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This is a simple Streamlit app that demonstrates the use of different machine learning classifiers (KNN, SVM, and Random Forest) on various datasets, including Iris, Breast Cancer, and Wine datasets from sklearn. The app also includes a PCA (Principal Component Analysis) visualization of the dataset to show the reduction of dimensions for the chosen dataset.
To run this app, you'll need the following Python packages installed: streamlit, scikit-learn, numpy, and matplotlib. You can install these dependencies using pip:
pip install streamlit scikit-learn numpy matplotlibClone the repository to your local machine:
git clone https://github.com/AndrewLamCS/StreamlitDataApp.git
cd StreamlitDataApppython3 -m venv .venv
source .venv/bin/activate # On Windows, use .venv\Scripts\activate
pip install -r requirements.txt
streamlit run main.py
This will open the app in your default web browser. You can interact with the app by selecting different datasets, classifiers, and adjusting the classifier parameters through the sidebar.StreamlitDataApp/
│
├── main.py # The main Streamlit app file
├── requirements.txt # List of dependencies
├── README.md # Project documentation
└── .venv/ # Virtual environment (optional)
This project is open-source and available under the MIT License.