Iris Flower Classifier

Visual Reference

Model Overview

The Iris Flower Classifier is a machine learning model that predicts the species of an iris flower based on its sepal and petal dimensions. The model is built using a Decision Tree Classifier trained on the well-known Iris dataset.

Model Details

Model Type: Decision Tree Classifier
Input Features:
- Sepal Length (cm)
- Sepal Width (cm)
- Petal Length (cm)
- Petal Width (cm)
Output: Species of the iris flower (Setosa, Versicolor, Virginica)

Training Data

Dataset: The model was trained on the Iris dataset, which contains 150 samples of iris flowers, each with four features and a corresponding species label.
Source: UCI Machine Learning Repository

Intended Use

This model is intended for educational purposes and can be used to:

Predict the species of an iris flower based on its measurements.
Serve as an example of using a Decision Tree Classifier in Python.

Limitations

The model may not perform well on unseen data that differs significantly from the training data.
It is specifically designed for classifying iris flowers and may not generalize to other types of flowers or datasets.

How to Use

You can use this model through a Gradio interface. Simply enter the measurements of the iris flower, and the model will predict the species.

How to open this model

By using this command
!git clone https://huggingface.co/shahad23/IrisFlowerModel
then copy the content of IrisModel.py then run it.

Example

To predict the species, input the following:

Sepal Length: 5.1
Sepal Width: 3.5
Petal Length: 1.4
Petal Width: 0.2

License

This model is licensed under the MIT License. You can use it freely, but attribution is appreciated.

Acknowledgments

Thanks to the contributors of the Iris dataset and the developers of the scikit-learn library for making this project possible.