Hotel Booking Prediction Using ML and DL
Dataset Description
Dataset Name: Hotel Booking Demand Dataset
Description:
The Hotel Booking Demand Dataset contains booking information for City Hotels and Resort Hotels. It includes customer demographics, booking details, stay duration, reservation information, and hotel-related attributes. The dataset is widely used for hotel booking cancellation prediction, customer behavior analysis, and machine learning classification tasks.
- Number of Records: 119,390
- Number of Features: 32
- Problem Type: Binary Classification
-
Domain: Hospitality and Tourism
Target Variable
Target Variable: is_canceled
| Value | Meaning |
|---|---|
| 0 | Booking was Not Cancelled |
| 1 | Booking was Cancelled |
Objective
Develop a Machine Learning or Deep Learning model to predict whether a hotel booking will be cancelled based on customer, booking, and reservation information.
Feature Description
| Feature | Description |
|---|---|
| hotel | Type of hotel (Resort Hotel or City Hotel) |
| is_canceled | Target variable indicating whether the booking was cancelled (0 = No, 1 = Yes) |
| lead_time | Number of days between booking date and arrival date |
| arrival_date_year | Year of arrival |
| arrival_date_month | Month of arrival |
| arrival_date_week_number | Week number of arrival |
| arrival_date_day_of_month | Day of the month of arrival |
| stays_in_weekend_nights | Number of weekend nights booked |
| stays_in_week_nights | Number of weekdays booked |
| adults | Number of adults |
| children | Number of children |
| babies | Number of babies |
| meal | Type of meal booked |
| country | Customer's country of origin |
| market_segment | Market segment through which the booking was made |
| distribution_channel | Booking distribution channel |
| is_repeated_guest | Whether the customer is a repeated guest |
| previous_cancellations | Number of previous cancelled bookings |
| previous_bookings_not_canceled | Number of previous successful bookings |
| reserved_room_type | Room type originally reserved |
| assigned_room_type | Room type actually assigned |
| booking_changes | Number of booking modifications |
| deposit_type | Type of deposit made |
| agent | Travel agent ID (if applicable) |
| company | Company ID (if booking was made through a company) |
| days_in_waiting_list | Number of days the booking was on the waiting list |
| customer_type | Type of customer (Transient, Group, Contract, etc.) |
| adr | Average Daily Rate (average room price per night) |
| required_car_parking_spaces | Number of parking spaces requested |
| total_of_special_requests | Number of special requests made by the customer |
| reservation_status | Final reservation status (Check-Out, Canceled, No-Show) |
| reservation_status_date | Date of the final reservation status |
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