🩺 Breast Cancer Diagnosis ML Web Application

📋 Project Overview

An end-to-end machine learning application for breast cancer diagnosis that predicts whether a breast mass is benign or malignant based on cytology lab measurements. The project includes both model training and an interactive web interface.

🚀 Features

1. Machine Learning Pipeline

• Data preprocessing and cleaning from the Wisconsin Breast Cancer Dataset
• Feature scaling using StandardScaler
• Logistic Regression classification model
• Model evaluation with accuracy metrics and classification reports
• Serialized model and scaler for production use

2. Interactive Web Application (Streamlit)

• Real-time interactive sliders for 30+ cell nuclei measurements
• Dynamic radar chart visualization comparing:
  • Mean values
  • Standard error values
  • Worst-case values
• Instant prediction results with probability scores
• Responsive two-column layout design

3. Key Functionalities

• Data Cleaning: Automatic handling of missing values and column mapping
• Feature Scaling: Min-max scaling for visualization and model input
• Model Prediction: Real-time inference with probability outputs
• Visual Analytics: Plotly-based radar charts for multi-dimensional data visualization
• User-Friendly Interface: Intuitive sidebar controls and clear result displays

📁 Project Structure

  ├── main.py # Streamlit web application
  ├── model_training.py # ML model training script
  ├── model.pkl # Trained logistic regression model
  ├── scaler.pkl # Fitted StandardScaler object
  ├── dataset/
  │ └── cdata.csv # Breast cancer dataset
  ├── requirements.txt # Python dependencies
  └── README.md # This file

🔧 Installation & Setup

Prerequisites

• Python 3.8+
• pip package manager

Installation Steps

1. Clone the repository:

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

2. Install dependencies:

   pip install -r requirements.txt

3. Run the web application:

   streamlit run main.py
   

Dependencies (requirements.txt)

streamlit==1.28.0
pandas==2.0.3
numpy==1.24.3
scikit-learn==1.3.0
plotly==5.17.0 

🧪 Model Training

To retrain the model:

 python model_training.py

This will:

1. Load and clean the dataset
2. Split data into training and testing sets
3. Train a logistic regression model
4. Evaluate model performance
5. Save the model and scaler as .pkl files

🎮 Using the Application

1. Adjust Measurements: Use the sidebar sliders to input cell nuclei measurements
2. View Visualization: Observe the radar chart showing three measurement categories
3. Get Predictions: See the prediction (Benign/Malignant) with probability scores
4. Medical Disclaimer: Always consult healthcare professionals for actual diagnoses

📊 Dataset Information

1. The application uses the Wisconsin Breast Cancer Dataset containing:
2. 569 instances with 30 features each
3. Features include mean, standard error, and worst values of:
4. Radius, Texture, Perimeter, Area
5. Smoothness, Compactness, Concavity
6. Concave Points, Symmetry, Fractal Dimension
7. Binary target variable: Malignant (M) or Benign (B)

🔍 Model Performance

1. The logistic regression model achieves:
2. High accuracy on test data
3. Detailed classification metrics
4. Probability outputs for confident decision-making

⚠️ Important Disclaimer

This application is designed to assist medical professionals and should NOT be used as a substitute for professional medical diagnosis, advice, or treatment. Always consult qualified healthcare providers for medical decisions.

🤝 Contributing

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

📄 License

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

🙏 Acknowledgments

• University of Wisconsin for the Breast Cancer Dataset
• Streamlit for the amazing web app framework
• Scikit-learn for machine learning tools
• Plotly for visualization capabilities

📞 Contact

For questions or feedback, please open an issue in the GitHub reposito

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Key Files to Upload to GitHub:

1. main.py - Streamlit web application
2. model_training.py - Model training script (from your second file)
3. model.pkl - Trained model
4. scaler.pkl - Scaler object
5. dataset/cdata.csv - Dataset file
6. requirements.txt - Dependencies
7. README.md - Documentation (created above)
8. .gitignore - To exclude unnecessary files

Quick Start Commands:

# Create requirements.txt
pip freeze > requirements.txt

# Initialize git repo
git init
git add .
git commit -m "Initial commit: Breast Cancer Diagnosis ML App"
git branch -M main
git remote add origin https://github.com/yourusername/repo-name.git
git push -u origin main