Sold by: Mphasis
Deployed on AWS
This solution predicts flight delays based on factors such as route, airport congestion, airline efficiency etc. using a trainable ML model
Overview
Flight delays could cause airlines to incur financial losses in the form of accommodation expenses for the delayed passengers as well as penalties, fines, and operational costs for aircraft labor retention at airports. Furthermore, continual unexpected delays could cause the airline to lose their customers.
This solution predicts whether a flight would be delayed at the origin airport and by how many minutes. It utilizes latent factors such as flight route, airport congestion, airline efficiency and temporal features derived from U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics data on flight on-time performance for large air carriers. The solution uses tree-based models to capture and predict on-time behavior of commercial flights.
Highlights
- The solution uses latent airport and airline specific operational features obtained from standardized U.S. DoT flight on-time performance data. The solution can be trained on client data to capture and predict client specific operational patterns.
- Delay in a flight causes subsequent flights to be delayed causing the aircraft and crew schedules to be negatively impacted. Being able to predict the delay allows for better operational planning at the destination airport based on expected flight delay at origin. It also allows for better customer communication in providing flight recommendations for multi-leg journeys and avoid potential over-scheduling.
- Mphasis HyperGraf is an Omni-channel customer 360 analytics solution. 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.m5.large Training Recommended | Algorithm training on the ml.m5.large instance type | $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 |
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 algorithm
An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the 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 the Version 1.1 of the algorithm
Additional details
Inputs
- Summary
Input should be a .zip file containing a single csv with all columns in the train/test file except for 'DEPARTURE_DELAY' column.
- Input MIME type
- application/zip, text/csv, text/plain, application/json
https://github.com/Mphasis-ML-Marketplace/Flight-Delay-Prediction/tree/main/Model%20Input
https://github.com/Mphasis-ML-Marketplace/Flight-Delay-Prediction/tree/main/Model%20Input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
DATE | Date of the Flight Trip (formatted as "DD/MM/YYYY"). | Type: FreeText | Yes |
AIRLINE | Airline Identifier. | Type: FreeText | Yes |
ORIGIN_AIRPORT | Origin/Departure Airport | Type: FreeText | Yes |
DESTINATION_AIRPORT | Destination/Arrival Airport | Type: FreeText | Yes |
SCHEDULED_DEPARTURE | Planned Departure Time (24 hr formatted as "HH:MM") | Type: FreeText | Yes |
SECURITY_DELAY | Delay caused by security | Default value: 0
Type: Integer
Minimum: 0 | No |
LATE_AIRCRAFT_DELAY | Delay caused by aircraft | Default value: 0
Type: Integer
Minimum: 0 | No |
Resources
Vendor resources
Support
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