{"id":26879547,"url":"https://github.com/athrvvv/diabetes_prediction_system","last_synced_at":"2025-07-30T01:37:42.422Z","repository":{"id":283629592,"uuid":"952394680","full_name":"Athrvvv/Diabetes_Prediction_System","owner":"Athrvvv","description":"The Diabetes Prediction System uses a Naive Bayes classification model to predict diabetes based on input data. It includes a Jupyter Notebook for model training, a Flask application (app.py) for deployment, and a pickle file (model.pkl) to load the trained model. 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The system processes medical data, applies predictive analysis, and provides results via a **Flask web application**.\n\n## Why Naive Bayes?  \n**Naive Bayes** is ideal for medical predictions due to its simplicity and efficiency. It calculates the **probability of diabetes** by analyzing input features independently, ensuring quick and accurate predictions.\n\n## Features  \n- **Machine Learning Model**: Trained using Naive Bayes on medical data.  \n- **Web Interface**: Flask-based UI for entering data and viewing results.  \n- **Pickle Model Deployment**: Pre-trained model stored and loaded via `model.pkl`.  \n- **Data Processing**: Pandas and NumPy for handling and transforming data.  \n\n## Technologies Used  \n- **Python**  \n- **Flask**  \n- **Scikit-learn**  \n- **Pandas, NumPy**  \n- **Jupyter Notebook**  \n\n## Installation \u0026 Setup  \n1. Clone the repository:  \n   ```bash\n   git clone https://github.com/Athrvvv/Diabetes_Prediction_System.git\n   cd Diabetes_Prediction_System\n   ```\n2. Install dependencies:  \n   ```bash\n   pip install -r requirements.txt\n   ```\n3. Run the Flask app:  \n   ```bash\n   python app.py\n   ```\n\n## Usage  \n- Open `diabetes_model.ipynb` to analyze data and retrain the model if needed.  \n- Run `app.py` to launch the web application and interact with the prediction system.  \n- Input relevant medical data to receive a **diabetes prediction**.  \n\n## Dataset  \nThe dataset used contains **medical attributes** such as glucose levels, BMI, and insulin, which are used by the model to predict diabetes.  \n\n## Future Enhancements  \n- Add more advanced models for comparison.  \n- Deploy on cloud platforms for wider accessibility.  \n- Enhance UI for better user experience.  \n\n## Contact  \nFor queries or suggestions, open an issue in the repository.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fathrvvv%2Fdiabetes_prediction_system","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fathrvvv%2Fdiabetes_prediction_system","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fathrvvv%2Fdiabetes_prediction_system/lists"}