🩺 Diabetes Prediction using Machine Learning

📌 Overview

This project develops a machine learning model to predict the likelihood of diabetes based on clinical features, including Pregnancies, Glucose, Blood Pressure, Skin Thickness, Age, cholesterol levels, and other relevant factors. The goal is to assist in early detection and support medical decision-making through data-driven insights.

🚀 Features

• Exploratory Data Analysis
• Data Preprocessing: Feature scaling, Handling inbalance class distribution
• Model Selection: Evaluation of multiple ML classifiers (e.g., Random Forest, XGBoost)
• Performance Metrics: Accuracy, precision, recall, F1-score, ROC-AUC curve
• Deployment: Interactive web interface using Streamlit

🛠️ Installation

Clone the repository

git clone https://github.com/auspicie/Diabetes_Prediction-ML.git
cd Diabetes_Prediction-ML

**Install dependencies**
pip install -r requirements.txt

💻 Usage

Run the Streamlit app: streamlit run diabetes_disease_app.py

Interact with the app: Input values and view predictions in your browser.

✨ Example Prediction

Input: Male, Age: 58, Cholesterol: 230, ...
'Pregnancies, 5:, Glucose, 120:, BloodPressure, 70:, SkinThickness,30... Output: ✅ *⚠️ High risk of diabetes (87.00% probability)

📷 Streamlit App Preview

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📊 Dataset

Source: Dataset

Clinical Features: Pregnancies, Glucose, BloodPressure, SkinThickness, etc.

🤝 Contributing

Contributions are welcome! Feel free to: Open an issue Submit a pull request

📄 License

This project is licensed under the MIT License.

📌 Notes

• Handle the inbalance in target class distribution to ensure model robustness
• Ensure that the heart_disease_model.pkl and scaler.pkl are in the same directory as the app.

Author: Samsudeen Bankole

Built with Streamlit and Scikit-learn.