All Lending Club Loan Data 🎓

Dataset: Lending Club Loan Data

Column Description: LC Data Dictionary

For Collaboration 🍴

PowerPoint

• Link: https://docs.google.com/presentation/d/11gyBqXtqfMvd8uPNTza4AVfDaESa5Iql/edit?usp=sharing&ouid=101303611464720615732&rtpof=true&sd=true

File Structuring

• DataSet: Include all .csv files. Also, find the LCDataDictionary.xlsx file for column information.

  • Note: After adding any dataset, review the .gitignore file to avoid committing large datasets.

• RMarkDownHTML: Folder for all .Rmd files. Please work in this folder.

• RMarkdownHTML: Folder to store all HTMLs knitted from .Rmd scripts.

• RMarkdownHTML/readMe.md: Once you complete an HTML answering a SMART question, document it in readMe.md with the purpose of that HTML and your name. This will help in navigating and compiling a single HTML later. Use Markdown Live Prieview to see a preview before commiting.

Git

• Each collaborator should create their own branch from the main branch with the branch name in the format LastName-FirstName to facilitate easy merge requests.

• Before making changes, check if anyone else is working on the same file. It's best for each person to work on a separate file. After completing your work, push your branch. Once reviewed, we can merge it into the main branch to avoid merge conflicts.

Targets

1. Loan Default Prediction

• loan_status: This column reflects the current status of the loan, such as "Fully Paid", "Charged Off", "Default", etc. It is commonly used for predicting whether a loan will default or be paid back.

  • Targets: Charged Off, Fully Paid, Default, etc.

• charged_off (binary flag you may create): Convert the loan_status into a binary variable (e.g., 1 for default/charged off, 0 for fully paid) to train a classification model.

2. Hardship or Financial Distress Prediction

• hardship_flag: This indicates whether the borrower has faced financial hardship (Y/N). You can use this as a target for predicting borrowers who are likely to face financial distress.

• debt_settlement_flag: This indicates whether the borrower has entered into a debt settlement (Y/N). You can use this to predict which borrowers may seek debt settlement.

3. Loan Prepayment Prediction

• loan_status (focus on Fully Paid or Current with early prepayment): You can predict if a borrower will fully pay off the loan ahead of time.

• total_rec_prncp vs. loan_amnt: If a borrower pays off the principal (total_rec_prncp) early compared to the total loan amount (loan_amnt), this could indicate prepayment behavior.

4. Interest Rate Prediction

• int_rate: The interest rate on the loan can be used as a target if you're trying to predict loan pricing based on borrower characteristics, credit history, etc.

5. Borrower Risk/Grade Prediction

• grade: The loan grade (A, B, C, etc.) assigned by Lending Club, representing the perceived credit risk of the borrower, can be a target for classifying or predicting risk levels.

• sub_grade: More granular than grade, this offers finer levels of credit risk, such as A1, A2, etc., and could serve as a target.

Other Potential Targets:

• recoveries: The amount recovered from a charged-off loan can be a target for predicting the recovery amount.
• term: The loan term (36 months or 60 months) could be used to predict whether borrowers prefer short- or long-term loans based on their profiles.

TODO ✈️

Task	Assignee	Branch Name	Status (❓, 🔄, ✅)
Git Setup	Singh, Aakash	Main	✅ Done
Invistigate Dataset	Singh, Aakash	singh-aakash	✅ Done
Completed Intial Phase	Singh, Aakash	singh-aakash	✅ Done
Merge with Main Branch	Singh, Aakash	singh-aakash	✅ Done
Merge All the branches	Singh, Aakash	singh-aakash	✅ Done
Compile one Rmd File	All	Main	✅ Done
Publish the work	All	Main	✅ Done

Contributors

Aakash Singh 💻 📖

ugantulga 💻 📖

msyago 💻 📖

Ayush_14 💻 📖