This project is a web application developed with Streamlit that predicts the Iris flower type based on its physical features. It utilizes a Random Forest classification model trained on the well-known Iris dataset. The app allows users to adjust parameters of the Iris flower (sepal length, sepal width, petal length, petal width) and view the model's prediction.
- Interactive interface for inputting flower parameters.
- Prediction probability visualization using interactive Plotly bar charts.
- Custom styling with CSS for an enhanced visual experience.
To run this application, follow these steps:
- Clone the repository:
git clone https://github.com/hitthecodelabs/PetalAnalyticsStreamlit.git- Navigate to the project directory:
cd PetalAnalyticsStreamlit- Install the dependencies:
pip install -r requirements.txtTo start the application, run:
streamlit run app_new.pyNavigate to the URL provided by Streamlit in your browser to interact with the app.
Contributions to this project are welcome. Please fork the repository and submit a pull request with your proposed changes.
This project is open source and available under the MIT License.