LoanEase - Loan Prediction App

LoanEase is a Flask-based web application designed to predict loan approval status using machine learning. The app uses a trained Gaussian Naive Bayes model to provide instant predictions based on user-provided input. It is deployed on Railway for ease of access.

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Features

• User-friendly loan approval form.
• Predicts loan approval status using machine learning.
• Validates user inputs (e.g., applicant income, loan amount, loan term).
• Displays personalized prediction results.
• Handles errors gracefully with descriptive error messages.
• Deployed on Railway for seamless access.

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Technologies Used

• Python: Core programming language.
• Flask: Web framework for building the app.
• Machine Learning: Gaussian Naive Bayes model for prediction.
• HTML/CSS: Frontend templates and styling.
• Railway: Cloud platform for app deployment.

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Folder Structure

.
├── Dockerfile             # Docker instructions for building the app
├── app.py                 # Flask application entry point
├── core
│   ├── __init__.py        # Core package initializer
│   ├── model_storage
│   │   └── gnb_model.pkl  # Pre-trained Gaussian Naive Bayes model
│   ├── models.py          # Model loading and prediction logic
│   ├── request.py         # Input parsing and validation
│   ├── routes.py          # Flask app routes
│   ├── static
│   │   ├── css
│   │   │   └── style.css  # Styling for the app
│   │   └── images
│   │       ├── favicon.ico  # App icon
│   │       └── loan.png     # Logo for branding
│   └── templates
│       ├── error.html      # Error page template
│       ├── index.html      # Loan application form
│       └── prediction.html # Prediction result display
├── project_images          # Documentation screenshots
│   ├── home.png            # Screenshot of the Home Page
│   ├── prediction.png      # Screenshot of the Prediction Page
│   ├── error.png           # Screenshot of the Error Page
├── requirements.txt        # Python dependencies
├── docker-compose.yml      # Docker Compose configuration
├── README.md               # Project documentation

INSTALLATION GUIDE LOCAL

Step 1: Clone the repository

git clone https://github.com/msjahid/loan-approval-analysis.git
cd loan-approval-analysis

Step 2: Set up a virtual environment

python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate

Step 3: Install dependencies

pip install -r requirements.txt

Step 4: Run the Flask app

python app.py

Step 5: Open the app in your browser

http://127.0.0.1:5000/

INSTALLATION GUIDE DOCKER

Step 1: Prerequisites

• Docker and Docker Compose installed on your machine.
• Your project structured properly (as defined earlier).

Step 2: Build the Docker Image

From the project directory, run the following command to build the Docker image:

docker-compose build

Step 3: Run the Application

Start the application using:

docker-compose up

This will:

• Build the Docker container if it hasn’t been built already.
• Start the Flask application inside the container.

Step 4: Access the Application

Once the container is running, open your browser and navigate to: Check your port number

http://127.0.0.1:4000

DEPLOYMENT ON RAILWAY

Step 1: Sign up or log in to Railway

Visit https://railway.app/ and create an account.

Step 2: Create a new project

• Click "New Project" → "Deploy from GitHub repo".
• Select your repository (e.g., loanease-app).

Step 3: Configure the deployment

• Railway auto-detects the Procfile and runtime.txt.
• Ensure dependencies in requirements.txt are accurate.

Step 4: Deploy the app

• Wait for the build and deployment to complete.

Step 5: Access the app

Railway provides a unique URL (e.g., https://loanease-app.up.railway.app/).

INPUT VALIDATION RULES

1. Name: Cannot be empty or "0".
2. Account Number: Cannot be "0".
3. Applicant Income: Must be greater than 0.
4. Loan Amount: Must be greater than 0.
5. Loan Term: Must be at least 36 months.
6. Credit History: Must be valid (0 or 1).

PAGE PREVIEW SCREENSHOTS

1. Home Page

• Displays the loan application form.

• Includes input fields for Name, Account Number, Income, etc.

  

2. Loan Prediction Page

• Displays the loan prediction result.
• Shows personalized messages based on prediction status.

3. Error Page

• Displays descriptive error messages for invalid inputs.
• Includes a visual illustration for better UX.

EXAMPLE WORKFLOW

INPUT

Name: Hasan Account Number: 123456 Applicant Income: 5000 Loan Amount: 150 Loan Term: 360 Credit History: 1 Property Area: Urban

OUTPUT (Approved)

Congratulations, Hasan! Your loan has been approved.
Account Number: 123456

OUTPUT (Not Approved)

Unfortunately, Hasan your loan has not been approved.
Account Number: 123456

CONTRIBUTING

Steps to contribute:

1. Fork the repository.

2. Create a new branch for your feature: git checkout -b feature-name

3. Commit your changes: git commit -m "Add new feature"

4. Push to your branch: git push origin feature-name

5. Submit a pull request for review.

LICENSE

This project is open-source and available under the MIT License.

CONTACT

• Author: Jahid Hasan
• GitHub: https://github.com/msjahid
• Email: msjahid.ai@gmail.com