "9     January  Resort Hotel             2          2398\n",
              "10       July    City Hotel             2          8232\n",
              "11       July  Resort Hotel             2          3547\n",
              "12       June    City Hotel             2          7476\n",
              "13       June  Resort Hotel             2          3569\n",
              "14      March    City Hotel             2          3736\n",
              "15      March  Resort Hotel             2          2138\n",
              "16        May    City Hotel             2          6458\n",
              "17        May  Resort Hotel             2          3281\n",
              "18   November    City Hotel             2          7400\n",
              "19   November  Resort Hotel             2          3067\n",
              "20    October    City Hotel             3          8981\n",
              "21    October  Resort Hotel             3          4873\n",
              "22  September    City Hotel             3          8088\n",
              "23  September  Resort Hotel             3          4540"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-62ea3278-6a80-46ed-bb7c-ea0b759fd78a\" class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>month</th>\n",
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              "      <th>0</th>\n",
              "      <td>April</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>4965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>April</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3047</td>\n",
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              "      <th>2</th>\n",
              "      <td>August</td>\n",
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              "      <td>7894</td>\n",
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              "    <tr>\n",
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              "      <td>August</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3033</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>December</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>7591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>December</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>February</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>4129</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>February</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>2599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>January</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>4354</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>January</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>2398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>July</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>8232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>July</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>June</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>7476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>June</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>March</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>March</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>2138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>May</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>6458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>May</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>November</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>7400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>November</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>October</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>3</td>\n",
              "      <td>8981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>October</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>3</td>\n",
              "      <td>4873</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>September</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>3</td>\n",
              "      <td>8088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>September</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>3</td>\n",
              "      <td>4540</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Normalizing No. bookings by taking the average throughout the years (2017-2019)"
      ],
      "metadata": {
        "id": "_kXBacMj2Jtl"
      },
      "id": "_kXBacMj2Jtl"
    },
    {
      "cell_type": "code",
      "source": [
        "dfg['avg_bookings'] = round(dfg['no._bookings']/dfg['unique_years'], 2)\n",
        "dfg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "YGGUqF2N2IA2",
        "outputId": "53c945d6-62f5-4e17-d3aa-a4d67ae8b9b6"
      },
      "id": "YGGUqF2N2IA2",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        month    hotel_type  unique_years  no._bookings  avg_bookings\n",
              "0       April    City Hotel             2          4965       2482.50\n",
              "1       April  Resort Hotel             2          3047       1523.50\n",
              "2      August    City Hotel             2          7894       3947.00\n",
              "3      August  Resort Hotel             2          3033       1516.50\n",
              "4    December    City Hotel             2          7591       3795.50\n",
              "5    December  Resort Hotel             2          3504       1752.00\n",
              "6    February    City Hotel             2          4129       2064.50\n",
              "7    February  Resort Hotel             2          2599       1299.50\n",
              "8     January    City Hotel             2          4354       2177.00\n",
              "9     January  Resort Hotel             2          2398       1199.00\n",
              "10       July    City Hotel             2          8232       4116.00\n",
              "11       July  Resort Hotel             2          3547       1773.50\n",
              "12       June    City Hotel             2          7476       3738.00\n",
              "13       June  Resort Hotel             2          3569       1784.50\n",
              "14      March    City Hotel             2          3736       1868.00\n",
              "15      March  Resort Hotel             2          2138       1069.00\n",
              "16        May    City Hotel             2          6458       3229.00\n",
              "17        May  Resort Hotel             2          3281       1640.50\n",
              "18   November    City Hotel             2          7400       3700.00\n",
              "19   November  Resort Hotel             2          3067       1533.50\n",
              "20    October    City Hotel             3          8981       2993.67\n",
              "21    October  Resort Hotel             3          4873       1624.33\n",
              "22  September    City Hotel             3          8088       2696.00\n",
              "23  September  Resort Hotel             3          4540       1513.33"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-47abfb31-b786-450a-833c-58f499c7987d\" class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>month</th>\n",
              "      <th>hotel_type</th>\n",
              "      <th>unique_years</th>\n",
              "      <th>no._bookings</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>April</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>4965</td>\n",
              "      <td>2482.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>April</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3047</td>\n",
              "      <td>1523.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>August</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>7894</td>\n",
              "      <td>3947.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>August</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3033</td>\n",
              "      <td>1516.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>December</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>7591</td>\n",
              "      <td>3795.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>December</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3504</td>\n",
              "      <td>1752.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>February</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>4129</td>\n",
              "      <td>2064.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>February</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>2599</td>\n",
              "      <td>1299.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>January</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>4354</td>\n",
              "      <td>2177.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>January</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>2398</td>\n",
              "      <td>1199.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>July</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>8232</td>\n",
              "      <td>4116.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>July</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3547</td>\n",
              "      <td>1773.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>June</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>7476</td>\n",
              "      <td>3738.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>June</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3569</td>\n",
              "      <td>1784.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>March</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3736</td>\n",
              "      <td>1868.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>March</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>2138</td>\n",
              "      <td>1069.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>May</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>6458</td>\n",
              "      <td>3229.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>May</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3281</td>\n",
              "      <td>1640.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>November</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>7400</td>\n",
              "      <td>3700.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>November</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3067</td>\n",
              "      <td>1533.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>October</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>3</td>\n",
              "      <td>8981</td>\n",
              "      <td>2993.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>October</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>3</td>\n",
              "      <td>4873</td>\n",
              "      <td>1624.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>September</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>3</td>\n",
              "      <td>8088</td>\n",
              "      <td>2696.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>September</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>3</td>\n",
              "      <td>4540</td>\n",
              "      <td>1513.33</td>\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-0de3ffee-7760-4f76-92fc-39c76a55f2f3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sorting the data based on month name"
      ],
      "metadata": {
        "id": "Sz087KGN4Md4"
      },
      "id": "Sz087KGN4Md4"
    },
    {
      "cell_type": "code",
      "source": [
        "months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
        "\n",
        "dfg['month'] = dfg['month'].str[:3]\n",
        "dfg['month'] = pd.Categorical(dfg['month'], categories = months, ordered=True)\n",
        "dfg = dfg.sort_values(['hotel_type', 'month']).reset_index(drop=True)\n",
        "\n",
        "dfg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "id": "Vw9_KuMc4VyZ",
        "outputId": "a32acc6f-8543-4882-d767-736f4e4031ab"
      },
      "id": "Vw9_KuMc4VyZ",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   month    hotel_type  unique_years  no._bookings  avg_bookings\n",
              "0    Jan    City Hotel             2          4354       2177.00\n",
              "1    Feb    City Hotel             2          4129       2064.50\n",
              "2    Mar    City Hotel             2          3736       1868.00\n",
              "3    Apr    City Hotel             2          4965       2482.50\n",
              "4    May    City Hotel             2          6458       3229.00\n",
              "5    Jun    City Hotel             2          7476       3738.00\n",
              "6    Jul    City Hotel             2          8232       4116.00\n",
              "7    Aug    City Hotel             2          7894       3947.00\n",
              "8    Sep    City Hotel             3          8088       2696.00\n",
              "9    Oct    City Hotel             3          8981       2993.67\n",
              "10   Nov    City Hotel             2          7400       3700.00\n",
              "11   Dec    City Hotel             2          7591       3795.50\n",
              "12   Jan  Resort Hotel             2          2398       1199.00\n",
              "13   Feb  Resort Hotel             2          2599       1299.50\n",
              "14   Mar  Resort Hotel             2          2138       1069.00\n",
              "15   Apr  Resort Hotel             2          3047       1523.50\n",
              "16   May  Resort Hotel             2          3281       1640.50\n",
              "17   Jun  Resort Hotel             2          3569       1784.50\n",
              "18   Jul  Resort Hotel             2          3547       1773.50\n",
              "19   Aug  Resort Hotel             2          3033       1516.50\n",
              "20   Sep  Resort Hotel             3          4540       1513.33\n",
              "21   Oct  Resort Hotel             3          4873       1624.33\n",
              "22   Nov  Resort Hotel             2          3067       1533.50\n",
              "23   Dec  Resort Hotel             2          3504       1752.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-64c6ba1e-f579-4b87-b598-43fdbaeed417\" 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>month</th>\n",
              "      <th>hotel_type</th>\n",
              "      <th>unique_years</th>\n",
              "      <th>no._bookings</th>\n",
              "      <th>avg_bookings</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Jan</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>4354</td>\n",
              "      <td>2177.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Feb</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>4129</td>\n",
              "      <td>2064.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Mar</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3736</td>\n",
              "      <td>1868.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Apr</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>4965</td>\n",
              "      <td>2482.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>May</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>6458</td>\n",
              "      <td>3229.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Jun</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>7476</td>\n",
              "      <td>3738.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Jul</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>8232</td>\n",
              "      <td>4116.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Aug</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>7894</td>\n",
              "      <td>3947.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Sep</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>3</td>\n",
              "      <td>8088</td>\n",
              "      <td>2696.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Oct</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>3</td>\n",
              "      <td>8981</td>\n",
              "      <td>2993.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Nov</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>7400</td>\n",
              "      <td>3700.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Dec</td>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>7591</td>\n",
              "      <td>3795.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Jan</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>2398</td>\n",
              "      <td>1199.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Feb</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>2599</td>\n",
              "      <td>1299.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Mar</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>2138</td>\n",
              "      <td>1069.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Apr</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3047</td>\n",
              "      <td>1523.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>May</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3281</td>\n",
              "      <td>1640.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Jun</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3569</td>\n",
              "      <td>1784.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Jul</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3547</td>\n",
              "      <td>1773.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Aug</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3033</td>\n",
              "      <td>1516.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Sep</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>3</td>\n",
              "      <td>4540</td>\n",
              "      <td>1513.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Oct</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>3</td>\n",
              "      <td>4873</td>\n",
              "      <td>1624.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Nov</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3067</td>\n",
              "      <td>1533.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Dec</td>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>3504</td>\n",
              "      <td>1752.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-12da3974-f0b2-4ead-bc20-78076fda98b1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing the monthly booking based on hotel type data"
      ],
      "metadata": {
        "id": "U8D0ckzq7AL9"
      },
      "id": "U8D0ckzq7AL9"
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Creating the plot\n",
        "plt.figure(figsize = (15, 6))\n",
        "plt.style.use('fivethirtyeight')\n",
        "fig = sns.lineplot(x='month', y = 'avg_bookings', hue = 'hotel_type', palette=['DeepSkyBlue', '#005f5f'], data = dfg)\n",
        "\n",
        "plt.axvline(x = -0.15, color = 'black', linewidth = 1.2, alpha = .7)\n",
        "plt.axhline(y = 20, color = 'black', linewidth = 1.3, alpha = .7)\n",
        "plt.ylim(0, 4500)\n",
        "plt.xlim(-0.2, 11.2)\n",
        "\n",
        "# Creating shaded areas\n",
        "plt.fill_between(dfg.index, y1=dfg.avg_bookings, y2=0, where=((dfg.index >= 4)&(dfg.index <= 6)) | ((dfg.index >= 9)&(dfg.index <= 11)), color='#adacac',\n",
        "                 alpha=0.5, interpolate=False)\n",
        "\n",
        "# Adding title and text\n",
        "fig.text(x = 2.65, y = 5900, s = \"Holiday season = Hotel season\",\n",
        "               fontsize = 27, weight = 'bold', alpha = .85)\n",
        "fig.text(x = 0.65, y = 5450,\n",
        "               s = 'Both hotel types reach high booking numbers in peak holiday season',\n",
        "              fontsize = 23, alpha = .85)\n",
        "fig.text(x = 1.03, y = 5100,\n",
        "               s = 'Both types tend to slump during off-season especially in march',\n",
        "              fontsize = 23, alpha = .85)\n",
        "fig.text(x = 2.2, y = 4700,\n",
        "               s = 'Average number of bookings per month by hotel type',\n",
        "              fontsize = 20, alpha = .5)\n",
        "# Adding annotations\n",
        "ax = plt.gca()\n",
        "ax.annotate(xy=(4.8,3750), xytext=(3.3, 4200), text='Eid al-Fitr &\\nSchool Holiday', fontsize=16,\n",
        "                arrowprops=dict(ec='black',\n",
        "                                arrowstyle= '->', mutation_scale = 15, linewidth = 2.0))\n",
        "ax.annotate(xy=(9.9,3650), xytext=(7.8, 3800), text='Christmas &\\nNew Year Holiday', fontsize=16,\n",
        "                arrowprops=dict(ec='black',\n",
        "                                arrowstyle= '->', mutation_scale = 15, linewidth = 2.0))\n",
        "\n",
        "# Setting axis labels\n",
        "plt.xlabel('Month', fontweight='bold')\n",
        "plt.ylabel('Average Number of Bookings', weight='bold')\n",
        "\n",
        "# Setting legend\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 754
        },
        "id": "HljeZ__B7gfW",
        "outputId": "0c728728-375e-4b65-ec65-62b05ff498e5"
      },
      "id": "HljeZ__B7gfW",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Analysis and interpretation**\n",
        "- **Seasonal Fluctuations:** Both City and Resort Hotels experience fluctuations in bookings throughout the year, which is common in the hospitality industry. It appears that the mid-year months, particularly June and July, have the highest average bookings for both hotel types. Conversely, the early-year months, including January through March, see relatively lower average bookings.\n",
        "- **Eid al-Fitr Impact:** It is essential to consider that Eid al-Fitr, a significant religious holiday in Indonesia, fell in June in the years 2017-2019. The surge in June-July bookings for both hotel types could be attributed to travelers returning to their hometowns to celebrate Eid al-Fitr with their families.\n",
        "- **Year-End Peak:** There is a noticeable surge in average bookings for both City and Resort Hotels in November and December. This surge suggests increased demand during the holiday season, including **Christmas** and **New Year's**, as travelers seek accommodations for festive celebrations and vacations.\n",
        "- **Academic Year:** Students in Indonesia usually commence their academic year in August or September. The decline in bookings observed in August and September for both hotel types may be associated with students and their families returning to school or work.\n",
        "- **City Hotels vs. Resort Hotels:** Comparing the two types of hotels, City Hotels generally maintain higher average bookings year-round when compared to Resort Hotels. This could be attributed to the urban location and the lower rates offered by City Hotels, making them more accessible and affordable to both business and leisure travelers.\n",
        "- **City Hotels vs. Resort Hotels (Part 2):** As stated in the above, more travelers book City Hotels in general. This could be the reason as to why the decline in Resort Hotels’ bookings was not as sharp as it was with City Hotels. Since Resort Hotels didn’t attract as many travelers, it didn’t peak as high, thus making the decline in bookings less sharp."
      ],
      "metadata": {
        "id": "6t707eiDCE5y"
      },
      "id": "6t707eiDCE5y"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Impact analysis of Stay Duration on Hotel Bookings Cancellation Rates**"
      ],
      "metadata": {
        "id": "8hdUXQTNRoKG"
      },
      "id": "8hdUXQTNRoKG"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Constructing new column for total stay duration"
      ],
      "metadata": {
        "id": "YG0YyDOfUdi1"
      },
      "id": "YG0YyDOfUdi1"
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.copy()\n",
        "df['stay_duration'] = df['stays_in_weekend_nights'] + df['stays_in_weekdays_nights']\n",
        "df.info()"
      ],
      "metadata": {
        "id": "6CYq5wmvs2RY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "027c5af5-e513-4e38-c500-38c9c15ea3af"
      },
      "id": "6CYq5wmvs2RY",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 118900 entries, 0 to 119389\n",
            "Data columns (total 26 columns):\n",
            " #   Column                          Non-Null Count   Dtype  \n",
            "---  ------                          --------------   -----  \n",
            " 0   hotel                           118900 non-null  object \n",
            " 1   is_canceled                     118900 non-null  int64  \n",
            " 2   lead_time                       118900 non-null  int64  \n",
            " 3   arrival_date_year               118900 non-null  int64  \n",
            " 4   arrival_date_month              118900 non-null  object \n",
            " 5   arrival_date_week_number        118900 non-null  int64  \n",
            " 6   arrival_date_day_of_month       118900 non-null  int64  \n",
            " 7   stays_in_weekend_nights         118900 non-null  int64  \n",
            " 8   stays_in_weekdays_nights        118900 non-null  int64  \n",
            " 9   meal                            118900 non-null  object \n",
            " 10  city                            118900 non-null  object \n",
            " 11  market_segment                  118900 non-null  object \n",
            " 12  distribution_channel            118900 non-null  object \n",
            " 13  is_repeated_guest               118900 non-null  int64  \n",
            " 14  previous_cancellations          118900 non-null  int64  \n",
            " 15  previous_bookings_not_canceled  118900 non-null  int64  \n",
            " 16  booking_changes                 118900 non-null  int64  \n",
            " 17  deposit_type                    118900 non-null  object \n",
            " 18  days_in_waiting_list            118900 non-null  int64  \n",
            " 19  customer_type                   118900 non-null  object \n",
            " 20  adr                             118900 non-null  float64\n",
            " 21  required_car_parking_spaces     118900 non-null  int64  \n",
            " 22  total_of_special_requests       118900 non-null  int64  \n",
            " 23  reservation_status              118900 non-null  object \n",
            " 24  total_guest                     118900 non-null  float64\n",
            " 25  stay_duration                   118900 non-null  int64  \n",
            "dtypes: float64(2), int64(15), object(9)\n",
            "memory usage: 24.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Looking at the stay duration distribution"
      ],
      "metadata": {
        "id": "2FzDrC48rvNQ"
      },
      "id": "2FzDrC48rvNQ"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.style.use('default')\n",
        "plt.figure(figsize=(15,8))\n",
        "sns.countplot(x='stay_duration', data = df, color ='#f0a91a')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "6tnos5Jik3UN",
        "outputId": "0386070a-3833-451a-84ab-42ae0e894684"
      },
      "id": "6tnos5Jik3UN",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**As can be seen from the above the values are right skewed**"
      ],
      "metadata": {
        "id": "zIfX-H5ytMxl"
      },
      "id": "zIfX-H5ytMxl"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Grouping the values of stay duration to increase significance of high stay duration"
      ],
      "metadata": {
        "id": "EhinomqZtTTi"
      },
      "id": "EhinomqZtTTi"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Stay duration >14 will be grouped as 15 in the data frame"
      ],
      "metadata": {
        "id": "ooKvNjrjyyVN"
      },
      "id": "ooKvNjrjyyVN"
    },
    {
      "cell_type": "code",
      "source": [
        "df['stay_duration'] = np.where(df['stay_duration']>14, 15, df['stay_duration'])\n",
        "df['stay_duration'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eIeovkreuC-3",
        "outputId": "88177117-1a2e-44f9-c103-31291a8e6921"
      },
      "id": "eIeovkreuC-3",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2     27530\n",
              "3     27025\n",
              "1     20796\n",
              "4     17363\n",
              "7      8634\n",
              "5      7765\n",
              "6      3850\n",
              "8      1157\n",
              "10     1135\n",
              "14      913\n",
              "9       838\n",
              "0       701\n",
              "15      435\n",
              "11      395\n",
              "12      223\n",
              "13      140\n",
              "Name: stay_duration, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15,8))\n",
        "sns.countplot(x='stay_duration', data = df, color ='#f0a91a')\n",
        "plt.xticks(range(0,16), labels=[str(x) for x in range(0,15)]+['>14'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "Y9YSdzONvnst",
        "outputId": "10cf9d4c-4d55-4305-b146-165e7fa4b501"
      },
      "id": "Y9YSdzONvnst",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Constructing aggregate table to see cancellation rates by stay duration for every hotel type"
      ],
      "metadata": {
        "id": "v1lJgdTE1mpS"
      },
      "id": "v1lJgdTE1mpS"
    },
    {
      "cell_type": "code",
      "source": [
        "dfg2 = df.groupby(['hotel', 'stay_duration', 'is_canceled']).agg({'stay_duration':'count'}).rename(columns={'stay_duration':'no._bookings'})\n",
        "dfg2 = dfg2.pivot_table(index = ['hotel', 'stay_duration'], columns='is_canceled', values='no._bookings').rename(columns={0:'not_canceled', 1:'canceled'}).reset_index()\n",
        "dfg2 = dfg2.copy()\n",
        "dfg2['cancellation_rate'] = round(dfg2['canceled']/(dfg2['canceled']+dfg2['not_canceled'])*100, 2)\n",
        "dfg2.columns = ['hotel_type', 'stay_duration', 'not_canceled', 'canceled', 'cancellation_rate']\n",
        "dfg2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BP14iGBc0ItC",
        "outputId": "84dc700a-21d7-44b3-d437-7694d525c22a"
      },
      "id": "BP14iGBc0ItC",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      hotel_type  stay_duration  not_canceled  canceled  cancellation_rate\n",
              "0     City Hotel              0           308        16               4.94\n",
              "1     City Hotel              1          9169      4102              30.91\n",
              "2     City Hotel              2         10992     10433              48.70\n",
              "3     City Hotel              3         11894      9472              44.33\n",
              "4     City Hotel              4          7703      4853              38.65\n",
              "5     City Hotel              5          3221      1776              35.54\n",
              "6     City Hotel              6          1116       943              45.80\n",
              "7     City Hotel              7          1251       808              39.24\n",
              "8     City Hotel              8           209       176              45.71\n",
              "9     City Hotel              9           120        93              43.66\n",
              "10    City Hotel             10            83       116              58.29\n",
              "11    City Hotel             11            36        46              56.10\n",
              "12    City Hotel             12            35        30              46.15\n",
              "13    City Hotel             13            16        26              61.90\n",
              "14    City Hotel             14            29        71              71.00\n",
              "15    City Hotel             15            44       117              72.67\n",
              "16  Resort Hotel              0           367        10               2.65\n",
              "17  Resort Hotel              1          6369      1156              15.36\n",
              "18  Resort Hotel              2          4382      1723              28.22\n",
              "19  Resort Hotel              3          3796      1863              32.92\n",
              "20  Resort Hotel              4          3303      1504              31.29\n",
              "21  Resort Hotel              5          1889       879              31.76\n",
              "22  Resort Hotel              6          1199       592              33.05\n",
              "23  Resort Hotel              7          4427      2148              32.67\n",
              "24  Resort Hotel              8           507       265              34.33\n",
              "25  Resort Hotel              9           405       220              35.20\n",
              "26  Resort Hotel             10           696       240              25.64\n",
              "27  Resort Hotel             11           240        73              23.32\n",
              "28  Resort Hotel             12            90        68              43.04\n",
              "29  Resort Hotel             13            73        25              25.51\n",
              "30  Resort Hotel             14           627       186              22.88\n",
              "31  Resort Hotel             15           149       125              45.62"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0a34cce0-612c-42cb-8985-da16fe6912f7\" 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_type</th>\n",
              "      <th>stay_duration</th>\n",
              "      <th>not_canceled</th>\n",
              "      <th>canceled</th>\n",
              "      <th>cancellation_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>308</td>\n",
              "      <td>16</td>\n",
              "      <td>4.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>9169</td>\n",
              "      <td>4102</td>\n",
              "      <td>30.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>10992</td>\n",
              "      <td>10433</td>\n",
              "      <td>48.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>3</td>\n",
              "      <td>11894</td>\n",
              "      <td>9472</td>\n",
              "      <td>44.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>4</td>\n",
              "      <td>7703</td>\n",
              "      <td>4853</td>\n",
              "      <td>38.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>5</td>\n",
              "      <td>3221</td>\n",
              "      <td>1776</td>\n",
              "      <td>35.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>6</td>\n",
              "      <td>1116</td>\n",
              "      <td>943</td>\n",
              "      <td>45.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>7</td>\n",
              "      <td>1251</td>\n",
              "      <td>808</td>\n",
              "      <td>39.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>8</td>\n",
              "      <td>209</td>\n",
              "      <td>176</td>\n",
              "      <td>45.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>9</td>\n",
              "      <td>120</td>\n",
              "      <td>93</td>\n",
              "      <td>43.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>10</td>\n",
              "      <td>83</td>\n",
              "      <td>116</td>\n",
              "      <td>58.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>11</td>\n",
              "      <td>36</td>\n",
              "      <td>46</td>\n",
              "      <td>56.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>12</td>\n",
              "      <td>35</td>\n",
              "      <td>30</td>\n",
              "      <td>46.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "      <td>26</td>\n",
              "      <td>61.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>14</td>\n",
              "      <td>29</td>\n",
              "      <td>71</td>\n",
              "      <td>71.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>15</td>\n",
              "      <td>44</td>\n",
              "      <td>117</td>\n",
              "      <td>72.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>0</td>\n",
              "      <td>367</td>\n",
              "      <td>10</td>\n",
              "      <td>2.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>1</td>\n",
              "      <td>6369</td>\n",
              "      <td>1156</td>\n",
              "      <td>15.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>2</td>\n",
              "      <td>4382</td>\n",
              "      <td>1723</td>\n",
              "      <td>28.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>3</td>\n",
              "      <td>3796</td>\n",
              "      <td>1863</td>\n",
              "      <td>32.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>4</td>\n",
              "      <td>3303</td>\n",
              "      <td>1504</td>\n",
              "      <td>31.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>5</td>\n",
              "      <td>1889</td>\n",
              "      <td>879</td>\n",
              "      <td>31.76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>6</td>\n",
              "      <td>1199</td>\n",
              "      <td>592</td>\n",
              "      <td>33.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>7</td>\n",
              "      <td>4427</td>\n",
              "      <td>2148</td>\n",
              "      <td>32.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>8</td>\n",
              "      <td>507</td>\n",
              "      <td>265</td>\n",
              "      <td>34.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>9</td>\n",
              "      <td>405</td>\n",
              "      <td>220</td>\n",
              "      <td>35.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>10</td>\n",
              "      <td>696</td>\n",
              "      <td>240</td>\n",
              "      <td>25.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>11</td>\n",
              "      <td>240</td>\n",
              "      <td>73</td>\n",
              "      <td>23.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>12</td>\n",
              "      <td>90</td>\n",
              "      <td>68</td>\n",
              "      <td>43.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>13</td>\n",
              "      <td>73</td>\n",
              "      <td>25</td>\n",
              "      <td>25.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>14</td>\n",
              "      <td>627</td>\n",
              "      <td>186</td>\n",
              "      <td>22.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>15</td>\n",
              "      <td>149</td>\n",
              "      <td>125</td>\n",
              "      <td>45.62</td>\n",
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          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing cancellation rates by stay duration for every hotel type"
      ],
      "metadata": {
        "id": "flaMQ6AK-hZJ"
      },
      "id": "flaMQ6AK-hZJ"
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating the plot\n",
        "plt.figure(figsize = (15, 6))\n",
        "plt.style.use('fivethirtyeight')\n",
        "fig = sns.lineplot(x='stay_duration', y = 'cancellation_rate', hue = 'hotel_type', palette=['DeepSkyBlue', '#005f5f'], data = dfg2)\n",
        "\n",
        "plt.axvline(x = -0.15, color = 'black', linewidth = 1.2, alpha = .7)\n",
        "plt.axhline(y = 0, color = 'black', linewidth = 1.3, alpha = .7)\n",
        "\n",
        "#Setting tick labels\n",
        "fig.set_yticks(range(0, 80, 10))\n",
        "fig.set_yticklabels(labels = ['0   ', '10   ', '20   ', '30   ', '40   ', '50   ', '60   ', '70 %'])\n",
        "fig.set_xticks(range(0,16))\n",
        "fig.set_xticklabels(labels = [str(x) for x in range(0,15)]+['>14'])\n",
        "plt.xlim(-0.2, 15.3)\n",
        "\n",
        "# creating trend line for both hotel types\n",
        "sns.regplot(x='stay_duration', y='cancellation_rate', data=dfg2[dfg2['hotel_type'] == 'City Hotel'],\n",
        "            scatter=False, label='City Hotels Trend', truncate=False, line_kws={\"linewidth\": 2, 'color': '#2590b0'})#  #edbf18 #2baed6\n",
        "sns.regplot(x='stay_duration', y='cancellation_rate', data=dfg2[dfg2['hotel_type'] == 'Resort Hotel'],\n",
        "            scatter=False, label='Resort Hotels Trend', truncate=False, line_kws={\"linewidth\": 2, 'color': '#67c916'})\n",
        "\n",
        "# Adding title and text\n",
        "fig.text(x = 1.35, y = 102, s = \"Long stay duration = High risk of cancellation\",\n",
        "               fontsize = 27, weight = 'bold', alpha = .85)\n",
        "fig.text(x = 0.15, y = 94,\n",
        "               s = 'Both hotel types get higher cancellation rates from longer stay duration',\n",
        "              fontsize = 23, alpha = .85)\n",
        "fig.text(x = 0.7, y = 87,\n",
        "               s = 'Resort hotels cancellation rates are less affected by stay duration ',\n",
        "              fontsize = 23, alpha = .85)\n",
        "fig.text(x = 3.3, y = 80,\n",
        "               s = 'Cancellation rates by stay duration and hotel type',\n",
        "              fontsize = 20, alpha = .5)\n",
        "\n",
        "# Adding annotations\n",
        "ax = plt.gca()\n",
        "ax.annotate(xy=(10.3,64), xytext=(3.9, 45), text=' ', fontsize=16,\n",
        "                arrowprops=dict(ec='#ba3c38',\n",
        "                                arrowstyle= '->', mutation_scale = 35, linewidth = 4.0))\n",
        "fig.text(x = 3.9, y = 50, s = 'Stay duration has a positive correlation\\nwith cancellation rate',\n",
        "         fontsize = 17, rotation = 11, color='#ba3c38', weight='bold')\n",
        "\n",
        "# Setting axis labels\n",
        "plt.xlabel('Stay Duration', fontweight='bold')\n",
        "plt.ylabel('Cancellation Rate (%)', weight='bold')\n",
        "\n",
        "# Setting legend\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 740
        },
        "id": "fFWsacB19004",
        "outputId": "e98bb3c3-ce00-4a96-c048-1b93c2e0f527"
      },
      "id": "fFWsacB19004",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Analysis and insights**\n",
        "- **General Trends:** There is a consistent trend across both hotel types (City Hotel and Resort Hotel) where cancellation rates tend to increase as the duration of stay becomes longer. Shorter stays generally had lower cancellation rates, while longer stays had higher cancellation rates.\n",
        "- **Short Stay Durations (0 to 5 nights):**\n",
        "  - City Hotel: Cancellation rates increase from 4.94% to 35.54% as stay duration increases from 0 to 5 nights.\n",
        "  - Resort Hotel: Cancellation rates increase from 2.65% to 31.76% as stay duration increases from 0 to 5 nights.\n",
        "  - Short stays have relatively low cancellation rates for both hotel types, but Resort Hotels performs slightly better.\n",
        "- **Medium Stay Durations (6 to 10 nights):**\n",
        "  - City Hotel: Cancellation rates increase from 45.80% to 58.29% as stay duration increases from 6 to 10 nights.\n",
        "  - Resort Hotel: Cancellation rates decrease from 33.05% to 25.64% as stay duration increases from 6 to 10 nights.\n",
        "  - For medium-length stays, City Hotels had higher cancellation rates compared to Resort Hotels.\n",
        "- **Long Stay Durations (>10 nights):**\n",
        "  - City Hotel: Cancellation rates remain relatively stable and high, ranging from 46.15% to 72.67% for stay durations exceeding 10 nights.\n",
        "  - Resort Hotel: Cancellation rates drop significantly from 43.04% (12 nights) to 22.88% (14 nights) but increase to 45.62% for stays longer than 14 nights.\n",
        "  - Long stays are associated with high cancellation rates for both hotel types, with City Hotels consistently having higher rates.\n",
        "-**City Hotels vs. Resort Hotels:** City Hotels had higher cancellation rates throughout the whole stay duration when compared to Resort Hotels. Resort Hotels’ cancellation rates also tend to be less affected by stay duration, exhibiting a shallower slope in cancellation rate increase throughout the whole stay duration.\n",
        "<br><br>\n",
        "\n",
        "**Overall Insights:**\n",
        "- Short stays are generally associated with lower cancellation rates in both hotel types, suggesting that guests were less likely to cancel shorter bookings.\n",
        "- Medium stays exhibit varying patterns, with both hotel types experiencing high and fluctuating cancellation rates.\n",
        "- Long stays consistently had high cancellation rates in both hotel types, indicating a higher risk of cancellations for extended bookings.\n",
        "- The trend is clear, bookings with higher stay duration carry a higher risk of cancellation.\n"
      ],
      "metadata": {
        "id": "nhFayd7udjA_"
      },
      "id": "nhFayd7udjA_"
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Impact Analysis of Lead Time on Hotel Bookings Cancellation Rates**"
      ],
      "metadata": {
        "id": "lL5X90uPkIQo"
      },
      "id": "lL5X90uPkIQo"
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Constructing new column for the categorization of lead time"
      ],
      "metadata": {
        "id": "nYQ7K0t-lFsp"
      },
      "id": "nYQ7K0t-lFsp"
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Categories to be made:**\n",
        "- Very Short: 0-7 days\n",
        "- Short: 8-30 days\n",
        "- Medium: 31-90 days\n",
        "- Long: 91-180 days\n",
        "- Very Long: 181+ days"
      ],
      "metadata": {
        "id": "aHPr0M9Lj_9u"
      },
      "id": "aHPr0M9Lj_9u"
    },
    {
      "cell_type": "code",
      "source": [
        "# Categorization function\n",
        "def categorization(x):\n",
        "  if x['lead_time'] <= 7:\n",
        "    category = 'Very Short'\n",
        "  elif x['lead_time'] > 7 and x['lead_time'] <= 30:\n",
        "    category = 'Short'\n",
        "  elif x['lead_time'] > 30 and x['lead_time'] <= 90:\n",
        "    category = 'Medium'\n",
        "  elif x['lead_time'] > 90 and x['lead_time'] <= 180:\n",
        "    category = 'Long'\n",
        "  else:\n",
        "    category = 'Very Long'\n",
        "\n",
        "  return category"
      ],
      "metadata": {
        "id": "iYv-h71CpfLE"
      },
      "id": "iYv-h71CpfLE",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.copy()\n",
        "df['lead_time_category'] = df.apply(lambda x: categorization(x), axis = 1)\n",
        "df[['lead_time', 'lead_time_category']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "tZwpHrQ3lbvl",
        "outputId": "b553485b-b05e-47aa-e44d-4c036d52ae7f"
      },
      "id": "tZwpHrQ3lbvl",
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              "        lead_time lead_time_category\n",
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            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Constructing aggregate table to see cancellation rates by lead time for every hotel type"
      ],
      "metadata": {
        "id": "8IftIsusqmuM"
      },
      "id": "8IftIsusqmuM"
    },
    {
      "cell_type": "code",
      "source": [
        "dfg3 = df.groupby(['hotel', 'lead_time_category', 'is_canceled']).agg({'is_canceled': 'count'}).rename(columns={'is_canceled':'no._bookings'})\n",
        "dfg3 = dfg3.pivot_table(index = ['hotel', 'lead_time_category'], columns='is_canceled', values='no._bookings').rename(columns={0:'not_canceled', 1:'canceled'}).reset_index()\n",
        "dfg3 = dfg3.copy()\n",
        "dfg3['cancellation_rate'] = round(dfg3['canceled']/(dfg3['canceled']+dfg3['not_canceled'])*100, 2)\n",
        "dfg3.columns = ['hotel_type', 'lead_time_category', 'not_canceled', 'canceled', 'cancellation_rate']\n",
        "dfg3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "9bCBsiyOsA-v",
        "outputId": "0790c79d-ca11-41b7-9513-75ede9dd9076"
      },
      "id": "9bCBsiyOsA-v",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     hotel_type lead_time_category  not_canceled  canceled  cancellation_rate\n",
              "0    City Hotel               Long          9477      8746              47.99\n",
              "1    City Hotel             Medium         12497      8300              39.91\n",
              "2    City Hotel              Short          8674      3880              30.91\n",
              "3    City Hotel          Very Long          6091     10843              64.03\n",
              "4    City Hotel         Very Short          9487      1309              12.12\n",
              "5  Resort Hotel               Long          5116      3074              37.53\n",
              "6  Resort Hotel             Medium          5887      2832              32.48\n",
              "7  Resort Hotel              Short          4941      1395              22.02\n",
              "8  Resort Hotel          Very Long          4515      3207              41.53\n",
              "9  Resort Hotel         Very Short          8060       569               6.59"
            ],
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              "      <td>Resort Hotel</td>\n",
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              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9c8827e5-678e-45bc-8bea-9e4e639d8eec');\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",
              "\n",
              "\n",
              "<div id=\"df-55ba2086-7d2e-47c1-8317-d8c25febd003\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-55ba2086-7d2e-47c1-8317-d8c25febd003')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-55ba2086-7d2e-47c1-8317-d8c25febd003 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 204
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Sorting the data based on lead time category"
      ],
      "metadata": {
        "id": "q6GBQCrN0SlM"
      },
      "id": "q6GBQCrN0SlM"
    },
    {
      "cell_type": "code",
      "source": [
        "categories = ['Very Short', 'Short', 'Medium', 'Long', 'Very Long']\n",
        "dfg3 = dfg3.copy()\n",
        "dfg3['lead_time_category'] = pd.Categorical(dfg3['lead_time_category'], categories = categories, ordered=True)\n",
        "dfg3 = dfg3.sort_values(['hotel_type', 'lead_time_category']).reset_index(drop=True)\n",
        "\n",
        "dfg3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "1Cut5KWmxb-b",
        "outputId": "9ac4009b-0e8d-4efd-fea1-3263d0aa74ea"
      },
      "id": "1Cut5KWmxb-b",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     hotel_type lead_time_category  not_canceled  canceled  cancellation_rate\n",
              "0    City Hotel         Very Short          9487      1309              12.12\n",
              "1    City Hotel              Short          8674      3880              30.91\n",
              "2    City Hotel             Medium         12497      8300              39.91\n",
              "3    City Hotel               Long          9477      8746              47.99\n",
              "4    City Hotel          Very Long          6091     10843              64.03\n",
              "5  Resort Hotel         Very Short          8060       569               6.59\n",
              "6  Resort Hotel              Short          4941      1395              22.02\n",
              "7  Resort Hotel             Medium          5887      2832              32.48\n",
              "8  Resort Hotel               Long          5116      3074              37.53\n",
              "9  Resort Hotel          Very Long          4515      3207              41.53"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4189ad97-5054-4d20-865e-d44523bf962e\" 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_type</th>\n",
              "      <th>lead_time_category</th>\n",
              "      <th>not_canceled</th>\n",
              "      <th>canceled</th>\n",
              "      <th>cancellation_rate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>Very Short</td>\n",
              "      <td>9487</td>\n",
              "      <td>1309</td>\n",
              "      <td>12.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>Short</td>\n",
              "      <td>8674</td>\n",
              "      <td>3880</td>\n",
              "      <td>30.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>Medium</td>\n",
              "      <td>12497</td>\n",
              "      <td>8300</td>\n",
              "      <td>39.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>Long</td>\n",
              "      <td>9477</td>\n",
              "      <td>8746</td>\n",
              "      <td>47.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>City Hotel</td>\n",
              "      <td>Very Long</td>\n",
              "      <td>6091</td>\n",
              "      <td>10843</td>\n",
              "      <td>64.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>Very Short</td>\n",
              "      <td>8060</td>\n",
              "      <td>569</td>\n",
              "      <td>6.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>Short</td>\n",
              "      <td>4941</td>\n",
              "      <td>1395</td>\n",
              "      <td>22.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>Medium</td>\n",
              "      <td>5887</td>\n",
              "      <td>2832</td>\n",
              "      <td>32.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>Long</td>\n",
              "      <td>5116</td>\n",
              "      <td>3074</td>\n",
              "      <td>37.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Resort Hotel</td>\n",
              "      <td>Very Long</td>\n",
              "      <td>4515</td>\n",
              "      <td>3207</td>\n",
              "      <td>41.53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4189ad97-5054-4d20-865e-d44523bf962e')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-4189ad97-5054-4d20-865e-d44523bf962e 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-4189ad97-5054-4d20-865e-d44523bf962e');\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",
              "\n",
              "\n",
              "<div id=\"df-847a2620-94e9-4050-be48-7edb8a2ec31f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-847a2620-94e9-4050-be48-7edb8a2ec31f')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-847a2620-94e9-4050-be48-7edb8a2ec31f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing cancellation rates by lead time for every hotel type"
      ],
      "metadata": {
        "id": "4yYhE-vyAAUz"
      },
      "id": "4yYhE-vyAAUz"
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.patches as patches\n",
        "\n",
        "# Creating plot\n",
        "plt.style.use('seaborn-v0_8-dark')\n",
        "sns.set_style(\"whitegrid\", {'grid.color': 'black'})\n",
        "plt.figure(facecolor = '#00d7d7', figsize=(12,8))\n",
        "plt.axes().set_facecolor('#00d7d7')\n",
        "\n",
        "bar = sns.barplot(x = 'lead_time_category', y='cancellation_rate', hue = 'hotel_type', data=dfg3, palette = ['#005f5f', '#d7ffff'])\n",
        "\n",
        "# Setting tick labels\n",
        "bar.set_yticks(range(0, 80, 10))\n",
        "bar.set_yticklabels(labels = ['0   ', '10   ', '20   ', '30   ', '40   ', '50   ', '60   ', '70 %'])\n",
        "\n",
        "# Adding title and texts\n",
        "bar.text(x = -0.65, y = 87,\n",
        "           s = 'Early Bookings Get Canceled More',\n",
        "          fontsize = 38, alpha =1, weight =1000, color='white')\n",
        "bar.text(x = -0.84, y = 82,\n",
        "           s = 'Both hotel types get higher cancellation rates from longer lead times',\n",
        "          fontsize = 23, alpha =1, color='black')\n",
        "bar.text(x = -0.8, y = 78,\n",
        "           s = 'Resort hotels are less affected by early bookings, although the jump from very short to short notice is 3.3X',\n",
        "          fontsize = 13, alpha =1, weight=1000, color='black')\n",
        "bar.text(x = 0.5, y = 72,\n",
        "           s = 'Cancellation rates by lead time and hotel type',\n",
        "          fontsize = 18, alpha =0.85, color='#005f5f')\n",
        "\n",
        "# Adding annotations and arrow\n",
        "for i, j in zip(bar.patches, range(len(bar.patches))):\n",
        "  height = i.get_height()\n",
        "  width = i.get_width()\n",
        "  x = i.get_x()\n",
        "  y = i.get_y()\n",
        "  if j > 4 != 0:\n",
        "    bar.annotate(f'{height}%', (x + width/2, y + (height/2)), ha='center', va='bottom', color = 'black')\n",
        "  else:\n",
        "    bar.annotate(f'{height}%', (x + width/2, y + (height/2)), ha='center', va='bottom', color = 'white')\n",
        "\n",
        "arrow = patches.FancyArrowPatch(\n",
        "    (-0.17, 30),\n",
        "    (3.7, 69),\n",
        "    connectionstyle=\"arc3,rad=0.13\",\n",
        "    arrowstyle=\"->\",\n",
        "    mutation_scale=45,\n",
        "    color = 'white',\n",
        "    linewidth = 9\n",
        ")\n",
        "bar.add_patch(arrow)\n",
        "\n",
        "# Setting axis labels\n",
        "bar.set_xlabel('Lead Time', fontweight='bold')\n",
        "bar.set_ylabel('Cancellation Rate (%)', weight='bold')\n",
        "\n",
        "# Setting legend\n",
        "plt.legend(loc='upper left')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 935
        },
        "id": "nCqxTv904QL1",
        "outputId": "43ece2c5-2838-4b33-9e2e-2a2d42c4dabb"
      },
      "id": "nCqxTv904QL1",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analysis and Interpretation:\n",
        "- **General Trend:** <br>The chart demonstrates a consistent trend where cancellation rates tend to increase as lead time increases, for both City Hotels and Resort Hotels. In other words, the longer the lead time before the booking, the higher the likelihood of a cancellation.\n",
        "- **City Hotels vs. Resort Hotels:** <br>City Hotels generally exhibit higher cancellation rates compared to Resort Hotels across all lead time categories. This suggests that the type of hotel can significantly influence cancellation behavior, with City Hotels experiencing a greater number of cancellations.\n",
        "- **City Hotels vs. Resort Hotels (part 2):** <br>Resort Hotels cancellation rates are more stagnant at higher lead times compared to City Hotels, however Resort Hotels cancellation rates jumped by more than 3.3x between very short and short notice, from having half the rate of City Hotels to having more than 2/3. This did not continue however, as towards the higher lead times City Hotels’ cancellation rates skyrocketed leaving Resort Hotels behind.\n",
        "- **Very Short Lead Time:** <br>Both City Hotels and Resort Hotels experience their lowest cancellation rates in the \"Very Short\" lead time category. City Hotels have a cancellation rate of 12.12%, while Resort Hotels have a rate of 6.59% in this category. This implies that bookings made on very short notice are less likely to be canceled, possibly because guests have firm plans or limited flexibility.\n",
        "- **Effect of Longer Lead Times:** <br>As lead time increases from \"Short\" to \"Very Long,\" there is a consistent upward trend in cancellation rates for both hotel types. City Hotels, in particular, show a substantial increase in cancellation rates from 30.91% at \"Short\" lead time to 64.03% at \"Very Long\" lead time, indicating that guests booking well in advance are more likely to cancel."
      ],
      "metadata": {
        "id": "iisCepIsx-MP"
      },
      "id": "iisCepIsx-MP"
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cR5zPPxIpggI"
      },
      "id": "cR5zPPxIpggI",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
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      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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