CultureConnect Machine Learning API 🤖

Welcome to the CultureConnect Machine Learning API! 🚀
This repository contains the ML model that provides recommendations based on user preferences and cultural data. It powers AI-driven features in the CultureConnect platform.

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📌 Features

✅ Recommendation System – Suggests destinations based on user preferences.
✅ Dataset Integration – Uses structured data for training and inference.
✅ Flask API – Serves predictions through an HTTP interface.
✅ Virtual Environment – Isolated dependencies using venv.

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🚀 Technologies Used

• Python 🐍 – Primary language for ML development.
• Flask 🌐 – Lightweight web framework to serve ML models.
• Pandas 📊 – Data manipulation and analysis.
• NumPy 🔢 – Efficient numerical computing.
• scikit-learn 🤖 – Machine learning algorithms.

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📂 Folder Structure

ml/
│-- venv/                 # Virtual environment (dependencies isolated)
│   ├── Include/
│   ├── Lib/
│   ├── Scripts/
│   ├── pyvenv.cfg
│
│-- .gitignore             # Ignore unnecessary files (venv, cache, etc.)
│-- app.py                 # Flask application to serve the ML model
│-- dataset.csv            # Dataset used for training and recommendations
│-- LICENSE                # Project license
│-- README.md              # Documentation (this file)
│-- recommendations.py     # Core recommendation engine logic
│-- requirements.txt       # Required dependencies for the ML model

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📥 How to Set Up and Run the Project

1️⃣ Clone the Repository

git clone https://github.com/CultureConnect-team/CultureConnect-ML.git

2️⃣ Create a Virtual Environment

Navigate to the project directory and create a virtual environment:

cd CultureConnect-ML
python -m venv venv

3️⃣ Activate the Virtual Environment

• Windows

  venv\Scripts\activate

• macOS/Linux

  source venv/bin/activate

4️⃣ Install Dependencies

pip install -r requirements.txt

5️⃣ Run the Flask API

python app.py

The API will be available at http://127.0.0.1:5000 or at http://127.0.0.1:5001.

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🛠 API Endpoints

🔹 Recommendations

METHOD	ENDPOINT	DESCRIPTION
POST	/recommend	Get personalized destination recommendations

📌 Example Request (JSON)

{
  "user_preferences": ["nature", "culture", "historical"]
}

📌 Example Response (JSON)

{
  "recommendations": [
    {"destination": "Bali", "score": 0.92},
    {"destination": "Yogyakarta", "score": 0.87}
  ]
}

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🏗 How It Works

1. Dataset (dataset.csv) – Contains information about various tourist destinations, categorized by features such as culture, adventure, history, etc.
2. ML Model (recommendations.py) – Uses scikit-learn to process user input and generate recommendations.
3. Flask API (app.py) – Serves the ML model through an HTTP API.

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🤝 Contributing

We welcome contributions! Follow these steps:

1. Fork the repository.
2. Create a new branch (feature/your-feature-name).
3. Make changes and commit with a clear message.
4. Push your branch and create a Pull Request (PR).

Please follow Python best practices and formatting (PEP 8).

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🛠 Troubleshooting

If you encounter issues:

• Ensure Python is installed (python --version).
• Activate the virtual environment before running the API.
• If dependencies fail, try:

  pip install --upgrade pip
  pip install -r requirements.txt

• Restart the API with python app.py.

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📌 License

This project is licensed under the MIT License – see the LICENSE file for details.

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🚀 Happy coding! 🎯