Diabetes-Prediction-using-Support-Vector-Machines

This project aims to predict the onset of diabetes using Support Vector Machines (SVM) with an accuracy of 76%. The model was developed using Python, scikit-learn, Jupyter, Pandas, NumPy, and Gradio for building a user-friendly prediction interface.

Features

• Feature selection using Recursive Feature Elimination (RFE) and Logistic Regression
• Data standardization using StandardScaler from scikit-learn
• Interactive web app for prediction using Gradio

Installation

1. Clone the repository: git clone https://github.com/your-username/Diabetes-Prediction-using-Support-Vector-Machines.git

2. Install the required dependencies: pip install -r requirements.txt

3. Run the Jupyter notebook:

4. Open the Diabetes_Prediction_SVM.ipynb file and execute the cells to train the model and launch the Gradio app.

Contributing

Feel free to open issues or submit pull requests if you'd like to improve the project or have any suggestions.

License

MIT License