Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a consumer Drinks Vodka.
Overview
This post-pandemic Propensity Model determines the probability that a US adult Drinks Vodka. Lift over Random 1.76. 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 1.76
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 |
|---|---|---|
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
You pay based on usage, with no upfront commitment. Five dimensions bill by host hours for batch inference, each tied to a different compute instance size. These instances run through a managed machine learning platform in batch mode, where the model scores data in bulk rather than in real time. Larger instance types provide more compute power, so you choose the size that fits your workload. A sixth dimension bills per inference request, charging by the number of predictions the model produces. This lets you match cost to either run time or request volume.
Top-of-mind questions for buyers
A host hour is one hour that a single compute instance runs while scoring your data in bulk. You pay for the time the instance stays active during the batch job. Metering stops when the job finishes and the instance is no longer running.
The five host-hour dimensions bill for instance run time in batch mode. The per-request dimension bills for each prediction the model produces. They meter separately. For steady bulk jobs, host hours usually drive cost. For request-driven scoring, prediction volume drives cost.
Charges accrue only while an instance is active and processing a batch job. When the job completes and the instance stops, host-hour software charges stop. Underlying AWS infrastructure fees may still apply separately for any retained resources.
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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
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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 |
Support
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