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:

1. Clone the repository:

git clone https://github.com/hitthecodelabs/PetalAnalyticsStreamlit.git

2. Navigate to the project directory:

cd PetalAnalyticsStreamlit

3. Install the dependencies:

pip install -r requirements.txt

To start the application, run:

streamlit run app_new.py

Navigate 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.