This ML service will use Python 3.11.1 and Django 4.1.6.
This ML system:
- Can handle many API endpoints.
- Each API endpoint can have several ML algorithms with different versions.
- ML code and artifacts (files with ML parameters) are stored in the code repository.
- Supports fast deployments and continuous integration (tests for both: server and ML code).
- Supports monitoring and algorithm diagnostic (support A/B tests).
- has a user interface.
Context
The online hotel reservation channels have dramatically changed booking possibilities and customers’ behavior. A significant number of hotel reservations are called-off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc. This is often made easier by the option to do so free of charge or preferably at a low cost which is beneficial to hotel guests but it is a less desirable and possibly revenue-diminishing factor for hotels to deal with.
Usage
Predict if the customer is going to honor the reservation or cancel it.
To start the app:
py manage.py runserver
- API Root URL: http://127.0.0.1:8000/api/v1/
- API Endpoints: "http://127.0.0.1:8000/api/v1/endpoints"
- ML Algorithms: "http://127.0.0.1:8000/api/v1/mlalgorithms"
- ML Algorithm Statuses: "http://127.0.0.1:8000/api/v1/mlalgorithmstatuses"
- ML Requests: "http://127.0.0.1:8000/api/v1/mlrequests"
Make Predictions:
The model training file contained the different attributes of customers' reservation details. The detailed data dictionary is given below.
- Booking_ID: unique identifier of each booking
- no_of_adults: Number of adults
- no_of_children: Number of Children
- no_of_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel
- no_of_week_nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel
- type_of_meal_plan: Type of meal plan booked by the customer
- required_car_parking_space: Does the customer require a car parking space? (0 - No, 1- Yes)
- room_type_reserved: Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels.
- lead_time: Number of days between the date of booking and the arrival date
- arrival_year: Year of arrival date
- arrival_month: Month of arrival date
- arrival_date: Date of the month
- market_segment_type: Market segment designation.
- repeated_guest: Is the customer a repeated guest? (0 - No, 1- Yes)
- no_of_previous_cancellations: Number of previous bookings that were canceled by the customer prior to the current booking
- no_of_previous_bookings_not_canceled: Number of previous bookings not canceled by the customer prior to the current booking
- avg_price_per_room: Average price per day of the reservation; prices of the rooms are dynamic. (in euros)
- no_of_special_requests: Total number of special requests made by the customer (e.g. high floor, view from the room, etc)
- booking_status: Flag indicating if the booking was canceled or not.
