Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a consumer is Planning to Buy a Computer
Overview
This Pandemic Propensity Model determines the probability that a US adult is Planning to Buy a Computer. Lift over Random 1.94
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 1.94
Details
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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.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 host hour for running this consumer-purchase propensity model on machine learning compute instances. Pricing splits into two modes: batch inference for scoring large datasets at once, and real-time inference for on-demand scoring. Within each mode, you choose an instance type from general-purpose, compute-optimized, and GPU-accelerated families. Larger instance sizes carry higher hourly rates, so cost scales with the compute power you select. Each dimension is independent — you pay only for the hours the instances you choose actually run. There is no upfront commitment; billing is usage-based.
Top-of-mind questions for buyers
One HostHr is one hour that a chosen machine learning compute instance runs the propensity model. Billing meters the running time of that specific instance type. If you run two instances for one hour, you accrue two HostHrs. Charges track actual instance uptime, not the number of records scored.
Both meter by HostHr on the instance type you select. Batch inference scores large datasets in one scheduled run, so instances run only during that job. Real-time inference keeps an instance running to answer on-demand scoring requests, so hours accrue for as long as the endpoint stays active.
HostHr charges accrue only while an instance runs. A stopped batch instance stops software charges once the job ends. A real-time endpoint keeps accruing hours until you shut it down, even when it receives no scoring requests. Underlying AWS resource fees may still apply separately.
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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
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