Welcome to OptiFine, a web application that demonstrates the power of hyperparameter tuning using Scikit-learn's GridSearchCV. OptiFine allows you to optimize your machine learning models by fine-tuning their hyperparameters to achieve peak performance.
- Select from two datasets: Iris Plants and Wine Recognition.
- Choose from three classifier algorithms: Random Forest, SVM, and Logistic Regression.
- Customiz 6218 e the number of cross-validation folds.
- View dataset information, such as the number of samples, features, and classes.
- Perform hyperparameter tuning using GridSearchCV.
- Display the best parameters and evaluation metrics, including accuracy, precision, recall, and F1-score.
- Clone the repository:
git clone https://github.com/MahtabRanjbar/OptiFine-ml-app.git- Install the required dependencies:
pip install -r requirements.txt- Run the application:
streamlit run app.py- Access the application in your web browser at
http://localhost:8501.
This project is licensed under the MIT License.