You are supporting a hotel with a project aimed to increase revenue from their room bookings. They believe that they can use data science to help them reduce the number of cancellations. This is where you come in! They have asked you to use any appropriate methodology to identify what contributes to whether a booking will be fulfilled or cancelled. They intend to use the results of your work to reduce the chance someone cancels their booking.
They have provided you with their bookings data in a file called hotel_bookings.csv, which contains the following:
- Booking_ID Unique identifier of the booking.
- no_of_adults The number of adults.
- no_of_children The number of children.
- no_of_weekend_nights Number of weekend nights (Saturday or Sunday).
- no_of_week_nights Number of week nights (Monday to Friday).
- type_of_meal_plan Type of meal plan included in the booking.
- required_car_parking_space Whether a car parking space is required.
- room_type_reserved The type of room reserved.
- lead_time Number of days before the arrival date the booking was made.
- arrival_year Year of arrival.
- arrival_month Month of arrival.
- arrival_date Date of the month for arrival.
- market_segment_type How the booking was made.
- repeated_guest Whether the guest has previously stayed at the hotel.
- no_of_previous_cancellations Number of previous cancellations.
- no_of_previous_bookings_not_canceled Number of previous bookings that were canceled.
- avg_price_per_room Average price per day of the booking.
- no_of_special_requests Count of special requests made as part of the booking.
- booking_status Whether the booking was cancelled or not.
Source (data has been modified): https://www.kaggle.com/datasets/ahsan81/hotel-reservations-classification-dataset
Use your skills to produce recommendations for the hotel on what factors affect whether customers cancel their booking.