https://github.com/hitthecodelabs/petalanalyticsstreamlit
Web application developed with Streamlit that predicts the Iris flower type based on its physical features
https://github.com/hitthecodelabs/petalanalyticsstreamlit
matplotlib model numpy pickle python scikit-learn sklearn streamlit
Last synced: 8 months ago
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Web application developed with Streamlit that predicts the Iris flower type based on its physical features
- Host: GitHub
- URL: https://github.com/hitthecodelabs/petalanalyticsstreamlit
- Owner: hitthecodelabs
- License: mit
- Created: 2023-11-11T06:02:27.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2023-11-11T06:21:41.000Z (about 2 years ago)
- Last Synced: 2025-03-29T10:47:29.677Z (10 months ago)
- Topics: matplotlib, model, numpy, pickle, python, scikit-learn, sklearn, streamlit
- Language: Python
- Homepage:
- Size: 4.33 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PetalAnalyticsStreamlit
## Description
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.
## Features
- Interactive interface for inputting flower parameters.
- Prediction probability visualization using interactive Plotly bar charts.
- Custom styling with CSS for an enhanced visual experience.
## Installation
To run this application, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/hitthecodelabs/PetalAnalyticsStreamlit.git
```
2. Navigate to the project directory:
```bash
cd PetalAnalyticsStreamlit
```
3. Install the dependencies:
```bash
pip install -r requirements.txt
```
## Usage
To start the application, run:
```bash
streamlit run app_new.py
```
Navigate to the URL provided by Streamlit in your browser to interact with the app.
## Contributing
Contributions to this project are welcome. Please fork the repository and submit a pull request with your proposed changes.
## License
This project is open source and available under the [MIT License](LICENSE).