The primary objective of this project is to analyze the provided hotel bookings data to extract meaningful insights, identify trends, and provide actionable recommendations to improve business operations and customer satisfaction.
Data Overview: The dataset consists of various attributes related to hotel bookings, including information about the booking dates, customer demographics, booking status, stay duration, special requests, and more. The key columns include:
hotel: Type of hotel (City Hotel or Resort Hotel) is_canceled: Booking status (canceled or not) lead_time: Number of days between the booking date and the arrival date arrival_date_year: Year of arrival arrival_date_month: Month of arrival stays_in_weekend_nights: Number of weekend nights stays_in_week_nights: Number of week nights adults: Number of adults children: Number of children babies: Number of babies meal: Type of meal booked country: Country of origin of the customer market_segment: Market segment designation distribution_channel: Booking distribution channel is_repeated_guest: Whether the booking is from a repeated guest previous_cancellations: Number of previous cancellations previous_bookings_not_canceled: Number of previous non-canceled bookings reserved_room_type: Code of room type reserved assigned_room_type: Code of room type assigned booking_changes: Number of changes made to the booking deposit_type: Type of deposit made agent: ID of the travel agency that made the booking company: ID of the company that made the booking days_in_waiting_list: Number of days the booking was on the waiting list customer_type: Type of customer (e.g., transient, contract, etc.) adr: Average Daily Rate