This project aims to explore and analyze breast cancer data using Support Vector Machines (SVMs) for classification. The dataset used is the Breast Cancer Wisconsin (Diagnostic) Dataset, available in the scikit-learn library.
- Load and explore the breast cancer dataset.
- Split the data into training and testing sets.
- Train SVM models with different kernels (linear, RBF, and sigmoid).
- Evaluate the accuracy of the models on the training and testing sets.
- Compare the performance of the models using graphs.
- scikit-learn: to load the dataset, train the SVM models, and evaluate accuracy.
- matplotlib: to plot graphs showing the models' accuracy.
This project is expected to provide a practical understanding of how SVMs can be applied for breast cancer data classification and demonstrate how different kernels affect model performance.