https://github.com/tareqwpy/diabetes-svm-model
just a simple SVM model based on diabetes databases.
https://github.com/tareqwpy/diabetes-svm-model
jupyter jupyter-notebook python
Last synced: 10 days ago
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just a simple SVM model based on diabetes databases.
- Host: GitHub
- URL: https://github.com/tareqwpy/diabetes-svm-model
- Owner: tareqWpy
- License: mit
- Created: 2024-12-24T17:44:59.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-12-24T18:46:45.000Z (5 months ago)
- Last Synced: 2024-12-24T19:22:05.648Z (5 months ago)
- Topics: jupyter, jupyter-notebook, python
- Language: Jupyter Notebook
- Homepage:
- Size: 78.1 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Diabetes SVM Model
A simple SVM model for predicting diabetes based on patient data.
## Project Description
This project implements a Support Vector Machine (SVM) model to predict the presence of diabetes in patients based on various health parameters. The model is trained on a dataset containing patient information such as pregnancies, glucose level, blood pressure, skin thickness, insulin level, BMI, diabetes pedigree function, and age.
## Prerequisites
- Python 3.11
- Jupyter Notebook
- Required Python libraries: scikit-learn, pandas, numpy, matplotlib
## Setup
1. Clone the repository:
```bash
git clone https://github.com/tareqWpy/diabetes-svm-model.git
```
2. Create a virtual environment (optional but recommended):
```bash
python -m venv venv
```
3. Activate the virtual environment:
For Windows:
```bash
venv\Scripts\activate
```
For macOS and Linux:
```bash
source venv/bin/activate
```
4. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
Open the Jupyter Notebook:
```bash
jupyter notebook
```
Navigate to the **diabetes.ipynb** notebook file in the Jupyter interface.
Run the notebook cells sequentially to train the SVM model and evaluate its performance.
You can also use the trained model to make predictions on new patient data by modifying the input values in the notebook.
## Data
The dataset used in this project is sourced from the UCI Machine Learning Repository: Pima Indians Diabetes Database. It contains information about female patients of Pima Indian heritage and their diabetes status.
Results
The SVM model achieves an accuracy of approximately 77% on the test set. The precision and recall scores are also reported in the notebook.
## License
This project is licensed under the MIT License.
## Contact
For any questions or inquiries, feel free to contact the project maintainer:
Email: [email protected]
GitHub: [tareqWpy](https://github.com/tareqWpy)