🧠 Breast Cancer Diagnosis (Logistic Regression)

Machine Learning project to diagnose breast cancer (malignant/benign) using logistic regression, trained on the Breast Cancer Wisconsin dataset.

Features | Tech Stack | Installation | Project Structure | Contributing | Author

🌟 Features

• Logistic Regression classifier for tumor diagnosis
• Exploratory data analysis (EDA) and preprocessing
• Confusion matrix, ROC curve, and model evaluation metrics
• Modular codebase (data, preprocessing, model, utils)
• Easy reproducibility and extendability for other ML models

🛠️ Tech Stack

• Language: Python 3.8+
• ML Library: scikit-learn
• Data Handling: pandas, numpy
• Visualization: matplotlib, seaborn
• Notebook Support: Jupyter

🚀 Installation

Prerequisites

• Python 3.8+
• pip package manager
• Recommended: virtual environment (venv or conda)

Steps

# Clone the repository
git clone https://github.com/TechFreak2003/breast-cancer-logreg.git
cd breast-cancer-logreg

# Install dependencies
pip install -r requirements.txt

# Run the main script
python main.py

📁 Project Structure

breast-cancer-diagnosis/
├── data/
│   └── breast_cancer_data.csv     # Dataset (Kaggle)
│
├── notebooks/
│   └── exploratory_analysis.ipynb # EDA and initial experiments
│
├── src/
│   ├── data_loader.py             # Load and read CSV
│   ├── preprocess.py              # Data cleaning & encoding
│   ├── model.py                   # Train & evaluate logistic regression
│   └── utils.py                   # Visualization & helpers
├── .gitignore
├── README.md
├── requirements.txt
└── main.py                        # Orchestrates the full pipeline

📊 Dataset

• Source: Kaggle - Breast Cancer Wisconsin Dataset
• Target Column: diagnosis (M = malignant, B = benign)
• Features: 30 numerical features (radius, texture, area, etc.)

📈 Results

Example outputs include:

• Accuracy: 95%+
• Confusion Matrix: True Positives / False Negatives breakdown
• ROC-AUC: Area under the curve visualized

📌 Overview of the ML pipeline including logistic regression and other models

📌 Include your actual metrics or screenshots after model evaluation

👥 Contributing

Contributions are welcome! Please check the issues and submit PRs to improve the code, add enhancements, or fix bugs.

To contribute:

1. Fork the repo
2. Create a new branch (git checkout -b feature-name)
3. Commit changes (git commit -am 'Add new feature')
4. Push and open a Pull Request

👨‍💻 Contributors

Avatar	Name	GitHub	Role	Contributions
	Suvrodeep Das	TechFreak2003	Creator	Full implementation, docs

📄 License

This project is licensed under the MIT License.

🙋‍♂️ Author

Created with ❤️ by Suvrodeep Das