🏥 Breast Cancer Prediction Web Application

A modern Flask web application that uses machine learning to predict breast cancer diagnosis based on cell nuclei characteristics. Built with Python, Flask, and scikit-learn.

🌟 Features

• AI-Powered Predictions: Uses Extra Trees Classifier with 100% F1 score on validation data
• Modern UI: Responsive design with Bootstrap 5, loading animations, and intuitive interface
• Real-time Analysis: Instant predictions with confidence scores and risk assessment

🚀 Live Demo

https://breast-cancer-flask-app.onrender.com/

📊 Model Performance

• Algorithm: Extra Trees Classifier (automatically selected based on F1 score)
• Validation F1 Score: 1.0000 (100%)
• Test Accuracy: 100%
• Data Preprocessing: Z-score outlier removal, StandardScaler normalization
• Features: 30 cell nuclei measurements (mean, standard error, and worst values)

🛠️ Technology Stack

• Backend: Python Flask
• Machine Learning: Scikit-learn (Extra Trees, Random Forest, SVM, etc.)
• Frontend: HTML5, CSS3, Bootstrap 5, JavaScript
• Data Processing: NumPy, Pandas
• Model Persistence: Joblib

📋 Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

🔧 Installation & Setup

1. Clone the repository

git clone https://github.com/yourusername/breast-cancer-prediction-web.git
cd breast-cancer-prediction-web

2. Create a virtual environment (recommended)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

4. Run the application

python app.py

5. Open your browser

Navigate to http://localhost:5000

🔄 How It Works

1. Data Loading: Uses the Wisconsin Breast Cancer dataset from scikit-learn
2. Data Cleaning: Removes outliers using Z-score method (threshold: 3)
3. Model Training: Trains 7 different ML algorithms and selects the best based on F1 score
4. Model Selection: Extra Trees Classifier achieves perfect F1 score and is automatically selected
5. Prediction: User inputs cell measurements → Model predicts Benign/Malignant with confidence

📁 Project Structure

breast-cancer-prediction-web/
│
├── app.py                 # Main Flask application
├── requirements.txt       # Python dependencies
├── Procfile              # For Heroku deployment
├── runtime.txt           # Python version for deployment
├── README.md             # Project documentation
├── .gitignore           # Git ignore file
│
├── models/              # ML models (auto-generated)
│   ├── breast_cancer_model.pkl
│   └── scaler.pkl
│
├── templates/           # HTML templates
│   ├── index.html       # Main prediction page
│   └── about.html       # About page
│
└── static/             # Static files
    ├── css/
    │   └── style.css    # Custom styles
    └── js/
        └── script.js    # Frontend JavaScript

🔌 API Endpoints

Main Routes

• GET / - Main prediction interface
• POST /predict - Make prediction (JSON response)
• GET /about - About page
• GET /api/model-info - Model information and metrics

API Usage Example

curl -X POST http://localhost:5000/predict \
  -d "mean radius=14.6" \
  -d "mean texture=22.7" \
  [... other 28 features]

Response:

{
  "prediction": "Benign",
  "confidence": {
    "benign": "71.0%",
    "malignant": "29.0%"
  },
  "risk_level": "Low Risk",
  "confidence_score": 0.71
}

🚀 Deployment Options

Option 1: Heroku (Free Tier)

# Install Heroku CLI, then:
heroku create your-app-name
git push heroku main

Option 2: Render

1. Connect your GitHub repo to Render
2. Select "Web Service"
3. Build command: pip install -r requirements.txt
4. Start command: python app.py

Option 3: Railway

1. Connect GitHub repo to Railway
2. Deploy automatically with zero configuration

Option 4: Local Network

python app.py
# Access via http://your-ip:5000

⚠️ Medical Disclaimer

IMPORTANT: This application is for educational and research purposes only. It should NOT be used for actual medical diagnosis or treatment decisions. Always consult with qualified healthcare professionals for medical advice.

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Wisconsin Breast Cancer Dataset from UCI Machine Learning Repository
• Scikit-learn community for excellent ML tools
• Bootstrap team for the responsive UI framework
• Flask community for the lightweight web framework

⭐ Star this repository if you found it helpful!