Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a consumer has Sleep Apnea.
Overview
This post-pandemic Propensity Model determines the probability that a US adult has Sleep Apnea. Lift over Random 2.54. This post-pandemic Propensity model is one of a series of consumer classification models based on data from over 17,000 US adults surveyed in 2021 from Prosper's US Media Behaviors & Influence study. Survey data was collected 9 months after the National Covid-19 Coronavirus Emergency was declared, capturing consumer behavior changes and preferences. 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 17,000 US adults surveyed in 2021 from Prosper's US Media Behaviors & Influence study.
- 100% Privacy Compliant Models. No PII Used.
- Lift over Random 2.54
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 |
|---|---|---|
ml.m4.xlarge Inference (Batch) Recommended | Model inference on the ml.m4.xlarge instance type, batch mode | $500.00/host/hour |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $500.00/host/hour |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $500.00/host/hour |
ml.m5.24xlarge Inference (Batch) | Model inference on the ml.m5.24xlarge instance type, batch mode | $500.00/host/hour |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $500.00/host/hour |
inference.count.m.i.c Inference Pricing | inference.count.m.i.c Inference Pricing | $0.02/request |
Dimensions summary
This model runs through Amazon SageMaker and bills by usage. Five dimensions charge per host hour for batch inference, each tied to a different instance size. You choose the instance type based on the compute power you need, and pricing scales with that choice. The instance families range from smaller sizes up to larger multi-processor configurations. A separate dimension bills by request count instead of host hours, so you pay per inference call. Pick host-hour pricing for batch jobs on a chosen instance, or request-based pricing to pay per prediction.
Top-of-mind questions for buyers
One host hour is one hour that a chosen instance type runs a batch inference job. Billing counts each running hour on that instance. Larger instance sizes carry more compute and memory per hour. You pay only for the hours the batch job actively runs on the selected instance.
The two models bill independently. Host-hour dimensions meter running time on a selected instance for batch jobs. The request-based dimension counts each inference call. For steady batch workloads, host-hour charges usually drive the bill. For sporadic single predictions, request count tends to dominate.
Host-hour charges apply only while the instance runs the batch job. Once the job completes and the instance stops, software host-hour charges stop accruing. Underlying AWS infrastructure fees may still apply separately. The request-based dimension charges only when inference calls are made.
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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
The model provides propensity estimates based on gender, age range, income range, and zip code. See the sample notebook for details concerning input variables and mappings.
- Input MIME type
- text/csv
1,5,24,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0
https://prosper-sample-batch.s3.us-east-2.amazonaws.com/batch_input_basic_geo.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Gender | Integer (0, 1)
0 = Female
1 = Male
| Type: Categorical
Allowed values: 0,1 | Yes |
Age Range | (Integer, 1-6)
1 = 18-24
2 = 25-34
3 = 35-44
4 = 45-54
5 = 55-64
6 = 65+
| Type: Categorical
Allowed values: 1,2,3,4,5,6 | Yes |
Household Income | (Integer, 0-24)
0 = Less than 10,000
1 = 10,000-14,999
2 = 15,000-19,999
3 = 20,000-24,999
4 = 25,000-29,999
5 = 30,000-34,999
6 = 35,000-39,999
7 = 40,000-44,999
8 = 45,000-49,999
9 = 50,000-54,999
10 = 55,000-59,999
11 = 60,000-64,999
12 = 65,000-69,999
13 = 70,000-74,999
14 = 75,000-79,999
15 = 80,000-84,999
16 = 85,000-89,999
17 = 90,000-94,999
18 = 95,000-99,999
19 = 100,000-109,999
20 = 110,000-119,999
21 = 120,000-129,999
22 = 130,000-139,999
23 = 140,000-149,999
24 = 150,000 or more
| Type: Categorical
Allowed values: 0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24 | Yes |
Zip Code | Five digit zip code as integer.
The model requires that the zip code be replaced by a set of 25 binary variables that represent special information regarding the zip. Prosper provides a file that maps every zip code into two integer values (division and cluster). These values are then converted into a set of binary values in a manner similar to one-hot encoding. The mapping file as well as the conversion routines are provided with the sample notebook.
| Type: Integer | Yes |
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