Sold by: MphasisÂ
Deployed on AWS
The solution identifies items bought together frequently in a retail shopping cart.
Overview
Market Basket Analysis uncovers associations between articles and identifies the frequent products which are likely to be purchased together by analyzing large volumes of transactional data. Knowing what products people purchase together can be advantageous to an e-commerce website or any retailer store in preparing recommendations and promotions.
Highlights
- Identifies the products purchased together frequently and generates association rules with details of antecedent support, rule support, confidence, lift, conviction and leverage.
- Anticipate customer purchase behaviour by using statistical affinity calculations to increase cross-selling and make promotions more effective.
- Mphasis HyperGraf is an omni-channel customer 360 analytics solution. Need customized Deep Learning/NLP solutions? Get in touch!
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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.m5.large Inference (Batch) Recommended | Model inference on the ml.m5.large instance type, batch mode | $8.00 |
ml.m5.large Inference (Real-Time) Recommended | Model inference on the ml.m5.large instance type, real-time mode | $4.00 |
ml.m4.4xlarge Inference (Batch) | Model inference on the ml.m4.4xlarge instance type, batch mode | $8.00 |
ml.m5.4xlarge Inference (Batch) | Model inference on the ml.m5.4xlarge instance type, batch mode | $8.00 |
ml.m4.16xlarge Inference (Batch) | Model inference on the ml.m4.16xlarge instance type, batch mode | $8.00 |
ml.m5.2xlarge Inference (Batch) | Model inference on the ml.m5.2xlarge instance type, batch mode | $8.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $8.00 |
ml.m4.2xlarge Inference (Batch) | Model inference on the ml.m4.2xlarge instance type, batch mode | $8.00 |
ml.c5.2xlarge Inference (Batch) | Model inference on the ml.c5.2xlarge instance type, batch mode | $8.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $8.00 |
Vendor refund policy
Currently we do not support refunds, but you can cancel your subscription to the service at any time.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA)Â .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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
Bug fixes and updates
Additional details
Inputs
- Summary
- Sample input file:
- Input file should have following columns:
- InvoiceID: This is the Invoice Number which is the systematically assigned sequential code unique to each invoice.
- SKUID: Stock Keeping Unit ID.
- Item: description of item, a string, name of item along with brand name and color name.
- Input MIME type
- text/csv, text/plain
https://github.com/Mphasis-ML-Marketplace/Mphasis-HyperGraf-Market-Basket-Analysis/blob/main/input
https://github.com/Mphasis-ML-Marketplace/Mphasis-HyperGraf-Market-Basket-Analysis/blob/main/input
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
InvoiceID | This is the Invoice Number which is the systematically assigned sequential code unique to each invoice. | Type: Integer | Yes |
SKUID | Stock Keeping Unit ID | Type: Integer | Yes |
Item | Description of item, a string, name of item along with brand name and color name. | Type: FreeText | Yes |
Resources
Vendor resources
Support
Vendor support
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