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The app provides an easy-to-use interface for selecting dependent and independent variables, scaling data, applying polynomial regression, and evaluating model performance.\n\nCheck the project: https://linear-regression-training-app.streamlit.app/\n## Features\n\n- **CSV File Upload**: Upload your dataset directly through the app.\n- **Variable Selection**: Choose the dependent and independent variables for model training.\n- **Data Scaling**: Automatically scales the independent variables.\n- **Polynomial Regression**: Option to apply polynomial regression with a selectable degree.\n- **Model Training**: Trains a linear regression model and provides predictions.\n- **Performance Metrics**: Displays R², MAE, RMSE, and MSE for evaluating the model.\n- **Variable Impact**: Displays the impact of each independent variable on the model.\n\n## Project Structure\n\n- `app.py`: Main script for running the Streamlit application.\n\n## How to Run\n\n1. **Clone the Repository**:\n\n   ```bash\n   git clone https://github.com/GhufranBarcha/linear-regression-app.git\n   cd linear-regression-app\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fghufranbarcha%2Flinear-regression-training-app","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fghufranbarcha%2Flinear-regression-training-app","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fghufranbarcha%2Flinear-regression-training-app/lists"}