Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a consumer is Planning to Purchase Pink.
Overview
This post-pandemic Propensity Model determines the probability that a US adult is Planning to Purchase Pink. Lift over Random 2.13. 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 2.13
Details
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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
You pay based on usage, with two pricing methods. Five dimensions charge by host hours for batch inference on specific SageMaker instance types: ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m5.12xlarge, and ml.m5.24xlarge. You pick the instance size that fits your workload, and your cost scales with the hours it runs. Larger instances offer more compute for heavier batch jobs. The sixth dimension, inference.count.m.i.c, charges per request instead of per hour. This lets you pay by inference volume rather than by runtime.
Top-of-mind questions for buyers
One host hour equals one hour that a chosen SageMaker instance runs a batch inference job. You are charged for each hour the instance stays active. Cost scales with runtime, so a job running two hours on one instance bills two host hours.
The host-hour dimensions meter time an instance runs, so cost tracks runtime. The inference.count.m.i.c dimension meters each inference request instead. You pay by the number of predictions made, not by how long a machine stays on. Volume-based work suits per-request; sustained batch runs suit host hours.
Host-hour charges apply only while the SageMaker instance actively runs a batch job. Once the batch job finishes and the instance stops, software host-hour charges stop. Underlying AWS storage or other resource fees may still apply, but the inference software meters running time only.
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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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