Diabetes Prediction App is a web application built with Flask that predicts the likelihood of diabetes based on user health input data. The ML model is trained using the popular Pima Indians Diabetes Dataset and deployed via a Flask server with a clean and responsive user interface.

⚠️ Note: This project is for educational purposes only. It does not replace professional medical advice or diagnosis.

• 🧠 Predict diabetes risk using ML
• 📋 User-friendly web form for data input
• ⚖️ Data pre-processing and scaling
• ⚡ Real-time predictions via trained model
• 🎨 Responsive frontend using HTML/CSS
• 📂 Easy-to-understand project structure

diabetes-prediction-app/
├── templates/
│   └── index.html          # Web UI
├── diabetes.csv            # Dataset
├── model.pkl               # Trained ML model
├── scaler.pkl              # Scaler object
├── train_model.py          # Model training script
├── app.py                  # Flask backend app
├── Screenshot.png          # Demo screenshot
└── README.md

1. Clone the Repository

   git clone https://github.com/GxAniket/diabetes-prediction-app.git
   cd diabetes-prediction-app

2. Install Dependencies

• pip install -r requirements.txt

3. Train the Model (Optional)

• python train_model.py

4. Run the App

• python app.py

5. Open in Browser

• http://127.0.0.1:5000

• This project helped me:

• Understand end-to-end ML model deployment

• Build Flask-based data apps

• Use scikit-learn for model training and scaling

• Design clean and responsive HTML interfaces

• Learn the structure of real-world Python apps

• 📊 Add result visualization (charts/graphs)
• 🧪 Improve model accuracy with better preprocessing
• ☁️ Deploy on Render, Railway, or Heroku
• 🛡️ Add input validation and error handling

This project is for learning purposes only. Feel free to use, modify, and share with proper credit. Not intended for medical or commercial use.

• Made with ❤️ by Aniket Sundriyal