Sold by: Mphasis
Deployed on AWS
Ensemble Machine Learning algorithm-based solution which predicts the probability of guests cancelling their hotel reservations.
Overview
Cancellation of scheduled hotel stays is a significant challenge for hospitality companies. Cancellations lead to erroneous demand estimation, room pricing and revenue management. This solution predicts the likelihood of guests cancelling their hotel reservations based on guests' booking information. The Solution assists hospitality companies to maximize occupancy and revenue per available room.
Highlights
- Hotel Reservation Cancellation Predictor uses guests' booking information to predict the likelihood of cancellation.
- This solution, developed on cutting-edge machine learning algorithms, can be leveraged by hospitality companies to pursue targeted interventions and improve guest check-in.
- Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $20.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $10.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $20.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $20.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $20.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $20.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $20.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $20.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $20.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $20.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
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Delivery details
Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
This is version 1.3.
Additional details
Inputs
- Summary
-
Supported content types: 'csv' file only
-
Mandatory fields: Guest_Id, hotel_location_type,lead_time, expected_arrival_month, expected_weekend_stays, expected_weekday_stays, adults, children, babies,meal_plan , guest_market_segment, repeat_guest, previous_cancellations, previous_bookings_not_canceled, booking_modifications,reservation_queue,average_daily_rate, required_car_parking_spaces, special_requests.
-
- Input MIME type
- text/csv, text/plain, application/zip
https://github.com/Mphasis-ML-Marketplace/Hotel-Reservation-Cancellation-Predictor/tree/main/input
https://github.com/Mphasis-ML-Marketplace/Hotel-Reservation-Cancellation-Predictor/tree/main/input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Guest_Id | Numeric or alpha-numeric value to uniquely identify a guest. For example, ‘G01’ | Type: FreeText | Yes |
hotel_location_type | If the location of the hotel is in city then value is ‘City Destination’, Else. if the location is a holiday spot then value is ‘Holiday Destination’. | Type: FreeText | Yes |
lead_time | Number of days between the date of reservation and the expected date of arrival. For example, If confirmation date is July 1 and appointment date is July 30, then Lead_Time value is ‘29’. | Type: Integer | Yes |
expected_arrival_month | Month of expected arrival date. The values can be ‘January’ , February‘, ‘March’,’ April’, ‘May’, ’June’, ’July’, ‘August’, ’September’, ’October’, ’November’, ’December’. | Type: Categorical
Allowed values: January , February, March, April, May, June, July, August, September, October, November, December | Yes |
expected_weekend_stays | Number of weekend nights (Saturday or Sunday) the guest booked to stay at the hotel. | Type: Integer | Yes |
expected_weekday_stays | Number of week day nights (Monday to Friday) the guest booked to stay at the hotel. | Type: Integer | Yes |
adults | Number of adults. | Type: Integer | Yes |
children | Number of children. | Type: Integer | Yes |
babies | Number of babies. | Type: Integer | Yes |
meal_plan | If the meal plan is not confirm- ‘Undefined’
If the meal plan is Self-Catering- ‘SC’
If the meal plan is of type Bed and Breakfast- ‘BB’
If the meal plan is of type Half Board (breakfast + 1 other meal)- ‘HB’
If the meal plan is of type Full Board (breakfast + lunch+ dinner)- ‘FB’ | Type: Categorical
Allowed values: Undefined, SC, BB, HB, FB | Yes |
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