Breast Cancer Classification with SVM

A machine learning project that uses Support Vector Machines (SVM) to classify breast cancer tumors as malignant or benign, achieving 98.25% accuracy.

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Features

• Data loading and exploration
• Feature visualization using histograms
• Data preprocessing with StandardScaler
• SVM model implementation with RBF kernel
• Model evaluation using classification report and confusion matrix

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Dataset

The Wisconsin Breast Cancer Diagnostic Dataset from scikit-learn:

• 569 samples (357 benign, 212 malignant)
• 30 numeric features computed from digitized images
• Target classes: Malignant (0) and Benign (1)

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Code Structure

1. Data Loading and Preparation
2. Data Preprocessing (Train-Test Split, Scaling)
3. Feature Distribution Visualization
4. SVM Model Implementation
5. Model Evaluation

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Installation

1. Clone repository:

git clone https://github.com/YOUR-USERNAME/breast-cancer-classification.git
cd breast-cancer-classification

2. Install requirements:

pip install -r requirements.txt

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Usage

1. Start Jupyter Notebook:

jupyter notebook notebooks/Breast_Cancer_Classification.ipynb

2. Open Breast_Cancer_Classification.ipynb
3. Run cells sequentially to:
   • Load and preprocess data
   • Train SVM model
   • Evaluate performance
   • Visualize results

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Results

• Accuracy: 98.25%
• Precision (Malignant): 100%
• Recall (Benign): 100%
• Confusion Matrix:

	Predicted Malignant	Predicted Benign
Actual Malignant	41	2
Actual Benign	0	71

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Contributing

• Fork the repository
• Create feature branch
• Submit PR with detailed description

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Contact

For questions or feedback, contact: abideen5036@gmail.com

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