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.
Long-Term Disability Claims
By:
Latest Version:
1.0.1
Predict likelihood of user to file a longterm disability claim in a group insurance plan.
Product Overview
Predict the likelihood of users to file a long-term disability claim in a group insurance plan. The model extracts and integrates relevant information from complicated insurance data. Use of the model delivered highly predictive results and very closed traced the actual incidents. The data includes group plan level data (e.g. coverages and provisions, tenure, plan size, SIC) and employee level data (e.g. gender, salary, dependents, dental claims) to generate data out insights to predict the score of filing long term disability claims. To preview our machine learning models, please Continue to Subscribe. To preview our sample Output Data, you will be prompted to add suggested Input Data. Sample Data is representative of the Output Data but does not actually consider the Input Data. Our machine learning models return actual Output Data and are available through a private offer. Please contact info@electrifai.net for subscription service pricing. SKU: LTDCL-PS-GIS-AWS-001
Key Data
Version
Type
Model Package
Highlights
Predict the likelihood of users to file a long-term disability claim in a group insurance plan.
The problem was approached as both a classification problem to predict the likelihood of filing a long-term disability claim as well as a prediction problem to estimate the cost of each claim.
Also added a claim costs regression model that exhibits high performance, with an actual to expected ratio of close to 1 on average across business-defined buckets and low variance.
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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.
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$0.00/hr
running on ml.p2.16xlarge
Model Batch Transform$0.00/hr
running on ml.m5.2xlarge
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$16.56/host/hr
running on ml.p2.16xlarge
SageMaker Batch Transform$0.461/host/hr
running on ml.m5.2xlarge
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.p2.xlarge | $0.00 | |
ml.p2.16xlarge Vendor Recommended | $0.00 | |
ml.p3.16xlarge | $0.00 |
Usage Information
Model input and output details
Input
Summary
6 CSV input files. The files should then be archived and zipped into a single file e.g. input.tar.gz Employees.csv (REQUIRED) Billing.csv (REQUIRED) Claims.csv (REQUIRED) Plan.csv (REQUIRED) Coverage.csv (REQUIRED) Dependent.csv
Input MIME type
application/octet-stream, multi, multipart/form-dataSample input data
Output
Summary
Output: A Json response with each employee's ID as the main key; for every entry, there will be two predictions in two different columns. Both predictions will be given for the employee ID in each row. The ltd_clm_prob column will contain the probability of an incident occurring. The ltd_clm_tot_amt column will contain the expected amount the insurance will pay for the long-term disability.
Column
member_id
ltd_clm_prob
ltd_clm_tot_amt
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
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Support Information
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