A full-stack web application that predicts salary based on various features using machine learning regression models.

• Salary Prediction: Predict salary based on experience, education, location, and other factors
• Multiple ML Models: Linear Regression, Random Forest, and XGBoost
• Interactive Dashboard: Beautiful charts and visualizations
• Data Management: Upload, view, and manage training data
• Model Comparison: Compare different regression models' performance
• Responsive Design: Works on desktop and mobile devices

• Python 3.8+
• Flask: Web framework
• Scikit-learn: Machine learning library
• Pandas & NumPy: Data manipulation
• SQLite: Database
• Flask-CORS: Cross-origin resource sharing

• React 18: Frontend framework
• TypeScript: Type safety
• Tailwind CSS: Styling
• Chart.js: Data visualization
• Axios: HTTP client
• React Router: Navigation

• Linear Regression: Baseline model
• Random Forest: Ensemble method
• XGBoost: Gradient boosting
• Feature Engineering: Data preprocessing
• Model Evaluation: Metrics and validation

WageWise/
├── backend/                 # Flask API
│   ├── app.py             # Main Flask application
│   ├── models/            # ML models and training
│   ├── data/              # Data processing
│   ├── api/               # API routes
│   └── requirements.txt   # Python dependencies
├── frontend/              # React application
│   ├── src/               # Source code
│   ├── public/            # Public assets
│   └── package.json       # Node dependencies
├── data/                  # Sample datasets
├── models/                # Trained ML models
└── README.md             # This file

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

cd frontend
npm install
npm start

• POST /api/predict - Predict salary
• GET /api/models - Get available models
• POST /api/train - Train models
• GET /api/metrics - Get model performance metrics
• POST /api/upload - Upload training data

• Input: Experience, education, location, company size, etc.
• Output: Predicted salary with confidence interval
• Support for multiple ML models

• Upload CSV files with salary data
• Data validation and preprocessing
• Feature engineering capabilities

• Automatic model training on new data
• Hyperparameter tuning
• Cross-validation

• Model performance comparison
• Feature importance analysis
• Data distribution charts
• Prediction accuracy metrics

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

MIT License - see LICENSE file for details