Diabetes Diagnosis Machine Learning Model

This project focuses on building and evaluating a machine learning model to diagnose diabetes based on patient data.

Overview

The goal of this project is to develop a reliable machine learning model that can predict whether a patient has diabetes based on specific medical measurements.

Dataset

The dataset used for this project is the Pima Indians Diabetes Database, which contains several medical predictor variables and one target variable indicating the presence of diabetes.

Requirements

• Python 3.6 or higher
• Jupyter Notebook

Installation

1. Clone the repository:

   git clone https://github.com/mohammadreza-mohammadi94/Diabetes_Diagnosis_Machine_Learning_Model.git
   cd Diabetes_Diagnosis_Machine_Learning_Model
   

2. Create and activate a virtual environment (optional but recommended):

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

3. Install the required packages:

   pip install -r requirements.txt
   
   streamlit run app.py
   

Model

The model uses various classification algorithms including Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM). The performance of these models is evaluated using metrics such as accuracy, precision, recall, and F1-score.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your improvements or new features.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Contact

For any questions or feedback, please feel free to reach out via GitHub issues.