Welcome to IrisWise – Welcome to the Iris Species Prediction app! 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.
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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.
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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...)
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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.
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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.
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Tooltips and Explanations: 💡 Hover over options and checkboxes to get detailed tooltips, making the app more informative and easier to use.
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Custom Animations: 🎨 Experience unique animations and transitions, including Lottie and JavaScript animations, that enhance the visual appeal of the app. (Working On It...)
Check out the live demo on
Get started with IrisWise by following these simple steps:
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Clone the repository to your local machine:
git clone https://github.com/Hunterdii/Iriswise.git
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Install the required dependencies:
pip install -r requirements.txt
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Run the Streamlit app:
streamlit run app.py
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Open your browser and go to
http://localhost:8501to start predicting Iris species.
You 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.