🩺 Diabetes Risk Diagnostic Tool

A professional, machine learning-powered web application built with Python and Streamlit. This tool uses a Random Forest Classifier trained on the Pima Indians Diabetes Database to predict the probability of diabetes based on clinical health metrics.

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🚀 Live Demo

Click here to try the Live App

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📺 Demo Preview

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✨ Features

• Interactive Sidebar: Adjustable sliders and input fields for real-time data entry.
• AI-Powered Predictions: Instant probability scoring with clear "High Risk" or "Low Risk" feedback.
• Data Visualization: Interactive donut charts powered by Plotly showing training data distribution.
• Downloadable Reports: Generate and download a personalized .txt diagnostic report of the results.
• Model Transparency: View accuracy metrics and model evaluation details (Confusion Matrix) directly in-app.

🛠️ Tech Stack

• Language: Python 3.13
• Framework: Streamlit (Web UI)
• Machine Learning: Scikit-learn (Random Forest)
• Data Handling: Pandas & NumPy
• Visualization: Plotly Express
• Deployment: Streamlit Community Cloud

🚀 Installation & Local Setup

1. Clone the repository:

   git clone [https://github.com/ali-faraz-py/DiabetesDetector.git](https://github.com/ali-faraz-py/DiabetesDetector.git)
   cd DiabetesDetector
   
   

2. Install dependencies:

   pip install -r requirements.txt
   
   

3. Run the application:

   streamlit run app.py
   

📂 Project Structure

DiabetesDetector/
├── app.py              # Streamlit Web Application logic
├── model.pkl           # Pre-trained Random Forest Model
├── explore.ipynb       # Data analysis & model training notebook
├── requirements.txt    # Project dependencies
├── .gitattributes      # GitHub language customization
└── assets/             # Images & Demo GIFs

🧠 Model Insights

The model achieves an 80.5% accuracy rate. Below is the Confusion Matrix showing how the model performs on unseen data:

The matrix shows our model is particularly strong at identifying healthy patients, with a focus on reducing false negatives.

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👤 Author

Syed Ali Faraz - GitHub Profile

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