README.md

Loan Eligibility Backend

🏦 Project Overview

This backend application determines loan eligibility using the Fuzzy Tsukamoto method. It provides intelligent loan assessment based on multiple financial parameters.

Fuzzy Preparation Here

🚀 Project Demo

• Backend Server: kelayakan-pinjaman-backend.vercel.app
• Frontend Application: kelayakanpinjaman.vercel.app/
• Frontend Repository: github/gdapriana/kelayakan-pinjaman-frontend

📂 Project Structure

kelayakan-pinjaman-backend/
│
├── dataset/                 # Contains project datasets
├── resources/
│   ├── fuzzy.py             # Fuzzy Tsukamoto logic implementation
│   ├── member.py            # Project team information
│   └── preprocessing.py     # Data preprocessing utilities
└── app.py                   # Main application entry point

🔍 API Endpoints

1. Predict Loan Eligibility

• URL: /predict
• Method: POST
• Request Body:

  {
    "pendapatan": float,
    "usia": int,
    "tanggungan": int,
    "pengeluaran": float,
    "aset": float
  }

2. Dataset Information

• URL: /dataset
• Method: GET
• Description: Provides details about the dataset used for loan eligibility prediction

3. Team Members

• URL: /member
• Method: GET
• Description: Returns information about the project team

🧠 Methodology

The application uses the Fuzzy Tsukamoto method to assess loan eligibility. This approach allows for intelligent and nuanced decision-making by:

• Converting crisp input values to fuzzy input
• Applying fuzzy inference rules
• Defuzzifying results to determine loan eligibility

🛠️ Technologies Used

• Python
• Flask
• Fuzzy Logic
• Vercel (Deployment)

👥 Team

For detailed team information, please check the /member endpoint or contact the repository maintainers.

🤝 Contributing

Interested in contributing? Please read our contributing guidelines and feel free to submit pull requests.