Sold by: Prosper Insights & Analytics
Deployed on AWS
Propensity model that determines the probability that a consumer works on their own car or truck
4
Overview
Prosper Insights & Analytics' propensity model predicts the probability that a U.S. adult consumer works on their own car. Based on a set of basic demographics, the model identifies individuals who are likely to be an automotive DIYer. 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 likely to be Automotive DIYers.
- 100% Privacy Compliant Models. No PII Used.
- Based on unique large sample consumer survey data (N=16,619).
Details
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Latest version
Deployed on AWS
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Pricing
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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.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $500.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $500.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $500.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge 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.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $500.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $500.00 |
Dimensions summary
You pay by the hour for each AWS machine learning instance you run to score consumers with this propensity model. Pricing splits into two modes: Batch, which processes datasets in scheduled runs, and Real-Time, which serves live predictions. Within each mode, you choose from many instance types across the m4, m5, c4, c5, p2, and p3 families. General-purpose and compute-optimized instances suit standard scoring; GPU instances handle heavier workloads. Larger sizes within each family carry higher hourly rates. You select the mode and instance size that fit your workload, and charges accrue only for hours used.
Top-of-mind questions for buyers
One HostHrs unit is one hour that a chosen machine learning instance runs to score consumers. Billing counts each active instance-hour. You pay for the hours the instance runs, whether processing a batch dataset or serving live requests. Stopped instances stop accruing software charges.
Batch mode meters instance-hours during scheduled runs that score whole datasets at once. Real-Time mode meters instance-hours while a live endpoint stays running to answer prediction requests. Both bill per hour of the same instance types. Batch suits periodic scoring; Real-Time suits always-on prediction serving.
This model predicts consumer propensity for car and truck do-it-yourself activity, built on Prosper's consumer survey data. You run it against your audiences on the instance you choose. The hourly instance charge is what appears in this listing; it covers running the model to generate scores.
www.prospermodelfactory.com
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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://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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Customer reviews
Stephane L.
Develop a greater knowledge of my car.
Reviewed on Dec 06, 2024
Review provided by G2
What do you like best about the product?
Propensity US: Car-Truck DIYer targets directly car and truck owners who like to fix their cars and trucks by themselves. It has a repair database which contains information on steps, troubleshooting, and parts lookup for different car brands and models.
What do you dislike about the product?
I started getting annoyed when I was looking for information on my used European car. The platform provided a very limited source of information that did not meet the needs of my vehicle project.
What problems is the product solving and how is that benefiting you?
Propensity US makes repair information for cars and trucks easily available to the DIYers in the US. Rather than having to rely on mechanics, I can identify and possibly solve some problems independently, thanks to the platform’s comprehensive instructions.