🔥 Diabetes Prediction Model with ROC Curve 💯

📌 Overview

This project builds a Diabetes Prediction Model using Machine Learning techniques. The model is trained on the diabetes.csv dataset and evaluates its performance using an ROC Curve. The model is then deployed using Flask.

📂 Project Structure

├── app.py                 # Flask web application
├── model.py               # Machine learning model script
├── diabetes.csv           # Dataset used for training
├── diabetes_model.pkl     # Saved ML model
├── Figure_1.png           # Model visualization
├── ROC CURVE.png          # ROC Curve of the model
├── requirements.txt       # Python dependencies
├── .gitignore             # Git ignore file
└── README.md              # Project documentation

🚀 Installation & Usage

1️⃣ Clone the Repository

git clone https://github.com/Siddhi272004-bit/diabetes-ml-project.git
cd diabetes-ml-project

2️⃣ Create a Virtual Environment (Optional but Recommended)

python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Run the Application

python app.py

Access the web app at https://diabetes-ml-project-uslyqxdtmhrgjzl6ph2tzp.streamlit.app/ in your browser.

📊 Model Performance

The ROC Curve is used to evaluate the performance of the model.

🤖 Technologies Used

• Python
• Streamlit (for web deployment)
• Scikit-learn (for machine learning)
• Matplotlib & Seaborn (for data visualization)
• Pandas & NumPy (for data manipulation)

🎯 Features

• Train a machine learning model on diabetes dataset.
• Save the trained model using Pickle.
• Serve predictions through a Flask web app.
• Visualize performance using ROC Curve.

📌 Future Improvements

• Implement JWT Authentication for secure API access.
• Deploy using AWS/GCP/Heroku.
• Improve model performance with Hyperparameter Tuning.

🛠 Contributing

Pull requests are welcome! For major changes, please open an issue first to discuss what you would like to change.

📜 License

This project is licensed under the MIT License.

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