MLFusionLab: ML for Everyone

Welcome to MLFusionLab! This repository houses a Django project designed to streamline your machine learning workflow, providing a user-friendly interface for building, training, and managing models.

Why MLFusionLab?

• Structured Environment: Organize your machine learning endeavors within a robust Django project, leveraging Django's structure for models, views, and templates.
• Customizable Interface: Interact with your models and data through a dynamic web interface, tailoring the experience to your specific needs.
• Simplified Experiment Tracking: Manage datasets, track experiment parameters, and compare model performance with ease.

Who is this for?

• Machine learning practitioners seeking a more organized and efficient way to develop and deploy models using PyTorch and scikit-learn.
• Data scientists and engineers who prefer a visual interface for interacting with their machine learning projects.
• Teams collaborating on machine learning tasks, benefitting from centralized model management. (todo)

Getting Started:

1. Clone the repository:

   git clone https://github.com/harikris001/MLFusionLab.git

2. Navigate to the project directory:

   cd MLFusionLab

3. Create a virtual environment (recommended):

   python -m venv .venv
   source .venv/bin/activate

4. Install project dependencies:

   pip install -r requirements.txt

5. Apply database migrations:

   python manage.py migrate

6. Start the development server:

   python manage.py runserver

   Access the application in your browser at http://127.0.0.1:8000/.

Key Features (Potential):

• User authentication and authorization for secure access and project management.
• Data upload and management capabilities through a user-friendly interface.
• Model training and evaluation workflows with integrated visualization tools specifically designed for PyTorch and scikit-learn.
• Support for data analysis, cleaning, and visualization using popular Python libraries like Pandas, NumPy, and Matplotlib.

Libraries Used:

• Django (Core framework)
• Django REST framework (For building APIs - optional if needed)
• PyTorch (Deep Learning library)
• Scikit-learn (Machine learning library)
• Pandas (Data analysis and manipulation)
• NumPy (Numerical computing)
• Matplotlib (Data visualization)
• Other libraries as needed for your specific machine learning tasks.

Contributing:

We welcome contributions! Share your ideas, report issues, or submit pull requests to help us improve MLFusionLab.