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A study, analysis and visualization of factors and relationships between those factors which determine the rate of interest and also if a person is a potential loan defaulter by using ML.

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AyusheGangal/Loan-or-Not

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Loan-or-Not

Loan defaulter prediction is extremely important and is widely used by banks and private loan providers all around the world to determine if a person would be able to repay the dept or not, and is used to determine if they should be given a loan or not. Machine Learning algorithms are being utilized for this task as they provide a near perfect estimate and are able to identify the important factors which contribute in making the estimate near perfect. In this project, I aim to study, analyze and visualize various factors and relationships between those factors which contribute in determining the rate of interest of the loan amount and also if a person is a potential loan defaulter.

The major questions I’m trying to answer with this project are:

  1. Does the amount of loan vary with the purpose which the loan has been taken, for two different loan terms?
  2. Is there a geographical connection between the loan amount for United States or not, if yes, which state has the highest number of loan defaulters?
  3. How does the amount of loan vary with the annual income of the borrower?
  4. Is there a relationship between the amount of loan, purpose of loan and the type of application for the loan?
  5. How the grade of the loan influences the rate of interest of the loan?

The pre-processing and machine learning algorithms have been applied using Python and can be found in 'Loan-or-Not/Loan-or-Not.py' or 'Loan-or-Not/Loan-or-Not.ipynb'

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A study, analysis and visualization of factors and relationships between those factors which determine the rate of interest and also if a person is a potential loan defaulter by using ML.

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