DataViz Studio 📊

An interactive web-based tool to visualize how different classification and regression algorithms behave when their parameters change. Explore patterns, visualize data, and discover insights through beautiful, interactive visualizations.

Features

• 6 algorithms: Linear Regression, Logistic Regression, KNN, Decision Tree, Random Forest, SVM
• Interactive hyperparameter tuning with sliders and dropdowns
• Real-time visualization of decision boundaries, accuracy curves, and feature importance
• Multiple toy datasets (moons, circles, iris)
• Clean, responsive UI with dark mode support

Tech Stack

• Frontend: React.js + Tailwind CSS + Framer Motion + Plotly.js
• Backend: FastAPI + scikit-learn + NumPy
• Communication: REST API

Quick Start

Prerequisites

• Node.js (v16+)
• Python (3.8+)
• npm or yarn

Backend Setup

cd backend
pip install -r requirements.txt
python app.py

Backend runs on http://localhost:8000

Frontend Setup

cd frontend
npm install
npm run dev

Frontend runs on http://localhost:5173

API Endpoints

• GET /datasets - List available datasets
• POST /train - Train model and get visualization data
• GET /health - Health check

Usage

1. Select a dataset from the dropdown
2. Choose an algorithm
3. Adjust hyperparameters using sliders
4. Click "Run Model" to see visualizations
5. Compare algorithms side-by-side (optional)

Project Structure

ml-visualizer/
├── frontend/          # React application
│   ├── src/
│   │   ├── components/
│   │   ├── pages/
│   │   └── utils/
│   └── package.json
├── backend/           # FastAPI application
│   ├── app.py
│   ├── models/
│   ├── utils/
│   └── requirements.txt
└── README.md

License

MIT