https://github.com/jatin-mehra119/loan_dataset

Predict Creditworthiness: Determine if a customer meets the credit underwriting criteria of LendingClub.com.
https://github.com/jatin-mehra119/loan_dataset

custom-transformer dataanalysis datascience loan-application machine-learning pandas pkl-model sklearn-classify

Last synced: about 1 month ago
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Predict Creditworthiness: Determine if a customer meets the credit underwriting criteria of LendingClub.com.

• Host: GitHub
• URL: https://github.com/jatin-mehra119/loan_dataset
• Owner: Jatin-Mehra119
• Created: 2024-05-27T15:53:58.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-05-27T16:12:38.000Z (7 months ago)
• Last Synced: 2024-05-28T02:38:56.097Z (7 months ago)
• Topics: custom-transformer, dataanalysis, datascience, loan-application, machine-learning, pandas, pkl-model, sklearn-classify
• Language: Jupyter Notebook
• Homepage:
• Size: 2.46 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Dataset - loan data from LendingClub.com.

# Objective/Business Goal

1. **Predict Creditworthiness**: Determine if a customer meets the credit underwriting criteria of LendingClub.com.

2. **Data Visualization/EDA**: Perform exploratory data analysis and visualization to understand the data.

3. **Model Deployment**: Save the trained model for use in the company's other applications.

# More information about Dataset:

## Here are what the columns represent:

1. **credit.policy**: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise. (**target**)

2. **purpose**: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other").

3. **int.rate**: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.

4. **installment**: The monthly installments owed by the borrower if the loan is funded.

5. **log.annual.inc**: The natural log of the self-reported annual income of the borrower.

6. **dti**: The debt-to-income ratio of the borrower (amount of debt divided by annual income).

7. **fico**: The FICO credit score of the borrower.

8. **days.with.cr.line**: The number of days the borrower has had a credit line.

9. **revol.bal**: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).

10. **revol.util**: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).

11. **inq.last.6mths**: The borrower's number of inquiries by creditors in the last 6 months.

12. **delinq.2yrs**: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.

13. **pub.rec**: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).