Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a consumer has Fibromyalgia
Overview
Prosper Insights & Analytics' propensity model predicts the probability that a U.S. adult consumer has a specific health condition. Based on a set of basic demographics, the model identifies individuals who are likely to have the health condition. The model was trained with data from Prosper's large Media Behaviors & Influence (MBI) study (N=16,619).
Highlights
- Enhances digital and offline targeting by identifying individuals our likely to have a specific health condition.
- 100% Privacy Compliant Models. No PII Used. HIPAA compliant.
- Based on unique large sample consumer survey data (N=16,619).
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 | $500.00 |
ml.m4.xlarge Inference (Real-Time) Recommended | Model inference on the ml.m4.xlarge instance type, real-time mode | $1.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $500.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $500.00 |
ml.m5.24xlarge Inference (Batch) | Model inference on the ml.m5.24xlarge instance type, batch mode | $500.00 |
ml.m5.12xlarge Inference (Batch) | Model inference on the ml.m5.12xlarge instance type, batch mode | $500.00 |
ml.m4.4xlarge Inference (Real-Time) | Model inference on the ml.m4.4xlarge instance type, real-time mode | $1.00 |
ml.m4.2xlarge Inference (Real-Time) | Model inference on the ml.m4.2xlarge instance type, real-time mode | $1.00 |
ml.m5.24xlarge Inference (Real-Time) | Model inference on the ml.m5.24xlarge instance type, real-time mode | $1.00 |
ml.m5.12xlarge Inference (Real-Time) | Model inference on the ml.m5.12xlarge instance type, real-time mode | $1.00 |
Dimensions summary
You pay by the host hour based on the compute instance you run this predictive model on. Ten options split into two processing modes: batch inference, which scores data in bulk, and real-time inference, which scores on demand. Within each mode, you pick from five instance sizes across the m4 and m5 families. Instances scale from smaller (ml.m4.xlarge) up to larger configurations (ml.m5.24xlarge). Larger instances carry more compute capacity per hour. Choose the mode that fits your workload, then the instance size that matches your throughput needs. Billing accrues only for the hours you use.
Top-of-mind questions for buyers
A host hour is one hour that a chosen compute instance runs the model. Billing meters the running time of the instance you select. You accrue charges only while the instance is active. Larger instance types carry more compute capacity per hour but bill on the same host-hour unit.
Batch inference scores data in bulk, so you run an instance long enough to process a dataset, then stop. Real-time inference keeps an instance running to score requests on demand, so hours accrue while the endpoint stays live. Batch suits scheduled scoring jobs; real-time suits continuous, on-demand scoring.
Charges accrue per host hour only while the instance runs. A stopped instance does not accrue software host-hour charges. For batch jobs, you pay for the hours the instance processes data. For real-time scoring, hours accrue as long as the endpoint stays running, even between requests.
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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
Minor fixes to the underlying software.
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://github.com/goprosper/prosper-sagemaker-basic-geo/blob/master/prosper_data/sample_basic_zip.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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