CancerPredict is a Breast Cancer prediction web application built with Python (Streamlit) that uses a trained machine learning model to predict breast cancer outcomes based on user inputs.
CancerPredict is a user-friendly tool to quickly assess breast cancer risk using clinical or imaging data. Built using Python and deployed via Streamlit, it's ideal for educational purposes or lightweight demo applications.
Predict breast cancer risk using a pre-trained ML model
Interactive web interface via Streamlit for easy user input
Clear visualization of results and prediction probability
Lightweight and perfect for exploration or prototyping
CancerPredict/
│
├── .idea/ # IDE-specific configs
├── DATA/ # Dataset files
├── MODEL/ # Saved model artifacts (e.g., .pkl)
├── app/ # Streamlit app code
├── assets/ # Static files (images, CSS, etc.)
└── requirements.txt # Python dependencies
Clone the repo
git clone https://github.com/Aayushmaan-24/CancerPredict.git
cd CancerPredict
Create and activate a virtual environment
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
Install dependencies
pip install -r requirements.txt
Running the App
Start the Streamlit app:
streamlit run app/app.py
Then open the provided localhost: URL (typically 8501) in your browser.
Enter patient data (e.g., features like age, tumor size, etc.).
Submit to see the predicted outcome and probability.
Use outputs for educational or evaluation purposes.
DATA/ contains the input data used to train the model.
MODEL/ houses the serialized machine learning model.
app/ contains code to load the model, accept user input, and display predictions via Streamlit.
assets/ includes UI enhancements like CSS styling and icons.
requirements.txt ensures reproducible dependency management.
MIT license