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The Decision Tree Model Trainer Backend is a server-side application built with Node.js and Python. It is designed to handle the training of decision tree models based on user input.

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sideffect263/DT_Model_Trainer_BE

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Decision Tree Model Trainer Backend 🧠

This repository contains the backend server for the Decision Tree App, responsible for training decision tree models. The backend is built using Node.js and Python.

Features ✨

  • Train decision tree models based on user input.
  • Handle model training requests and responses.
  • Integrate seamlessly with the frontend application.

Installation 🛠️

To run this project locally, follow these steps:

  1. Clone the repository:

    git clone https://github.com/sideffect263/DT_Model_Trainer_BE.git
    cd DT_Model_Trainer_BE
  2. Install Node.js dependencies:

    npm install
  3. Install Python dependencies:

    pip install -r requirements.txt
  4. Start the server:

    node server.js

    The server will run on http://localhost:5000.

Usage 🚀

The backend server provides API endpoints for model training. To integrate with the frontend application, ensure the server is running and accessible.

Available Scripts 📜

In the project directory, you can run:

node server.js

Starts the backend server.

Contributing 🤝

Contributions are welcome! Please follow these steps to contribute:

  1. Fork the repository.
  2. Create a new branch (git checkout -b feature-branch).
  3. Make your changes.
  4. Commit your changes (git commit -m 'Add new feature').
  5. Push to the branch (git push origin feature-branch).
  6. Open a pull request.

Feel free to check the issues page for ideas on what to work on, or to report bugs and request features.

License 📄

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

Contact 📧

If you have any questions, feel free to reach out to the repository owner.


This backend server works in conjunction with the Decision Tree App frontend.

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The Decision Tree Model Trainer Backend is a server-side application built with Node.js and Python. It is designed to handle the training of decision tree models based on user input.

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