aman-prajapat/LoanDeafultPredictor

WebApp Link https://loandeafultpredictor.streamlit.app/

To create a README.md file for your Streamlit Loan Prediction Model application, you can use the following template:

# Loan Prediction Model Application

This Streamlit application predicts loan approval based on user-provided input parameters using a machine learning model. The model predicts whether a loan application will be approved or rejected based on factors such as annual income, loan amount, loan term, CIBIL score, residential assets, number of dependents, and employment status.

## Requirements

- Python 3.6 or higher
- Required libraries: `streamlit`, `joblib`

## Installation

1. Clone this repository.
2. Install the required libraries using `pip`:
   ```bash
   pip install streamlit joblib

Usage

To run the application, execute the following command in your terminal:

streamlit run app.py

Application Interface

• Employment Status: Select whether the applicant is employed or unemployed.
• Number of Dependent Members: Number of dependent family members (0 to 10).
• Annual Income: Annual income of the applicant (minimum value 0).
• Loan Amount: Amount of loan requested (minimum value 0).
• Loan Term (year): Duration of the loan in years (1 to 20 years).
• CIBIL Score: Credit score of the applicant (300 to 900).
• Residential Assets: Value of residential assets owned by the applicant (minimum value 0).

Predicting Loan Approval

• After entering the required information, click on the Predict button.
• The model predicts whether the loan application is Approved or Rejected based on the input provided.

Model Details

• Machine Learning Model: Logistic Regression model trained on historical loan data.
• Data Preprocessing: Features are scaled using a pre-trained scaler model (LoanPredictionPickleModel) before prediction.

Example

Here's a sample screenshot of the Loan Prediction Model application: