🩸 Diabetes Prediction App: Clinical ML Dashboard

🚀 Live Application

Experience the interactive clinical dashboard here: Diabetes Prediction Web App

📖 Overview

This repository contains a data-driven web application that integrates a pre-trained machine learning model into an interactive clinical dashboard. Built with Streamlit and PyCaret, the application utilizes a tuned Random Forest classifier to predict the likelihood of diabetes based on standard diagnostic measurements.

This project demonstrates the deployment phase of the CRISP-DM lifecycle, showcasing how to transition a trained model (.pkl) into an accessible, user-friendly tool for real-time inference.

✨ Key Features

• Interactive Clinical Inputs: A clean user interface allowing the input of 8 critical health metrics, including Glucose, BMI, Insulin levels, and Blood Pressure.
• Integrated PyCaret Pipeline: Utilizes PyCaret's predict_model functionality, ensuring that data transformations and model inference are handled seamlessly under the hood.
• Automated Data Structuring: The app automatically captures user inputs and converts them into a structured Pandas DataFrame format required by the machine learning model.
• Real-Time Inference: Instantaneous prediction generation with robust error handling to prevent app crashes if the model file is missing or inputs are invalid.

🛠️ Technology Stack

• Language: Python 3.10+
• Web Framework: Streamlit
• Machine Learning: PyCaret (Classification)
• Data Manipulation: Pandas

💻 How to Run Locally

1. Clone the repository:

   git clone [https://github.com/yourusername/diabetes-prediction-app.git](https://github.com/yourusername/diabetes-prediction-app.git)
   cd diabetes-prediction-app
   

2. Ensure Model is Present: Verify that the pre-trained model file tuned_rf_diabetes.pkl is located in the root directory of the project.

3. Install the required dependencies:

   pip install streamlit pandas pycaret

(Note: PyCaret is a heavy library. It is recommended to run this in an isolated virtual environment).

4. Run the Streamlit App:

   streamlit run app.py
   

5. Access the Dashboard: Open your browser and navigate to the local URL provided in your terminal (usually http://localhost:8501).

⚠️ Disclaimer For Educational Purposes Only. This application and its underlying machine learning model are designed for portfolio demonstration and educational purposes. It is not intended to be a substitute for professional medical advice, diagnosis, or treatment.

✍️ Author

Femi James Data & Business Analyst | Integrated AI Specialist

(Don't forget to update the yourusername placeholder in the clone link before you commit it!)