https://github.com/abhinav-codealchemist/predicting-lending-and-loan-repayment-using-ml

To predict whether the loan will be fully funded, based on the scoring of and information related to the application.
https://github.com/abhinav-codealchemist/predicting-lending-and-loan-repayment-using-ml

jupyter-notebook lending-club loan loan-repayment machine-learning prediction python scoring

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To predict whether the loan will be fully funded, based on the scoring of and information related to the application.

• Host: GitHub
• URL: https://github.com/abhinav-codealchemist/predicting-lending-and-loan-repayment-using-ml
• Owner: abhinav-codealchemist
• Created: 2018-11-05T10:21:40.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2019-03-03T15:14:22.000Z (about 6 years ago)
• Last Synced: 2025-01-13T09:34:30.040Z (4 months ago)
• Topics: jupyter-notebook, lending-club, loan, loan-repayment, machine-learning, prediction, python, scoring
• Language: Jupyter Notebook
• Size: 155 KB
• Stars: 0
• Watchers: 0
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
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# Predicting Lending And Loan Repayment Using ML

To predict whether the loan will be fully funded, based on the scoring of and information related to the application.

I used data from the "Lending Club" to develop understanding of machine learning concepts. The Lending Club is a peer-to-peer lending company. It offers loans which are funded by other people. In this sense, the Lending Club acts as a hub connecting borrowers with investors. The client applies for a loan of a certain amount, and the company assesses the risk of the operation. If the application is accepted, it may or may not be fully covered. I focused on the prediction of whether the loan will be fully funded, based on the scoring of and information related to the application.

I used the partial dataset of period 2007–2011. Framing the problem a little bit more, based on the information supplied by the customer asking for a loan, we want to predict whether it will be granted up to a certain threshold 'thr'. The attributes we use in this problem are related to some of the details of the loan application, such as amount of the loan applied for the borrower, monthly payment to be made by the borrower if the loan is accepted, the borrower’s annual income, the number of incidences of delinquency in the borrower’s credit file, and interest rate of the loan, among others.

https://www.lendingclub.com/info/download-data.action

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