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The dataset used for this project is sourced from Kaggle, originally provided by the National Institute of Diabetes and Digestive and Kidney Diseases.\n\n## Aim\nTo develop a predictive health analytics tool for assessing diabetic risk and providing personalized reports.\n\n## Mission\nTo leverage machine learning for early detection of diabetes, enabling timely medical intervention and improving health outcomes.\n\n## Learning Objective\n- Understand the end-to-end process of developing a machine learning model.\n- Gain experience in deploying applications on cloud platforms like Heroku.\n- Learn to build interactive web applications using Streamlit.\n\n## Technical Aspect\n- Training a machine learning model using scikit-learn.\n- Building and hosting a Strealit web app on Heroku.\n- User input for features such as pregnancies, insulin level, age, BMI, etc., followed by a prediction display.\n\n## Technologies Used\n- Python\n- scikit-learn\n- strealit\n- seaborn\n- Heroku\n\n## Installation\n1. Clone this repository and unzip it.\n2. Navigate into the project directory.\n    ```bash\n    cd filename\n    ```\n3. Create a virtual environment with Python 3 and activate it.\n    ```bash\n    python3 -m venv venv\n    source venv/bin/activate  # On Windows use `venv\\Scripts\\activate`\n    ```\n4. Install the required packages.\n    ```bash\n    pip install -r requirements.txt\n    ```\n5.  Run\nExecute the following command to start the application:\n```bash\npython app.py\n```\n\n## Contributors\n- [Bharath](https://github.com/Bharath-tars)\n- Pooja Chinta\n- Yenuganti Sai Kumar\n\n## Credits\nThis repository was created with ❤️ by Sudarsanam Bharath.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbharath-tars%2Fstreamlit_diabsynth","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbharath-tars%2Fstreamlit_diabsynth","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbharath-tars%2Fstreamlit_diabsynth/lists"}