Should This Loan Be Approved Or Denied?
A large and rich dataset from the U.S. Small Business Administration (SBA)
@kaggle.mirbektoktogaraev_should_this_loan_be_approved_or_denied
About this Dataset
Should This Loan Be Approved Or Denied?
Context
The dataset is from the U.S. Small Business Administration (SBA)
The U.S. SBA was founded in 1953 on the principle of promoting and assisting small enterprises in the U.S. credit market (SBA Overview and History, US Small Business Administration (2015)). Small businesses have been a primary source of job creation in the United States; therefore, fostering small business formation and growth has social benefits by creating job opportunities and reducing unemployment.
There have been many success stories of start-ups receiving SBA loan guarantees such as FedEx and Apple Computer. However, there have also been stories of small businesses and/or start-ups that have defaulted on their SBA-guaranteed loans.
Content
Shape of the data: 899164 rows and 27 columns
Data Dictionary
| Variable Name | Description |
|---|---|
| LoanNr_ChkDgt | Identifier Primary key |
| Name | Borrower name |
| City | Borrower city |
| State | Borrower state |
| Zip | Borrower zip code |
| Bank | Bank name |
| BankState | Bank state |
| NAICS | North American industry classification system code |
| ApprovalDate | Date SBA commitment issued |
| ApprovalFY | Fiscal year of commitment |
| Term | Loan term in months |
| NoEmp | Number of business employees |
| NewExist | 1 = Existing business, 2 = New business |
| CreateJob | Number of jobs created |
| RetainedJob | Number of jobs retained |
| FranchiseCode | Franchise code, (00000 or 00001) = No franchise |
| UrbanRural | 1 = Urban, 2 = rural, 0 = undefined |
| RevLineCr | Revolving line of credit: Y = Yes, N = No |
| LowDoc | LowDoc Loan Program: Y = Yes, N = No |
| ChgOffDate | The date when a loan is declared to be in default |
| DisbursementDate | Disbursement date |
| DisbursementGross | Amount disbursed |
| BalanceGross | Gross amount outstanding |
| MIS_Status | Loan status charged off = CHGOFF, Paid in full =PIF |
| ChgOffPrinGr | Charged-off amount |
| GrAppv | Gross amount of loan approved by bank |
| SBA_Appv | SBA’s guaranteed amount of approved loan |
Description of the first two digits of NAICS.
| Sector | Description |
|---|---|
| 11 | Agriculture, forestry, fishing and hunting |
| 21 | Mining, quarrying, and oil and gas extraction |
| 22 | Utilities |
| 23 | Construction |
| 31–33 | Manufacturing |
| 42 | Wholesale trade |
| 44–45 | Retail trade |
| 48–49 | Transportation and warehousing |
| 51 | Information |
| 52 | Finance and insurance |
| 53 | Real estate and rental and leasing |
| 54 | Professional, scientific, and technical services |
| 55 | Management of companies and enterprises |
| 56 | Administrative and support and waste management and remediation services |
| 61 | Educational services |
| 62 | Health care and social assistance |
| 71 | Arts, entertainment, and recreation |
| 72 | Accommodation and food services |
| 81 | Other services (except public administration) 92 Public administration |
Acknowledgements
Original data set id from “Should This Loan be Approved or Denied?”: A Large Dataset with Class Assignment Guidelines.
by: Min Li, Amy Mickel & Stanley Taylor
To link to this article: https://doi.org/10.1080/10691898.2018.1434342
Inspiration
Good luck with predictions!