{"id":21284250,"url":"https://github.com/hunterdii/iriswise","last_synced_at":"2026-02-21T23:05:40.436Z","repository":{"id":253132316,"uuid":"842535629","full_name":"Hunterdii/Iriswise","owner":"Hunterdii","description":"IrisWise is a machine learning application for predicting Iris flower species. 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Enter the details below to predict the species of an Iris flower based on its features. This application uses a K-Nearest Neighbors classification model to predict the species.\n\n## Features\n\n- **Interactive User Interface:** 🖥️ Enjoy a sleek, modern UI with custom styling, animations, and colorful themes, enhancing the overall user experience. The interface is designed to be intuitive and engaging.\n\n- **Multiple Machine Learning Models:** 📊 Choose from a variety of models, including K-Nearest Neighbors, Random Forest, SVM, and Logistic Regression, each evaluated for performance to give you the best possible prediction. **(Working On It...)**\n\n- **Real-time Predictions:** 🌸 Input the features of the Iris flower (sepal length, sepal width, petal length, petal width) and get an instant prediction of the species.\n\n- **Dynamic Visualizations:** 📈 Explore decision boundaries and model performance graphs that update dynamically as you interact with the app. The visualizations help in understanding the decision process of each model.\n\n- **Tooltips and Explanations:** 💡 Hover over options and checkboxes to get detailed tooltips, making the app more informative and easier to use.\n\n- **Custom Animations:** 🎨 Experience unique animations and transitions, including Lottie and JavaScript animations, that enhance the visual appeal of the app. **(Working On It...)**\n\n## Demo\n\nCheck out the live demo on [![Open in Streamlit](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://iriswise.streamlit.app/)\n\n## Preview\n\n![IrisWise Screenshot](https://github.com/user-attachments/assets/c6eb8853-cdbf-490d-808e-321f54aac302)\n\n\n## Usage\n\nGet started with IrisWise by following these simple steps:\n\n1. Clone the repository to your local machine:\n\n   ```bash\n   git clone https://github.com/Hunterdii/Iriswise.git\n   ```\n\n2. Install the required dependencies:\n\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n3. Run the Streamlit app:\n\n   ```bash\n   streamlit run app.py\n   ```\n\n4. Open your browser and go to `http://localhost:8501` to start predicting Iris species.\n\n## Customization\n\nYou can customize the app to your liking by modifying the CSS for styling, updating the machine learning models, or adding new features. The code is well-documented to help you navigate and tweak the application as needed.\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhunterdii%2Firiswise","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhunterdii%2Firiswise","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhunterdii%2Firiswise/lists"}