{"id":19774242,"url":"https://github.com/ganesh2409/salary_prediction","last_synced_at":"2026-04-14T10:31:37.078Z","repository":{"id":212698792,"uuid":"732081502","full_name":"Ganesh2409/Salary_Prediction","owner":"Ganesh2409","description":"Forging a cutting-edge Salary Prediction Software for Software Engineers. 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The application uses a Random Forest model trained on data from the Stack Overflow Developer Survey 2023.\n\n## Project Structure\n\n- `app.py`: The main entry point of the application, where users can choose to explore data or predict salaries.\n- `predict_page.py`: Contains the functionality to gather user input and predict salaries.\n- `explore_page.py`: Provides an interactive exploration of the salary data.\n- `Salary_prediction.ipynb`: A Jupyter Notebook used for data analysis, feature engineering, and model training.\n- `Salary_Prediction/models/`: Directory where the trained model and encoders are saved.\n\n## Features\n\n- **Salary Prediction**: Predicts the salary of a software developer based on their country, education level, and years of experience.\n- **Data Exploration**: Visualizes salary data by country and experience level, using charts like bar charts, line charts, and pie charts.\n\n## Getting Started\n\n### Prerequisites\n\n- Python 3.8 or higher\n- Required Python libraries listed in `requirements.txt`\n\n### Installation\n\n1. Clone the repository:\n\n    ```bash\n    git clone https://github.com/Ganesh2409/Salary_Prediction.git\n    cd Salary_Prediction\n    ```\n\n2. Install the required packages:\n\n    ```bash\n    pip install -r requirements.txt\n    ```\n\n3. Run the application:\n\n    ```bash\n    streamlit run app.py\n    ```\n\n## Usage\n\n1. **Predict Salary**: Navigate to the \"Predict\" page, select your country, education level, and years of experience, then click \"Calculate Salary\". The application will display the predicted salary.\n  \n2. **Explore Data**: On the \"Explore\" page, you can visualize salary distributions by country and experience.\n\n## Model Information\n\n- **Data**: The model is trained on data from the Stack Overflow Developer Survey 2023.\n- **Algorithms**: The application uses a Random Forest Regressor to predict salaries.\n- **Preprocessing**:\n  - Label Encoding: For the country column.\n  - Ordinal Encoding: For the education level column.\n## Model Improvement:\n\n- **Try Different Algorithms**: Experiment with other machine learning models like Gradient Boosting, XGBoost, or Neural Networks to see if they offer better performance than Random Forest.\n- **Hyperparameter Tuning**: Perform more advanced hyperparameter tuning using techniques like Bayesian optimization or Genetic Algorithms to further improve the model's accuracy.\n## Contributing\n\nContributions are welcome! Please fork this repository and submit a pull request with your changes.\n\n## Contact\nFor any questions or feedback, please contact:\n- **Name** - [Ganesh Chowdhary P]()\n- **Email** - [pinnamaneniganesh24@gmail.com ](mailto:your.pinnamaneniganesh24@gmail.com)\n\n```bash\nMade with ❤️ ( ͡• ͜ʖ ͡• ) Follow for more  ... :)\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fganesh2409%2Fsalary_prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fganesh2409%2Fsalary_prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fganesh2409%2Fsalary_prediction/lists"}