This project is a Machine Learning model that predicts whether a loan will be approved based on various applicant details. It's built using Python and libraries like Pandas, NumPy, and Scikit-learn.
- Predicts loan approval status (Approved / Rejected)
- Handles missing values and encodes categorical data
- Simple, interpretable model (Logistic Regression)
- Easy to extend with advanced ML models
The dataset includes the following features:
| S.No | Column Name | Description |
|---|---|---|
| 1 | Loan_ID | A unique ID assigned to each loan |
| 2 | Gender | Gender of the applicant (Male / Female) |
| 3 | Married | Marital Status of the applicant (Yes / No) |
| 4 | Dependents | Number of dependents the applicant has |
| 5 | Education | Education level of the applicant (Graduate / Not Graduate) |
| 6 | Self_Employed | Employment status (Self-employed: Yes / No) |
| 7 | ApplicantIncome | Monthly income of the applicant |
| 8 | CoapplicantIncome | Monthly income of the co-applicant |
| 9 | LoanAmount | Loan amount (in thousands) |
| 10 | Loan_Amount_Term | Loan term (in months) |
| 11 | Credit_History | Credit history (1 = good, 0 = bad / no history) |
| 12 | Property_Area | Property location (Urban / Rural / Semi-urban) |
| 13 | Loan_Status | Loan approval status (Y = Yes, N = No) |
- Python 3.x
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- Clone the repository:
git clone https://github.com/your-username/loan-approval-predictor.git cd loan-approval-predictor