This project involves training a Support Vector Machine (SVM) classifier using the sklearn digits dataset to classify handwritten digits. The project includes measuring the accuracy of the model using different kernels (RBF and linear), tuning the model using regularization and gamma parameters to achieve the highest accuracy score, and using 80% of the samples as training data.
- Data loading and preprocessing
- SVM classifier implementation using different kernels (RBF and linear)
- Model accuracy evaluation with different kernels
- Hyperparameter tuning for regularization and gamma to optimize accuracy
- Detailed code comments and explanations
- Python 3.x
- Jupyter Notebook
- Libraries: numpy, pandas, scikit-learn