🎯 Codeforces Problem Recommender

An intelligent web application that analyzes Codeforces user performance and provides personalized problem recommendations to improve weak areas and enhance competitive programming skills.

✨ Key Features

• 🔍 Smart User Analysis: Fetches and analyzes user data from Codeforces API
• 📊 Performance Insights: Analyzes past submissions and identifies weak topics
• 🧠 ML-Powered Recommendations: Uses machine learning to recommend optimal problems
• ⚡ Fast & Lightweight: Browser-based with local storage, no database required
• 🌐 Modern Web Stack: React frontend + FastAPI backend
• 📱 Responsive Design: Works seamlessly on desktop and mobile devices

🚀 Live Demo

• Frontend: Deployed on Vercel
• Backend: Deployed on Render
• Demo: Live Application

🛠️ Tech Stack

Frontend

• React with Vite
• React Router for navigation
• Axios for API calls
• Recharts for data visualization
• CSS3 for styling

Backend

• FastAPI for REST API
• Scikit-learn for ML models
• Pandas for data processing

📁 Project Structure

ml-project/
├── backend/
│   ├── api.py              # FastAPI endpoints
│   ├── main.py             # Core logic for data processing
│   ├── model.py            # ML model implementation
│   ├── requirements.txt    # Python dependencies
│   └── runtime.txt         # Python version
├── frontend/
│   ├── src/
│   │   ├── components/     # Reusable UI components
│   │   ├── pages/         # Page components
│   │   ├── Services/      # API service layer
│   │   ├── context/       # React context providers
│   │   └── assets/        # Static assets
│   ├── package.json       # Node.js dependencies
│   └── vite.config.js     # Vite configuration
└── README.md

🚦 Quick Start

Prerequisites

• Python 3.8+
• Node.js 16+
• Codeforces account

Backend Setup

1. Navigate to backend directory:

cd backend

2. Create virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Run the server:

python -m uvicorn api:app --reload

The backend will be available at http://localhost:8000

Frontend Setup

1. Navigate to frontend directory:

cd frontend

2. Install dependencies:

npm install

3. Start development server:

npm run dev

The frontend will be available at http://localhost:5173

🔌 API Endpoints

Core Endpoints

Method	Endpoint	Description
POST	/recommend	Get personalized problem recommendations
GET	/api/rating-accuracy	Get accuracy statistics by rating
GET	/syncproblems	Get user's solved problems

Example Request

curl -X POST "http://localhost:8000/recommend" \
  -H "Content-Type: application/json" \
  -d '{"handle": "tourist"}'

🎯 How It Works

1. Data Collection: Fetches user submissions from Codeforces API
2. Feature Extraction: Processes submission data to extract features
3. Model Training: Trains ML model on user performance patterns
4. Problem Filtering: Identifies unsolved problems within skill range
5. Recommendation: Ranks problems based on improvement potential

📊 Features Breakdown

User Analysis

• Submission history analysis
• Rating progression tracking
• Weak topic identification
• Performance metrics calculation

Problem Recommendations

• Personalized difficulty levels
• Topic-based recommendations
• Skill gap targeting
• Progress tracking

Visualizations

• Rating accuracy charts
• Topic performance graphs
• Progress over time
• Problem difficulty distribution

🧪 Development

Running Tests

# Backend tests
cd backend
python -m pytest

# Frontend tests
cd frontend
npm test

Code Quality

# Backend linting
cd backend
flake8 .

# Frontend linting
cd frontend
npm run lint

🚀 Deployment

Frontend (Vercel)

cd frontend
npm run build
vercel --prod

Backend (Render)

# Push to GitHub and connect to Render
# Set environment variables in Render dashboard

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

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