GlucoGuard 1.2.0

"GlucoGuard" is a data-driven Machine Learning model specifically designed for Binary Classification to predict the risk of developing diabetes in its early stages. It assists healthcare professionals in the diagnosis and management of diabetes. The model encompasses the following key features:

1. Machine Learning Models:
   "GlucoGuard" utilizes Logistic Regression, Linear SVM, and Polynomial SVM classifiers for diabetes risk prediction.

2. Rich Dataset Utilization:
   The model uses the Early Stage Diabetes Risk Prediction Dataset (2020) from the UCI Machine Learning Repository.

3. Enhanced Data Processing:
   This release includes data preprocessing techniques like normalization, outlier detection, and feature encoding.

4. Intuitive Notebook Experience:
   The included Jupyter notebook covers detailed project instructions and examples, including data preparation, model training, and evaluation, with clear explanations and visualizations.

5. Ready-to-Use Models:
   Trained models are available in the ./models/ directory for use.

Getting Started

"GlucoGuard" is designed for ease of use. To get started:

1. Clone the repository to your local machine.
2. Install the necessary dependencies by running pip install -r requirements.txt.
3. Explore the Jupyter notebook GlucoGuard.ipynb for a detailed understanding of the model's capabilities and applications.

Release version: v1.2.0
© 2023. Wentao Jiang