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Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

DeepInsights Branch Location Predictor

By: Mphasis Latest Version: 2.3

This solution performs relative bench marking of Metropolitan Statistical Areas (MSA) and predicts their attractiveness

Product Overview

This solution empirically measures demand fulfillment potential of a geographical region through multi-criteria decision making and provides market attractiveness scores. The output will be scores (between 0-1) for each region. Higher score signifies higher demand fulfillment in a market. This can be used to analyze geographical regions for business decisions such as new branch location setup, branch expansion, market-entry & competition analysis. Mphasis DeepInsights is a cloud-based cognitive computing platform that offers data extraction and predictive analytics capabilities.

Key Data

Version
By Mphasis
Categories
Type Model Package
Fulfillment Methods Amazon SageMaker

Usage Information

Fulfillment Methods

Amazon SageMaker

Input

1) The input file should be in the "csv" form with 'utf-8' encoding. 2) The input can have a maximum of 1000 records 3) Supported content types: text/csv 4) Input should contain parameters provided by the HMDA (Home Mortgage Disclosure Act) dataset (Largest open-source mortgage data set in the US) 5) Please refer the following link to get the list of input parameters and the sample input file: https://tinyurl.com/tga5hsd

Output

1) Sample interim output file from the model will contain the following fields: | MSA_Name | MSAID | Supply-Demand_fullfillment | | ABILENE, TX | 10180 | 0.93913999 |

2) Final ouput file will contain the following columns : (please refer to the provided Jupyter notebook to generate the final output)

  • MSA_Name: Name of the Metro statistical Area
  • MSA_Demad_Score_below_0.6 :MSAs where Mortagage Demand is not getting addressed hence these regions are more attractive.
  • MSA_Demad_Score_between_0.6_to_0.8:MSAs where Mortagage Demand is getting partially addressed.
  • MSA_Demad_Score_above_0.8:MSAs where Mortagage Demand is getting addressed.

3) Supported Content type: text/csv 4) Sample output : https://tinyurl.com/wcyx88h

Invoking endpoint

AWS CLI Command

If you are using real time inferencing, please create the endpoint first and then use the following command to invoke it:

aws sagemaker-runtime invoke-endpoint --endpoint-name "endpoint-name" --body fileb://input.csv --content-type text/csv --accept text/csv result.csv

Substitute the following parameters:

  • endpoint-name - name of the inference endpoint where the model is deployed
  • input.csv - input file
  • text/csv - MIME type of the given input file (above)
  • result.csv - filename where the inference results are written to.

Resources

Sample Notebook Sample Input Sample Output

Additional Resources

End User License Agreement

By subscribing to this product you agree to terms and conditions outlined in the product End User License Agreement (EULA)

Support Information

DeepInsights Branch Location Predictor

For any assistance reach out to us at:

https://www2.mphasis.com/AWS-Marketplace-Support-LP.html

AWS Infrastructure

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Refund Policy

Currently we do not support refunds, but you can cancel your subscription to the service at any time.

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