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🚀 Drag-n-Drop Machine Learning Environment An innovative Scratch-like tool for building machine learning pipelines through an interactive drag-and-drop interface! Designed to simplify ML concepts for beginners and empower developers to experiment with machine learning workflows without writing complex code. 🌟

With built-in tutorials and pre-configured tools, this environment bridges the gap between visual programming and real-world machine learning!

🌟 Features Interactive Drag-n-Drop Interface: Build ML workflows with ease using blocks. Integrated Backend: Supports real-time model training and predictions via Flask APIs. Customizable ML Pipelines: Easily modify the code blocks to extend functionality. Built-in ML Models: Start with models like Linear Regression and expand as needed. Beginner-Friendly: Includes step-by-step guides for learning ML concepts hands-on. 🔧 Tech Stack Frontend: JavaScript for drag-and-drop programming. Backend: Python with Flask for managing ML operations. Machine Learning: scikit-learn for training and predictions. Data: Support for dummy data and real-world datasets. Database: SQLite/MySQL for saving user pipelines and configurations. 🚀 Getting Started Follow these steps to set up and run the project:

1️⃣ Prerequisites Python 3.7+ installed Node.js (optional for advanced frontend customizations) 2️⃣ Installation Clone this repository:

bash Copy code git clone https://github.com/InfinityAditya/DND-Drag_N_Drop .git cd drag-n-drop-ml Install backend dependencies:

bash Copy code pip install flask scikit-learn Launch the Flask server:

bash Copy code python app.py Open the index.html file in your browser to start building!

📚 How It Works 🧱 Build Pipelines with Blocks Drag and drop pre-defined blocks to create ML workflows:

Train Model: Train a model with example data. Predict: Use trained models to make predictions. 🌐 Backend The Flask backend handles:

Model training and serialization. API endpoints for predictions. 🔍 Real-World Applications Teach machine learning concepts in schools and colleges. Prototype machine learning workflows without writing code. Experiment with models and datasets interactively. 🌟 Roadmap Planned features for future updates:

Support for Advanced Models: Add support for Decision Trees, SVMs, Neural Networks, etc. Interactive Visualizations: Show real-time graphs of model performance. Dataset Upload: Allow users to upload and process custom datasets. Community Sharing: Save and share ML workflows with others. 🤝 Contributing We ❤️ contributions! If you want to improve this project:

Fork the repository. Create a new branch: git checkout -b feature/YourFeature. Commit your changes: git commit -m 'Add your feature'. Push the changes: git push origin feature/YourFeature. Submit a pull request. 📄 License This project is licensed under the MIT License - see the LICENSE file for details.

📧 Contact Have questions or suggestions? Feel free to reach out:

Email: [email protected] GitHub: InfinityAditya Start building your ML projects like never before! 🚀