🧠 Breast Cancer Detection using PyCaret

🎗️ This project uses PyCaret, a low-code machine learning library, to detect breast cancer based on tumor features from the Breast Cancer Wisconsin (Diagnostic) Dataset. The objective is to classify tumors as benign or malignant with high accuracy using an automated pipeline.

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📊 Dataset

• Source: Kaggle Repository
• Features: 30 numeric features computed from digitized images of breast masses.
• Target: diagnosis - M = Malignant, B = Benign

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⚙️ Tech Stack

• Python 3.x
• PyCaret (for ML automation)
• Pandas, NumPy, Seaborn, Matplotlib (for data handling and visualization)
• Jupyter Notebook (for development)

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📈 Model Performance

Metric	Score
Accuracy	✅ 96-98% (based on selected model)
Precision	High (less false positives)
Recall	High (saves lives 💯)
Confusion Matrix	✔️ Included in Notebook

⚡ Powered by PyCaret’s compare_models() and evaluate_model() functions.

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🧪 How to Run

1. Clone the repository:

git clone https://github.com/QuantumCoderrr/PyCaret-BCDetection.git
cd PyCaret-BCDetection

🪪 License

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

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✨ Acknowledgements

• Kaggle
• PyCaret Team
• Scikit-learn Community

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