Iris FlowerSpecs is an innovative web application that utilizes machine learning algorithms to predict the variety or species of a flower based on its key characteristics. Whether you're a botanist, a gardener, or simply a flower enthusiast, FlowerSpecs is your go-to tool for quickly and reliably identifying different flower varieties.
- Accurately predict flower variety based on sepal length, sepal width, petal length, and petal width
- User-friendly interface with a seamless and intuitive user experience
- Fast and efficient predictions powered by advanced machine learning techniques
- Explore a wide range of flower varieties with detailed information
- Modern and visually appealing design
- Clone the repository:
git clone https://github.com/nv21053/Iris-cnn_model - Navigate to the project directory:
cd Iris-cnn_model - Install the necessary dependencies:
npm install
- Launch the application:
npm start - Open your preferred web browser and go to:
http://localhost:3000 - Enter the measurements of the flower's sepal length, sepal width, petal length, and petal width in the provided fields.
- Click the "Predict" button to get the predicted flower variety.
- Python
- TensorFlow.js
- HTML
- CSS
Contributions are welcome! If you have any ideas, suggestions, or bug reports, please open an issue or submit a pull request. Together, let's enhance FlowerSpecs and make it even more powerful!
This project is licensed under the [NVTC].
- Special thanks to the creators and contributors of the Iris dataset
- Inspired by the beauty of flowers and the potential of machine learning
For any inquiries or questions, please reach out to us at ali.albalushi557@gmail.com.