🧪 Breast Cancer Classification using Support Vector Machine (SVM)

This project uses a Support Vector Machine (SVM) with an RBF kernel to classify tumors as malignant or benign using the Breast Cancer Wisconsin Dataset. It includes scaling, cross-validation, and hyperparameter tuning with different regularization strengths (C values).

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🔍 What's Included

• Loads the breast_cancer dataset from sklearn.datasets
• Scales features using StandardScaler
• Trains an SVM classifier with RBF kernel
• Performs 5-fold cross-validation
• Evaluates accuracy across different C values
• Visualizes performance trends to choose the best C

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📈 Visualization

The notebook generates a plot showing:

• Mean cross-validation accuracy
• Standard deviation of performance
• Behavior across different regularization strengths (log scale)

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🛠️ How to Run

1. Clone the repository:

git clone https://https://github.com/NoorNick/Breast-Cancer-Detection.git
cd Breast-Cancer-Detection

2. Install dependencies:

pip install -r requirements.txt

3. Launch Jupyter Notebook:

jupyter notebook

Open breast_cancer_svm.ipynb and run all cells.

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🧪 Example Output

Cross-validation scores: [0.9561 0.9649 0.9386 0.9649 0.9737]
Mean CV score: 0.960 (+/- 0.025)

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Support early detection. Classify responsibly. 🎗️