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🩺 Diabetes Prediction App using Machine Learning

A simple and powerful Machine Learning web application built with Streamlit that predicts whether a person is diabetic or not based on medical input features.

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🚀 Project Overview

This project uses a Neural Network (Deep Learning Model) trained on the Pima Indians Diabetes Dataset to predict diabetes.

Users can input health-related parameters through a clean web interface and instantly get predictions.

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🎯 Features

• 🧠 Deep Learning Model (Keras / TensorFlow)
• 🌐 Interactive Web App using Streamlit
• ⚡ Real-time Prediction
• 📊 User-friendly input fields
• ✅ Instant result display (Diabetic / Not Diabetic)

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📂 Project Structure

DIABETES_PREDICTION/
│
├── app.py                     # Streamlit App
├── train.py                  # Model Training Script
├── test.py                   # Model Testing Script
├── diabetes_model.h5         # Trained Model
├── pima-indians-diabetes.csv # Dataset
├── requirements.txt          # Dependencies
└── README.md                 # Project Documentation

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🧠 Machine Learning Model

• Algorithm: Artificial Neural Network (ANN)
• Framework: Keras + TensorFlow
• Activation Functions: ReLU, Sigmoid
• Loss Function: Binary Crossentropy
• Optimizer: Adam

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📊 Input Features

1. Pregnancies
2. Glucose Level
3. Blood Pressure
4. Skin Thickness
5. Insulin
6. BMI
7. Diabetes Pedigree Function
8. Age

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▶️ How to Run the Project

1️⃣ Clone the Repository

git clone https://github.com/selvan-01/diabetes-prediction.git
cd diabetes-prediction

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2️⃣ Install Dependencies

pip install -r requirements.txt

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3️⃣ Run the App

streamlit run app.py

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4️⃣ Open in Browser

http://localhost:8501

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🧪 Sample Input

Pregnancies: 2
Glucose: 120
Blood Pressure: 70
Skin Thickness: 20
Insulin: 85
BMI: 28.5
Pedigree: 0.5
Age: 30

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🎯 Output

• ✅ Not Diabetic
• ⚠️ Diabetic

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📌 Future Improvements

• 🎨 Advanced UI/UX Design
• 📊 Show prediction probability
• ☁️ Deploy to cloud (Streamlit Cloud / Render)
• 📱 Mobile responsive UI

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🔗 Links

• 💼 LinkedIn
• 🌍 Portfolio
• 💻 GitHub

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⭐ Support

If you like this project, give it a ⭐ on GitHub!

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🏁 Conclusion

This project demonstrates how Machine Learning can be used in real-world healthcare applications to assist in early diagnosis and decision-making.

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