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Develop a model to predict for 'quality' applicants. In this project, 'quality' applicants are those who reach a key part of the loan application process.

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vishwasbasotra/E-Signing-of-Loan-Based-on-Financial-History

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E-Signing of Loan Based on Financial History

Lending companies work by analyzing the financial history of their loan applicants and choosing whether or not the applicant is too risky to be given a loan. If the applicant is not, the company determines the terms of the loan. To acquire these applicants, companies can organically receive them through their websites/apps, often with the help of advertisement campaigns. Other times, lending companies partner with peer-to-peer(P2P) lending marketplaces, to acquire leads of possible applicants. Some example marketplaces include Upstart, Lending Tree, and Learning Club. In this project, we are going to assess the 'quality' of the leads our company receives from the marketplaces.

  • Market: The target audience is the set of loan applicants who reached out through an intermediary marketplace.
  • Product: Loan
  • Goal: Develop a model to predict for 'quality' applicants. In this project, 'quality' applicants are those who reach a key part of the loan application process.

Technical Requirements

This Project has the following software requirements:

  • Python 3.7
  • Anaconda
  • Jupyter Notebook

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Develop a model to predict for 'quality' applicants. In this project, 'quality' applicants are those who reach a key part of the loan application process.

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