Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a consumer is Planning to Buy Furniture.
Overview
This Pandemic Propensity Model determines the probability that a US adult is Planning to Buy Furniture. Lift over Random 1.39
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 1.39
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.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 for running this predictive model, billed by usage with no upfront commitment. Pricing is organized around the AWS instance type you choose to host the model. Options span general-purpose (m4, m5), compute-optimized (c4, c5), and GPU-accelerated (p2, p3) families, in sizes from large up to 24xlarge. Larger instances carry higher hourly rates. Each instance type is offered in two modes: Batch, for scoring datasets in scheduled runs, and Real-Time, for on-demand scoring. Your total cost depends on the instance you select, the mode, and the hours you run.
Top-of-mind questions for buyers
Both meter host-hours on the instance you pick. Batch mode scores a dataset in scheduled runs, so you pay only while the job runs. Real-Time mode keeps an endpoint live for on-demand scoring, so charges accrue for every hour the endpoint stays running, including idle time.
One host-hour is one hour that your chosen instance type runs the model. Real-Time endpoints accrue charges for every hour they stay active, even without scoring requests. Batch jobs charge only for the hours the run takes. Underlying AWS infrastructure fees may apply separately.
Three factors combine: the instance type you select, the mode (Batch or Real-Time), and the hours you run. GPU-accelerated families (p2, p3) and larger sizes carry higher hourly rates. Your bill is the hourly rate for your instance multiplied by host-hours used in each mode.
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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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