🎯 91.8% Accuracy | 📊 Advanced ML Model | 🌐 Interactive Web Interface
Welcome to the Advanced Salary Prediction Web App - a cutting-edge machine learning application that predicts salaries with remarkable 91.8% accuracy! Built using state-of-the-art Gradient Boosting algorithms, this app provides data-driven salary insights for professionals across various industries.
- 🧠 Advanced ML Model: Gradient Boosting with optimized hyperparameters
- 📈 High Accuracy: 91.8% prediction accuracy on test data
- 🎨 Interactive UI: Beautiful Streamlit-powered web interface
- 📊 Rich Visualizations: Comprehensive data analysis and insights
- 🔍 Feature Analysis: Detailed breakdown of salary-influencing factors
- 💡 Smart Predictions: Confidence intervals and key factor explanations
Our advanced machine learning model delivers exceptional results:
🏆 Best Model: Gradient Boosting Regressor
📊 Test Accuracy: 91.8%
📉 Generalization Gap: 0.0451
🎯 RMSE: $11,434
📍 MAE: $9,238
- Training R²: 0.9636
- Test R²: 0.9185
- Low Overfitting: Excellent generalization capability
- Robust Predictions: Consistent across different salary ranges
- Python 3.11 (Download here)
- Git (optional, for cloning)
git clone https://github.com/yourusername/salary-predictor.git
cd salary-predictor# Create virtual environment
python -m venv venv
# Activate virtual environment
# Windows:
venv\Scripts\activate
# macOS/Linux:
source venv/bin/activatepip install -r requirements.txtstreamlit run app.py🎉 That's it! The app will open in your browser at http://localhost:8501
salary-predictor/
├── 📊 data/
│ └── Salary_Data.csv # Training dataset
├── 🧠 models/
│ ├── salary_prediction_model.joblib # Trained ML model
│ ├── model_metadata.json # Model configuration
│ └── visualization_data.json # Visualization data
├── 📈 visualizations/ # Generated plots and charts
├── 🌐 app.py # Main Streamlit application
├── 🔧 salary_model_api.py # Model API and utilities
├── 📋 requirements.txt # Python dependencies
├── 📖 README.md # This file
└── 🎯 .gitignore # Git ignore file
- Real-time Predictions: Get instant salary estimates
- Confidence Intervals: Understand prediction reliability
- Range Estimates: Min-max salary ranges for better planning
- Feature Importance: See what factors matter most
- Correlation Analysis: Understand relationships between variables
- Interactive Visualizations: Explore data through beautiful charts
- Years of Experience (30.8% impact)
- Age (22.4% impact)
- Education Level (10.4% impact)
- Job Title
- Gender
- Industry Specifics
- Predicted Salary: $152,670
- Confidence Range: $144,506 - $160,833
- Key Factors: Master's degree, 8+ years experience
- Predicted Salary: $82,826
- Confidence Range: $74,663 - $90,990
- Key Factors: High-demand role, growth potential
- Predicted Salary: $217,888
- Confidence Range: $209,725 - $226,052
- Key Factors: Leadership role, extensive experience
- Data Preprocessing: Feature engineering and cleaning
- Model Selection: Comprehensive algorithm comparison
- Hyperparameter Tuning: Grid search optimization
- Validation: Cross-validation and performance testing
- Deployment: Streamlit web interface
GradientBoostingRegressor(
learning_rate=0.1,
max_depth=5,
min_samples_split=20,
n_estimators=150,
subsample=0.8
)- Average Salary: $135,375
- Salary Range: $67,247 - $234,803
- Sample Size: Comprehensive professional dataset
- Features: Age, Experience, Education, Job Title, Gender
pandas>=1.5.3
numpy>=1.23.5
scikit-learn>=1.2.2
scipy>=1.10.1
matplotlib>=3.6.3
seaborn>=0.12.2
plotly>=5.14.1
joblib>=1.2.0
streamlit>=1.22.0
Beautiful, intuitive web interface for salary predictions
Interactive charts showing salary distributions and correlations
Detailed prediction breakdown with confidence intervals
streamlit run app.py- Push to GitHub
- Connect to Streamlit Cloud
- Deploy with one click
FROM python:3.11-slim
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
EXPOSE 8501
CMD ["streamlit", "run", "app.py"]We welcome contributions! Here's how you can help:
- 🍴 Fork the repository
- 🌿 Create a feature branch (
git checkout -b feature/AmazingFeature) - 💾 Commit your changes (
git commit -m 'Add some AmazingFeature') - 📤 Push to the branch (
git push origin feature/AmazingFeature) - 🔄 Open a Pull Request
- 🎨 UI/UX improvements
- 📊 Additional visualization features
- 🧠 Model performance enhancements
- 📱 Mobile responsiveness
- 🌐 Multi-language support
- 🔄 Real-time data updates
- 🌍 Geographic salary variations
- 📱 Mobile app development
- 🤖 Advanced AI features
- 📊 Industry-specific models
- 🔐 User authentication system
Issue: ModuleNotFoundError
# Solution: Ensure virtual environment is activated
pip install -r requirements.txtIssue: Port already in use
# Solution: Use different port
streamlit run app.py --server.port 8502Issue: Model file not found
# Solution: Ensure models directory exists
mkdir models
# Re-run training script if needed- 🐛 Bug Reports: Create an Issue
- 💡 Feature Requests: Discussions
- 📧 Email: [email protected]
- 🐦 Twitter: @yourusername
This project is licensed under the MIT License - see the LICENSE file for details.
- 📚 Scikit-learn team for the amazing ML library
- 🎨 Streamlit for the beautiful web framework
- 📊 Plotly for interactive visualizations
- 🤝 Open Source Community for inspiration and support