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Lending club case study project by karan singh bisht and Surendra babu kunka (UPGRAD PROJECT)

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LEANDING CLUB CASE STUDY

Business Understanding

You work for a consumer finance company which specialises in lending various types of loans to urban customers. When the company receives a loan application, the company has to make a decision for loan approval based on the applicant’s profile. Two types of risks are associated with the bank’s decision:

  1. If the applicant is likely to repay the loan, then not approving the loan results in a loss of business to the company

  2. If the applicant is not likely to repay the loan, i.e. he/she is likely to default, then approving the loan may lead to a financial loss for the company

Table of Contents

  • General Information
  • Technologies Used
  • Conclusions
  • Technology used
  • Acknowledgements

General Information

When a person applies for a loan, there are two types of decisions that could be taken by the company:

Loan accepted: If the company approves the loan, there are 3 possible scenarios described below:

Fully paid: Applicant has fully paid the loan (the principal and the interest rate)

Current: Applicant is in the process of paying the instalments, i.e. the tenure of the loan is not yet completed. These candidates are not labelled as 'defaulted'.

Charged-off: Applicant has not paid the instalments in due time for a long period of time, i.e. he/she has defaulted on the loan

DATA_DICTIONARY is also available.

Data cleaning used for removing few columns and standerlize data

Conclusions

  • Mostly people take upto 30k Loan
  • People are taking more loan to pay other debt
  • Higher the term and loan amount, higher Charged off's
  • Intrest rate is also increased when Loan amount increase
  • Loan amount and installment has high correlation
  • Annual Income and int_rate has negative correlation

Technologies Used

  • NUMPY , PANDA
  • PYTHON
  • MATPLOTLIB , SEABORN

Acknowledgements

Give credit here.

  • This project was completed by learnings of python visualization
  • Used EDA techniques which was covered so far
  • This project was based on lending club
  • Univariate study and project code written by karan
  • Further approaches and bivaraiate written by Surendra babu kunka

Contact

Created by KARAN Singh BISHT and Surendra babu kunka batch : ML C44

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Lending club case study project by karan singh bisht and Surendra babu kunka (UPGRAD PROJECT)

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