Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a consumer is Planning to Buy a House.
Overview
This Pandemic Propensity Model determines the probability that a US adult is Planning to Buy a House. Lift over Random 1.33
This Pandemic Propensity model is one of a series of consumer classification models based on data from over 24,000 US adults surveyed between April to June 2020 from Prosper's US Monthly Consumer survey. Survey data was collected during the Covid-19 Coronavirus Pandemic, capturing behavior changes and preferences during the National Emergency. The survey is anonymous. Zero PII. CCPA and HIPAA Compliant.
Highlights
- Enhances digital and offline targeting by identifying an individual’s probability to engage in a specific behavior. Model is based on data from over 24.000 US adults surveyed between April to July 2020 from Prosper's US Monthly Consumer survey.
- 100% Privacy Compliant Models. No PII Used.
- Lift over Random 1.33
Details
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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.m4.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $350.00 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $0.28 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $350.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $350.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $350.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $350.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $350.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $350.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $350.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $350.00 |
Dimensions summary
You pay by the hour based on the compute instance you run, with no upfront commitment. Charges apply per host hour (HostHrs) for running this predictive model. Pricing splits into two modes: batch inference, which scores data in scheduled groups, and real-time inference, which scores on demand. Within each mode, you choose from a range of AWS machine learning instance types across the m4, m5, c4, c5, p2, and p3 families. General-purpose, compute-focused, and GPU-based options are available in different sizes. Larger instances with more resources bill at higher hourly rates.
Top-of-mind questions for buyers
One host hour is one hour that a chosen AWS machine learning instance runs your inference job. You are billed per running hour of that instance. Each instance type meters its own hours separately. If you run several instances at once, each accrues host hours independently.
Batch inference scores data in scheduled groups, so you run instances only while the batch job processes. Real-time inference keeps an instance running to score requests on demand. Both meter host hours the same way. Batch suits periodic scoring; real-time suits continuous, on-demand scoring that needs the instance always available.
Host hour charges apply only while an instance runs. A stopped instance does not accrue software host hour charges. Real-time inference instances keep running until you stop them, so they continue to bill even without active requests. Batch instances stop once the job completes.
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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
None
Additional details
Inputs
- Summary
For complete usage instructions, sample data, and sample code, go to: https://github.com/goprosper/prosper-sagemaker-basic-geo.git
- Input MIME type
- text/csv
See Input Summary
See Input Summary
Support
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