MLXplore

MLXplore is a full-stack Machine Learning experimentation platform designed to simplify the process of building, testing, and exploring ML models. Main Purpose: MLXplore allows users to explore and experiment with various machine learning tasks (classification, regression, clustering), datasets, and models through a user-friendly interface. It facilitates data preview, model training, and hyperparameter tuning.

🚀 Key Features

• End-to-End ML Experimentation: Train models with custom datasets , Run experiments and visualize results , Compare different models and hyperparameters.
• Interactive ML Tasks: Users can select from classification, regression, and clustering tasks.
• Dataset Exploration: A variety of pre-defined datasets are available for each task.
• Model Selection: A range of popular ML algorithms are supported for each task.
• Parameter Configuration: Intuitive controls (sliders, menus, text fields) allow for dynamic adjustment of model hyperparameters.
• Data Preview: Visualize sample data to understand its structure.
• Model Training & Visualization: Train models, view performance metrics, and visualize results.
• Hyperparameter Tuning: Optimize model parameters using Grid Search or Random Search.
• Theming: Supports both light and dark modes for a personalized experience.
• Modern UI/UX: Features a visually appealing and user-friendly interface with smooth animations, custom scrollbars, and enhanced interactive elements.

Technology Used

• FRONTEND

  • React: The core JavaScript library for building the user interface.
  • Material-UI (MUI): A popular React component library for building the UI elements.
  • CSS: For styling, including custom fonts (Inter), smooth animations, and responsive design.

• BACKEND

  • FastAPI: A modern, fast (high-performance) web framework for building the API.
  • Python: The primary language for the backend, leveraging ML libraries

• Machine Learning Libraries (Backend): A range of standard ML libraries are implicitly used for tasks like data splitting, model fitting, evaluation, and hyperparameter tuning (such as scikit-learn for model selection and cross-validation, pandas for data manipulation, and numpy for numerical operations).

Try web app here

🌐 https://ml-xplore-omega.vercel.app/

⚙️ Installation

1. Clone the Repository:

   git clone https://github.com/JeetGupta2506/MLXplore.git
   cd MLXplore

2. Setup Backend:

   cd backend
   python -m venv venv
   venv\Scripts\activate     
   pip install -r requirements.txt
   python main.py

3. Setup Frontend:

   cd ../frontend
   npm install 
   npm start

🎯 Why MLXplore?

• Makes machine learning experimentation more accessible.
• No need to write boilerplate ML code — just configure and run.
• Useful for: Students learning ML concepts.

Screenshots

Developers Information

Created by Jeet Gupta . Connect with me on

• GitHub
• LinkedIn
• Email