Inspiration

Loan Eyes

Overview

This program is designed to determine the eligibility of a user to obtain a loan using machine learning. It utilizes Python with the Scikit-Learn library for the machine learning model, and Node.js for the backend server, along with JavaScript for the frontend user interface.

Requirements

To run this program, you will need the following software and libraries installed on your system:

• Python (3.6 or higher)
• Node.js
• Scikit-Learn
• Express.js
• React.js

You can install the required Python libraries using pip:

pip install scikit-learn

Getting Started

1. Clone or download this repository to your local machine.

2. Navigate to the project directory using the command line.

3. Install the Node.js dependencies for the backend server by running the following command in the backend directory:

   cd backend
   npm install
   

4. Install the React.js dependencies for the frontend interface by running the following command in the frontend directory:

   cd frontend
   npm install
   

5. Go back to the root directory of the project:

   cd ..
   

6. Prepare your dataset:

   • The machine learning model requires a dataset to train on. Make sure you have a CSV file containing historical data of loan applicants, including their attributes and loan approval status.

7. Train the machine learning model:

   • Use Python and Scikit-Learn to train your model. You can create a Python script in the root directory or a separate directory for model training. Make sure to save the trained model to a file.

8. Set up the configuration:

   • In the backend directory, create a configuration file (e.g., config.js) to specify the path to your trained model and other settings.

9. Start the backend server:

   • In the backend directory, run the following command: bash node server.js

10. Start the frontend interface:

    • In the frontend directory, run the following command: bash npm start

11. Access the application in your web browser at http://localhost:3000.

Usage

• Users can enter their details in the provided form on the frontend.
• Click on the "Check Eligibility" button to send the data to the backend for prediction.
• The backend server will use the trained machine learning model to predict the user's eligibility for a loan.
• The result will be displayed on the frontend, indicating whether the user is eligible for a loan or not.

Customization

You can customize this program by:

• Modifying the frontend interface to enhance user experience.
• Improving the machine learning model for better prediction accuracy.
• Adding additional features like user authentication, database storage, or real-time updates.

License

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

Acknowledgments

• Scikit-Learn for providing powerful machine learning tools.
• Express.js and React.js for enabling the development of the backend and frontend components.
• The open-source community for their valuable contributions.

Disclaimer

This program is for educational and demonstration purposes only. Make sure to comply with all legal and ethical requirements when handling loan eligibility decisions.

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for LoanEyes

Built With

• javascript
• node.js
• python