Loan AI App

Overview

Loan AI App is a Flutter-based mobile application that leverages machine learning to predict whether a customer qualifies for a loan or not. The app uses a Flask-based API to interact with a pre-trained ML model, ensuring seamless integration between the front-end interface and back-end prediction services.

---

Features

• Intuitive User Interface: Built with Flutter, providing a smooth and responsive user experience.
• AI-Powered Predictions: Utilizes a machine learning model to assess loan eligibility.
• API Integration: Flask is used to host the ML model, which the app communicates with via RESTful API.
• Cross-Platform Compatibility: Runs on both Android and iOS devices.

---

Technologies Used

Frontend:

• Flutter (Dart)

Backend:

• Flask (Python)

Machine Learning:

• Scikit-learn (or other ML libraries used for the model)

API:

• RESTful API endpoints

---

Installation

Prerequisites

• Flutter SDK installed
• Python (3.8 or higher)
• Pip package manager
• Virtual environment for Python (optional but recommended)

Setup Steps

1. Clone the Repository

git clone https://github.com/amoako419/loan-ai-app.git
cd loan-ai-app

2. Backend Setup

1. Navigate to the backend directory:

   cd backend

2. Create and activate a virtual environment (optional):

   python3 -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Flask server:

   python app.py

   The API will be available at http://127.0.0.1:5000/.

3. Frontend Setup

1. Navigate back to the project root:

   cd ../

2. Install Flutter dependencies:

   flutter pub get

3. Run the app:

   flutter run

---

API Endpoints

Base URL

http://127.0.0.1:5000/

Endpoints

1. Predict Loan Eligibility
   • URL: /predict
   • Method: POST
   • Request Body: JSON object containing customer details (e.g., income, credit score, etc.)
   • Response: JSON object with prediction result (e.g., eligible: true/false)

---

How It Works

1. User enters loan application details into the Flutter app.
2. The app sends the data to the Flask API.
3. Flask API processes the data using the ML model.
4. Prediction results are sent back to the app and displayed to the user.

---

Future Enhancements

• Add more advanced ML models for better accuracy.
• Implement secure authentication for API access.
• Enhance UI/UX with additional features.
• Deploy the Flask API to a cloud service for scalability.

---

Contributing

1. Fork the repository.
2. Create a feature branch (git checkout -b feature-name).
3. Commit your changes (git commit -m 'Add feature').
4. Push to the branch (git push origin feature-name).
5. Open a Pull Request.

---

License

This project is licensed under the MIT License. See the LICENSE file for more details.

---