Breast Cancer Prediction App

A Streamlit-based web application for predicting breast cancer diagnosis using machine learning. This application uses a Logistic Regression model trained on the Wisconsin Breast Cancer dataset to predict whether a tumor is malignant (M) or benign (B) based on various features.

Features

• Interactive web interface built with Streamlit
• Machine learning model for breast cancer prediction
• Data visualization capabilities
• User-friendly input form for prediction
• Model performance metrics and insights

Prerequisites

• Python 3.7+
• pip (Python package installer)

Installation

1. Clone the repository:

git clone [your-repository-url]
cd streamlit-cancer-predict

2. Create a virtual environment (recommended):

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

3. Install the required packages:

pip install -r requirements.txt

Project Structure

streamlit-cancer-predict/
├── app/
│   └── main.py          # Streamlit application
├── model/
│   └── main.py          # Model training and preprocessing
├── data/
│   └── data.csv         # Dataset
├── assets/              # Static assets
└── requirements.txt     # Project dependencies

Usage

1. Train the model:

cd model
python main.py

2. Run the Streamlit app:

cd app
streamlit run main.py

3. Open your web browser and navigate to the URL shown in the terminal (typically http://localhost:8501)

Model Details

The application uses a Logistic Regression model with the following features:

• Standardized input features
• 80-20 train-test split
• StandardScaler for feature normalization

Dependencies

• numpy==1.23.4
• pandas==1.5.1
• plotly==5.11.0
• scikit_learn==1.2.2
• streamlit
• altair<5.0.0

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgments

• Wisconsin Breast Cancer Dataset
• Streamlit for the web framework
• Scikit-learn for machine learning capabilities