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This solution template shows how to build and deploy a loan-credit-risk solution with Microsoft ML Server

License: MIT License

Jupyter Notebook 14.38% R 48.81% PowerShell 16.77% Shell 0.28% TSQL 19.75%

r-server-loan-credit-risk's Introduction

Loan Credit Risk

Predict risk of customers defaulting on loans.

Deploy to Azure on SQL Server

Deploy to Azure (SQL Server)

More samples and information

Discover more examples at Microsoft Machine Learning Server

For all documentation, visit the Loan Credit Risk website.

NOTE: Please don't use "Download ZIP" to get this repository, as it will change the line endings in the data files. Use "git clone" to get a local copy of this repository.

Contributing

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

r-server-loan-credit-risk's People

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r-server-loan-credit-risk's Issues

Problem with ODBC using Windows authentication on local SQL Server 2017 Developer Edition

When running on-prem using Windows authentication on local SQL Server 2017 Developer Edition it works uploading the first three tables:
Loan_sql <- RxSqlServerData(table = "Loan", connectionString = connection_string)
Borrower_sql <- RxSqlServerData(table = "Borrower", connectionString = connection_string)
Merged_sql <- RxSqlServerData(table = "Merged", connectionString = connection_string, stringsAsFactors = T)

But when in step 1

crashing at step1, [1] "Replacing missing values with the global mean or mode..."

It fails to insert into Merged_Cleaned
Merged_Cleaned_sql <- RxSqlServerData(table = "Merged_Cleaned", connectionString = connection_string)

Not sure why is this happening, but then as is testing machine I used sa in instead of trusted connection setting in connection string:

Was crashing with local Windows user with Dual authentication SQL mode and local db using Trusted_Connection=Yes

crashing at step1, [1] "Replacing missing values with the global mean or mode..."

connection_string <- sprintf("Driver=SQL Server;Server=%s;Database=%s;Uid=sa;Pwd=mypass", server, db_name)

connection_string <- sprintf("Driver=SQL Server;Server=%s;Database=%s;Trusted_Connection=Yes", server, db_name)

Hi

Where could i see the prediction of credit risk in your project ?

thanks

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