Sold by: ZabdaÂ
Deployed on AWS
Dynamic smart pricing ML model to predict freight cost for spot trucking market
Overview
Data-driven Machine Learning model predicts spot price for freight brokers by considering data inputs from history, live information, current market conditions, customer and carrier profiles to maximize revenue for every load.
Highlights
- * Model uses internal historical data from customer to predict the sell price and overlay with business inputs to present to Carrier Reps with 3 Ranges of price points. Min, Median and Max. * Maximization of price based on Carrier and Customer Characteristics. * Model also uses DAT, FW, SONAR data around Average price per Markets, Load / Truck Ratio,freight Sonar Indices to characterize the Inbound / Outbound markets and adjust the predicted price accordingly
- * Metrics are broken down to measure at carrier and customer level to monitor the effectiveness of suggested prices * Model can be further customized and fine tuned according to the business needs
Details
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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
Beta release
Additional details
Inputs
- Summary
All the inputs are mandatory
Distance in KMS i.e. 63.3
Weight in pounds i.e. 43500
order lead time in days i.e. 28.0
OriginPostalcode (US Postal code format - Five digit code) i.e. 38261
destinationPostalcode (US Postal code format - Five digit code) i.e. 38301
example: 63.3,43500,28.0,38261,38301
- Input MIME type
- text/csv, application/json, application/jsonlines
https://github.com/ZabdaTechnologies/Dynamic-spot-pricing-Sample-Notebooks/blob/main/sample_notebook/data/input/real-time/input.csv
https://github.com/ZabdaTechnologies/Dynamic-spot-pricing-Sample-Notebooks/blob/main/sample_notebook/data/input/batch/batchtransform_test_input.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
Distance | Distance in KMS
Weight in pounds
order lead time in days
OriginPostalcode (US Postal code format - Five digit code)
destinationPostalcode (US Postal code format - Five digit code) | Type: Continuous | Yes |
Weight | Distance in KMS
Weight in pounds
order lead time in days
OriginPostalcode (US Postal code format - Five digit code)
destinationPostalcode (US Postal code format - Five digit code) | Type: Continuous | Yes |
orderleadtime | Distance in KMS
Weight in pounds
order lead time in days
OriginPostalcode (US Postal code format - Five digit code)
destinationPostalcode (US Postal code format - Five digit code) | Type: Continuous | Yes |
OriginPostalcode | Distance in KMS
Weight in pounds
order lead time in days
OriginPostalcode (US Postal code format - Five digit code)
destinationPostalcode (US Postal code format - Five digit code) | Type: Continuous | Yes |
destinationPostalcode | Distance in KMS
Weight in pounds
order lead time in days
OriginPostalcode (US Postal code format - Five digit code)
destinationPostalcode (US Postal code format - Five digit code) | Type: Continuous | Yes |
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