This is a binary classification machine learning project focused on determining the best model for predicting loan acceptance, coded in python.
The associated research paper can be found here
The dataset used for this project is the Loan Prediction Dataset from Kaggle. It consists of 13 columns and 614 rows, with the following features:
| Feature | Description | Data Type/Ranges |
|---|---|---|
| Loan_ID | Unique identifier for each loan application | - |
| Gender | Gender of the applicant | Categorical: Male or Female |
| Married | Marital Status of the applicant | Categorical: Yes or No |
| Dependents | Number of dependants if any | Categorical: 0, 1, 2, or 3 |
| Education | Educational Status | Categorical: Graduate or Not Graduate |
| Self Employed | Defines if the applicant is self-employed | Categorical: Yes or No |
| Applicant Income | Applicant income | Numerical: From 150 to 81000 |
| Coapplicant Income | Co-applicant income | Numerical: From 0 to 41667 |
| Loan Amount | Loan amount (in thousands) | Numerical: From 9 to 650 |
| Loan Amount Term | Terms of loan (in months) | Numerical: From 12 to 480 |
| Credit History | Credit history of individual’s repayment of debts | Categorical: 0 or 1 |
| Property Area | Area of property | Categorical: Urban, Semiurban, or Rural |
| Loan Status (Target) | The acceptance or rejection of the loan | Categorical: Yes or No |
This project is licensed under the MIT License