This project demonstrates hyperparameter tuning on a Support Vector Machine (SVM) model to classify breast cancer as malignant or benign. The Breast Cancer Wisconsin (Diagnostic) Dataset is used for this purpose.
The dataset contains features computed from breast cancer cell images, and the target variable indicates whether the cancer is malignant or benign.
- Load and Preprocess the Data: Loading the dataset, splitting into training and testing sets, and standardizing the features.
- Hyperparameter Tuning: Using grid search to find the optimal set of hyperparameters for the SVM model.
- Model Evaluation: Evaluating the best model using classification metrics.
The best parameters found through grid search improved the model's performance, achieving higher accuracy in classifying breast cancer.
Hyperparameter tuning is a vital step in optimizing machine learning models. This project highlights its importance and demonstrates the process using an SVM model for breast cancer classification.
This project is licensed under the MIT License.