Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a consumer is Comfortable with Vacation Travel.
Overview
This Pandemic Propensity Model determines the probability that a US adult is Comfortable with Vacation Travel. Lift over Random 1.24
This Pandemic Propensity model is one of a series of consumer classification models based on data from over 31,000 US adults surveyed between April to July 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 31,000 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 1.24
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 for model inference, billed per host hour on the machine learning instance you choose. Pricing splits into two modes: batch, which scores data in bulk jobs, and real-time, which serves predictions on demand. Within each mode, you pick from general-purpose (m4, m5), compute-optimized (c4, c5), and GPU (p2, p3) instance families. Larger instance sizes carry higher hourly rates. Your total cost depends on which instance type you run, the mode you select, and how many hours you use. There is no upfront commitment.
Top-of-mind questions for buyers
One host hour is one hour that a single machine learning instance runs your inference job. Charges start when the instance launches and stop when it shuts down. If you run several instances at once, each one bills its own host hours simultaneously.
Batch mode runs the instance only while it scores a bulk job, then shuts down, so you pay host hours for that job. Real-time mode keeps an instance running to serve on-demand predictions, so it accrues host hours the entire time the endpoint stays active.
Yes. Real-time mode bills per host hour while the endpoint stays running, whether or not it receives prediction requests. To stop software charges, you must shut down the endpoint. Batch mode avoids idle time because the instance runs only during a scoring job.
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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.
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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://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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Similar products
Zip Propensity Data is derived from Prosper's US Monthly Consumer survey. The survey data is modeled to Zip Code level. Source data was collected in July 2020, during the Covid-19 Coronavirus Pandemic, capturing behavior changes and preferences of over 7,800 adults during the National Emergency. Zero PII. CCPA and HIPAA Compliant. Data file lists the Percentage Propensity by Zip for the answer "Comfortable with Vacation Travel."
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