Breast-Cancer-Prediction

A web application for predicting breast cancer using machine learning.
Built with Flask, scikit-learn, and Bootstrap.

Features

• Predicts cancerous or non-cancerous based on user input features
• Clean, responsive UI with Bootstrap
• Displays result images for "Cancerous" and "Not Cancerous"
• Ready for deployment on Vercel

Project Structure

Breast-Cancer-Prediction/
│
├── backend/
│   └── app.py                # Flask application
├── model/
│   └── model.pkl             # Trained ML model (pickle file)
├── static/
│   ├── main.jpg              # Main header image
│   ├── img.jpg               # Cancerous result image
│   └── img1.jpg              # Not cancerous result image
├── templates/
│   └── index.html            # Main HTML template
├── requirements.txt          # Python dependencies
├── vercel.json               # Vercel deployment config
└── .gitignore                # Git ignore file

Setup

1. Clone the repository:

   git clone https://github.com/<your-username>/Breast-Cancer-Prediction.git
   cd Breast-Cancer-Prediction
   

2. Create and activate a virtual environment:

   python -m venv venv
   venv\Scripts\activate   # On Windows
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Run the Flask app:

   python backend/app.py
   

   Visit http://localhost:5000 in your browser.

Deployment (Vercel)

• The project includes a vercel.json for deployment.
• Push your code to GitHub and import the repo in Vercel.
• Vercel will automatically detect the Python serverless function and deploy.

Usage

• Enter medical features (comma separated) in the input box.
• Click Predict to see the result and corresponding image.

License

MIT

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Note:

• Make sure model/model.pkl exists and is a valid trained model.
• Place your result images in the static/ folder as described above.