Amazon Sagemaker
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.
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Vehicle Insurance Claims Prediction
By:
Latest Version:
2.0
The solution provides occurrence and claim amount prediction for a policyholder. The solution is based on Regression and XG Boost.
Product Overview
Automotive claims prediction is a component of HyperGraf, which predicts occurrence of a claim and the claim amount for a policyholder. The underlying ML algorithms are based on variations of Regression and XG Boost using important policyholder, vehicle and GeoZone characteristics. Trained on real world claims data from an Insurance company, the algorithm considers important business factors for robustness and accuracy.
Key Data
Version
By
Type
Model Package
Highlights
Automotive Claim Prediction predicts claim occurrence and amount using real world historical claims data using vehicle, driver and GeoZone characteristics.
Provides claim occurrence and claim amount predictions for cities across 3 countries
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Pricing Information
Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.
Contact us to request contract pricing for this product.
Estimating your costs
Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.
Version
Region
Software Pricing
Model Realtime Inference$4.00/hr
running on ml.m5.large
Model Batch Transform$8.00/hr
running on ml.m5.large
Infrastructure PricingWith Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
Learn more about SageMaker pricing
SageMaker Realtime Inference$0.115/host/hr
running on ml.m5.large
SageMaker Batch Transform$0.115/host/hr
running on ml.m5.large
Model Realtime Inference
For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.InstanceType | Realtime Inference/hr | |
---|---|---|
ml.m4.4xlarge | $4.00 | |
ml.m5.4xlarge | $4.00 | |
ml.m4.16xlarge | $4.00 | |
ml.m5.2xlarge | $4.00 | |
ml.p3.16xlarge | $4.00 | |
ml.m4.2xlarge | $4.00 | |
ml.c5.2xlarge | $4.00 | |
ml.p3.2xlarge | $4.00 | |
ml.c4.2xlarge | $4.00 | |
ml.m4.10xlarge | $4.00 | |
ml.c4.xlarge | $4.00 | |
ml.m5.24xlarge | $4.00 | |
ml.c5.xlarge | $4.00 | |
ml.p2.xlarge | $4.00 | |
ml.m5.12xlarge | $4.00 | |
ml.p2.16xlarge | $4.00 | |
ml.c4.4xlarge | $4.00 | |
ml.m5.xlarge | $4.00 | |
ml.c5.9xlarge | $4.00 | |
ml.m4.xlarge | $4.00 | |
ml.c5.4xlarge | $4.00 | |
ml.p3.8xlarge | $4.00 | |
ml.m5.large Vendor Recommended | $4.00 | |
ml.c4.8xlarge | $4.00 | |
ml.p2.8xlarge | $4.00 | |
ml.c5.18xlarge | $4.00 |
Usage Information
Fulfillment Methods
Amazon SageMaker
Input:
Following are the mandatory inputs for predictions made by the algorithm:
- Age: The owner’s age, between 0 and 99, a numeric vector
- Gender: Gender of the Vehicle Owner, (0 for Male, 1 for Female)
- City: The model is trained on cities from US, UK and India. The user has to choose one of the cities from the following list as input to the model.
- US: 'Chicago', 'New York', 'San Francisco', 'Los Angeles', 'Washington DC', 'Boston', 'San * * Diego', 'Philadelphia', 'Houston'
- UK: 'London', 'Liverpool', 'Leeds', 'Birmingham', 'Manchester', 'Glasgow', 'Edinburgh'
- India: 'Bangalore', 'Chennai', 'Hyderabad', 'Kolkata', 'Delhi', 'Mumbai', 'Ahmedabad'
- EnginePower
- EnginePowerUnit (Ps/Kilowatts/bhp/hp)
- VehCurbWeight : Vehicle Curb Weight
- VehCurbWeightUnit (Kg/Pound)
- VehAge: Vehicle Age, between 0 and 99, a numeric vector
- ClaimStatus: Claim Status, taking values from 1 to 7. A new driver starts with bonus class 1; for each claim-free year the claim status is increased by 1. After the first claim the claim status is decreased by 2; the driver can’t return to class 7 with less than 6 consecutive claim free years, a numeric vector
- PolicyDuration: Policy duration in years
- Supported content types: 'text/csv'.
Output:
- Supported content types: 'text/csv'.
Invoking endpoint:
If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:
aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.csv --content-type text/csv --accept text/csv out.csv
Substitute the following parameters:
"endpoint-name"
- name of the inference endpoint where the model is deployedinput.csv
- input image to do the inference onout.csv
- filename where the inference results are written to
Resources:
Additional Resources
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Support Information
Vehicle Insurance Claims Prediction
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