In this model, we built a loan approval model in which we can determine whether a person should get a loan or not, and is based on various factors.
We can define a loan as a type of credit vehicle in which a sum of money is lent to another party in exchange for future repayment of the value or principal amount. In many cases, the lender also adds interest or finance charges to the principal value, which the borrower must repay in addition to the principal balance. (credit: Investopedia)
The dataset used for this model was taken from Kaggle: Loan Prediction Problem Dataset.
Each Applicant is attributed with the following columns in this data set and is as follows:
| Column | Description |
|---|---|
| Loan_ID | Unique Loan ID |
| Gender | Male/Female |
| Married | Whether Married: Yes/No |
| Dependents | No. of people depending on the Applicant |
| Education | Graduate/Undergraduate |
| Self_Employment | Whether Self_Employment : Yes/No |
| ApplicantIncome | Applicant Income |
| CoapplicantIncome | Co-Applicant Income |
| LoanAmount | Loan Amount (in thousands) |
| Loan_Amount_Term | Loan Duration |
| Credit_History | Credit History of the Applicant |
| Property_Area | Urban/Semiurban/Rural |
| Loan_Status | Whether Loan Approved: Yes/No |
The dimensions of the dataset are 614*13
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- Random Forest Classifier
- Decision Tree Classifier
- Support Vector Classifier